chore(deps): Bump minimatch from 10.0.3 to 10.2.3 in /packages/altimate-code#1
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Bumps [minimatch](https://github.com/isaacs/minimatch) from 10.0.3 to 10.2.3. - [Changelog](https://github.com/isaacs/minimatch/blob/main/changelog.md) - [Commits](isaacs/minimatch@v10.0.3...v10.2.3) --- updated-dependencies: - dependency-name: minimatch dependency-version: 10.2.3 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com>
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OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting If you change your mind, just re-open this PR and I'll resolve any conflicts on it. |
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…s customization guide Training is not a CLAUDE.md replacement — it's the mechanism by which users customize the data engineering harness for their specific project. The agent works WITH the user to discover what it needs to know, rather than requiring users to write perfect static instructions. Changes: - Increase TRAINING_BUDGET from 6000 to 16000 chars (removes the #1 criticism from user simulations — budget was worse than unlimited CLAUDE.md) - Complete docs rewrite with correct positioning: - "Customizing Your AI Teammate" framing (not "Training Your AI Teammate") - Research-backed "why" section (40-70% knowledge omission, guided discovery) - Clear comparison table: training vs CLAUDE.md (complementary, not competing) - 6 real-world scenarios including Databricks, Salesforce quirks, cost spikes - Honest limitations section (not a linter, not an audit trail, not automatic) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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…s customization guide Training is not a CLAUDE.md replacement — it's the mechanism by which users customize the data engineering harness for their specific project. The agent works WITH the user to discover what it needs to know, rather than requiring users to write perfect static instructions. Changes: - Increase TRAINING_BUDGET from 6000 to 16000 chars (removes the #1 criticism from user simulations — budget was worse than unlimited CLAUDE.md) - Complete docs rewrite with correct positioning: - "Customizing Your AI Teammate" framing (not "Training Your AI Teammate") - Research-backed "why" section (40-70% knowledge omission, guided discovery) - Clear comparison table: training vs CLAUDE.md (complementary, not competing) - 6 real-world scenarios including Databricks, Salesforce quirks, cost spikes - Honest limitations section (not a linter, not an audit trail, not automatic) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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…s customization guide Training is not a CLAUDE.md replacement — it's the mechanism by which users customize the data engineering harness for their specific project. The agent works WITH the user to discover what it needs to know, rather than requiring users to write perfect static instructions. Changes: - Increase TRAINING_BUDGET from 6000 to 16000 chars (removes the #1 criticism from user simulations — budget was worse than unlimited CLAUDE.md) - Complete docs rewrite with correct positioning: - "Customizing Your AI Teammate" framing (not "Training Your AI Teammate") - Research-backed "why" section (40-70% knowledge omission, guided discovery) - Clear comparison table: training vs CLAUDE.md (complementary, not competing) - 6 real-world scenarios including Databricks, Salesforce quirks, cost spikes - Honest limitations section (not a linter, not an audit trail, not automatic) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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…148) * Add AI Teammate repositioning design document Comprehensive design for repositioning altimate from "AI tool" to "AI teammate" — including trainable knowledge system (/teach, /train, /feedback), Deep Research mode for multi-step investigations, team memory that persists via git, and UX reframing from "agent modes" to "teammate roles." https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Enrich design doc with OpenClaw research and proactive behaviors Add detailed competitive analysis from OpenClaw (self-improving memory, heartbeat scheduler, meet-users-where-they-are), Devin ($10.2B valuation, "junior partner" framing), and Factory AI (workflow embedding). Add proactive behaviors section with background monitors (cost alerts, freshness checks, schema drift, PII scanning) and auto-promotion of learned corrections. https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Implement AI Teammate training system and Deep Research mode Core training infrastructure built on top of existing memory system: Training Store & Types: - TrainingStore wraps MemoryStore with training-specific conventions - Four knowledge kinds: pattern, rule, glossary, standard - Structured metadata (applied count, source, acceptance tracking) - Training blocks stored in .opencode/memory/training/ (git-committable) - One person teaches, whole team benefits via git Training Tools: - training_save: Save learned patterns, rules, glossary, standards - training_list: List all learned knowledge with applied counts - training_remove: Remove outdated training entries Training Skills: - /teach: Learn patterns from example files in the codebase - /train: Learn standards from documents or style guides - /training-status: Dashboard of all learned knowledge System Prompt Injection: - Training knowledge injected alongside memory at session start - Structured by kind: rules first, then patterns, standards, glossary - Budget-limited to 6000 chars to control prompt size - Zero LLM calls on startup — just reads files from disk Deep Research Agent Mode: - New "researcher" agent for multi-step investigations - 4-phase protocol: Plan → Gather → Analyze → Report - Read-only access to all warehouse, schema, FinOps tools - Structured reports with evidence, root causes, action items Agent Awareness: - All agent prompts updated with training awareness section - Agents offer to save corrections as rules when users correct behavior - Training tools permitted in all agent modes Tests: - 88 new tests across 5 test files (types, store, prompt, tools, integration) - All tests standalone (no Instance dependency) - Full lifecycle tests: save → list → format → inject → remove - Edge cases: budget limits, meta roundtrips, coexistence with memory https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Polish AI Teammate training UX: auto-lowercase names, update detection, budget visibility - Fix researcher agent permissions: add training_save/remove (was read-only) - Auto-lowercase + space-to-hyphen name transform in training_save (ARR → arr) - Detect update vs new save, show "Updated" with preserved applied count - Show training budget usage (chars/percent) on save, list, and remove - Improve training_list: group by kind, show most-applied entries, budget % - Improve training_remove: show available entries on not-found, applied count - Show similar entry names in duplicate warnings (not just count) - Raise content limit from 1800 to 2500 chars - Export TRAINING_BUDGET constant, add budgetUsage() to TrainingPrompt - Add 30 new tests: auto-lowercase, update detection, budget overflow, name collision, scale (80 entries), improved messaging - All 118 training tests + 305 memory tests pass https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Enhance training UX: attribution, correction detection, priority sorting - Builder prompt: add attribution instructions (cite training entries that influenced output), correction detection (explicit + implicit patterns), conflict flagging between contradictory training entries - Add /teach, /train, /training-status to Available Skills list in builder prompt - Sort training entries by applied count (descending) in prompt injection so most-used entries get priority within the 6000-char budget - Restructure Teammate Training section with clear subsections https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Fix experience gaps from user journey simulations Simulation findings and fixes: 1. training_save now echoes back saved content so user can verify what was captured (new saves show content preview, updates show old vs new diff) 2. When training limit is reached, error now lists existing entries sorted by applied count and suggests the least-applied entry for removal 3. Researcher prompt now documents training_save/remove permissions (was contradicting its own permissions by saying "read-only" while having write access to training) 4. Added 10 new tests: content echo, update diff, limit suggestion, special character preservation (SQL -->, Jinja, HTML comments, code blocks), priority sorting verification Verified: --> in content does NOT corrupt meta block (false positive). The non-greedy regex terminates at the meta block's own --> correctly. 128 training tests + 305 memory tests all pass. https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Add self-improvement loop: applied tracking, insights, staleness detection OpenClaw-inspired self-improvement mechanisms: 1. Wire up incrementApplied at injection time — counters now actually increment once per session per entry (deduped via session-scoped set), making "Most Applied" dashboard and priority sorting meaningful 2. TrainingInsights module analyzes training metadata and surfaces: - Stale entries (7+ days old, never applied) — suggests cleanup - High-value entries (5+ applications) — highlights most impactful - Near-limit warnings (18-19 of 20 entries per kind) - Consolidation opportunities (3+ entries with shared name prefix) 3. Insights automatically shown in training_list output 4. 24 new tests covering all insight types, boundary conditions, session tracking dedup, and format output 152 training tests + 305 memory tests all pass. https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * fix: add dedicated training feature flag and remove unused insight type - Add `ALTIMATE_DISABLE_TRAINING` flag independent of memory's disable flag - Use new flag in session prompt injection and tool registry - Remove unused `budget-warning` insight type from `TrainingInsight` Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: reset training session tracking, add error logging, fix list truncation - Call `TrainingPrompt.resetSession()` at session start (step === 1) to prevent applied counters from growing unbounded across sessions - Add structured error logging to all three training tools - Add truncation indicator (`...`) when training list preview is cut off Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: use `.altimate-code/memory` as primary storage path with `.opencode` fallback Memory store was hardcoded to `.opencode/memory/` but the config system already uses `.altimate-code` as primary with `.opencode` as fallback. Now checks for `.altimate-code/` directory first, falls back to `.opencode/`, and defaults to `.altimate-code/` for new projects. Result is cached per process to avoid repeated filesystem checks. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add Trainer agent mode with pattern discovery and training validation Add dedicated trainer mode — the 8th primary agent — for systematically building the AI teammate's knowledge base. Unlike inline corrections in other modes, trainer mode actively scans codebases, validates training against reality, and guides knowledge curation. Changes: - New `trainer` agent mode with read-only permissions (no write/edit/sql_execute) - New `training_scan` tool: auto-discover patterns in models, SQL, config, tests, docs - New `training_validate` tool: check training compliance against actual codebase - Expand `TrainingKind` to 6 types: add `context` (background "why" knowledge) and `playbook` (multi-step procedures) - Update `count()` to derive from enum (prevents drift when kinds change) - Add KIND_HEADERS for context and playbook in prompt injection - Update injection order: rules first, playbooks last (budget priority) - Update training-save and training-list descriptions for new kinds Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add comprehensive training guide with scenarios and limitations - New `data-engineering/training/index.md` (350+ lines): - Quick start with 3 entry points (trainer mode, inline corrections, /train skill) - Deep dive into all 4 trainer workflows (scan, validate, teach, gap analysis) - 5 comprehensive scenarios: new project onboarding, post-incident learning, quarterly review, business domain teaching, pre-migration documentation - Explicit limitations section (not a hard gate, budget limits, no auto-learning, heuristic validation, no conflict resolution, no version history) - Full reference tables for tools, skills, limits, and feature flag - Updated `agent-modes.md`: add Researcher and Trainer mode sections with examples, capabilities, and "when to use" guidance - Updated `getting-started.md`: add training link to "Next steps" - Updated `mkdocs.yml`: add Training nav section under Data Engineering Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: increase training budget to 16K chars and rewrite docs as harness customization guide Training is not a CLAUDE.md replacement — it's the mechanism by which users customize the data engineering harness for their specific project. The agent works WITH the user to discover what it needs to know, rather than requiring users to write perfect static instructions. Changes: - Increase TRAINING_BUDGET from 6000 to 16000 chars (removes the #1 criticism from user simulations — budget was worse than unlimited CLAUDE.md) - Complete docs rewrite with correct positioning: - "Customizing Your AI Teammate" framing (not "Training Your AI Teammate") - Research-backed "why" section (40-70% knowledge omission, guided discovery) - Clear comparison table: training vs CLAUDE.md (complementary, not competing) - 6 real-world scenarios including Databricks, Salesforce quirks, cost spikes - Honest limitations section (not a linter, not an audit trail, not automatic) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: merge training into memory with context-aware relevance scoring Replace two parallel injection systems (memory 8KB + training 16KB) with a single unified injection that scores blocks by relevance to the current agent. How it works: - All blocks (memory + training) loaded in one pass - Each block scored: agent tag match (+10), training kind relevance per agent (+1-5), applied count bonus (+0-3), recency (+0-2), non-training base (+5) - Builder sees rules/patterns first; analyst sees glossary/context first - Budget is 20KB unified, filled greedily by score - Training blocks still tracked with applied counts (fire-and-forget) Architecture: - memory/prompt.ts: new scoreBlock(), unified inject() with InjectionContext - memory/types.ts: UNIFIED_INJECTION_BUDGET, AGENT_TRAINING_RELEVANCE weights - session/prompt.ts: single inject call with agent context (was 2 separate) - training/prompt.ts: deprecated, delegates to MemoryPrompt (backward compat) No changes to: MemoryStore, TrainingStore, training tools, memory tools. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor: cut training_scan and training_validate, simplify docs Research from 8 independent evaluations + SkillsBench (7,308 test runs) found that compact focused context beats comprehensive docs by 20pp. The training system's value is in correction capture (2-sec saves) and team propagation (git sync) — not in regex scanning or keyword grep. Removed: - training_scan (255 lines) — regex pattern counting, not discovery - training_validate (315 lines) — keyword grep, not validation Simplified: - trainer.txt: removed scan/validate workflows, focused on guided teaching and curation - agent-modes.md: updated trainer section with correction-focused example - training docs: complete rewrite with new pitch: "Correct the agent once. It remembers forever. Your team inherits it." Backed by SkillsBench research showing compact > comprehensive. Net: -753 lines. 152 tests pass. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove dead accepted/rejected fields, add training tips, expand limitations Gaps found by simulation team: 1. Remove `accepted`/`rejected` counters from TrainingBlockMeta — they were never incremented anywhere in the codebase (dead code since inception) 2. Add 5 training discoverability tips to TUI tips (was 0 mentions in 152 tips) 3. Expand limitations section in docs with honest, complete list: context budget, 20/kind limit, no approval workflow, SQL-focused, git discipline required Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: update site-wide docs for training and new agent modes - Homepage: update from "Four agents" to "Seven agents" — add Researcher, Trainer, Executive cards with descriptions - Getting Started: update training link to match new pitch "Corrections That Stick" - Tools index: add Training row (3 tools + 3 skills) with link - All references now consistent with simplified training system Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address Sentry review findings — 7 bugs fixed 1. stripTrainingMeta/parseTrainingMeta regex: remove multiline `m` flag that could match user content starting with `<!-- training` mid-string (types.ts, store.ts) 2. training_save content limit: reduce from 2500 to 1800 chars to account for ~200 char metadata overhead against MemoryStore's 2048 char limit (training-save.ts) 3. injectTrainingOnly: change `break` to `continue` so budget-exceeding section headers skip to next kind instead of stopping all injection (memory/prompt.ts) 4. injectTrainingOnly: track itemCount and return empty string when no items injected (was returning header-only string, inflating budget reports) (memory/prompt.ts) 5. projectDir cache: replace module-level singleton with Map keyed by Instance.directory to prevent stale paths when AsyncLocalStorage context changes across concurrent requests (memory/store.ts) 6. budgetUsage side effect: already fixed — delegates to injectTrainingOnly which is read-only (no applied count increment). Sentry comments were against pre-refactor code. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: CI failure + new Sentry finding — orphaned headers and agent test 1. Agent test: add researcher + trainer to "all disabled" test so it correctly expects "no primary visible agent" when ALL agents are off 2. Orphaned section headers: add pre-check that at least one entry fits before adding section header in both injectTrainingOnly and inject memory section (prevents header-only output inflating budget reports) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address multi-model code review findings Fixes from 6-model consensus review (Claude + GPT + Gemini + Kimi + MiniMax + GLM-5): 1. training_remove: add name validation regex matching training_save (Gemini finding — prevents path traversal via malformed names) 2. training_save: improve name transform to strip ALL non-alphanumeric chars, not just whitespace (Gemini finding — "don't-use-float!" now becomes "don-t-use-float" instead of failing regex) 3. incrementApplied: replace silent `.catch(() => {})` with warning log (Kimi + GLM-5 consensus — fire-and-forget is by design but failures should be visible in logs for debugging) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address new Sentry findings — regex m flag and off-by-one budget check 1. formatTrainingEntry regex: remove multiline `m` flag that could match user content mid-string (memory/prompt.ts:82) 2. Memory block budget check: change `<` to `<=` so blocks that fit exactly into remaining budget are included (memory/prompt.ts:204) 3 prior Sentry findings already fixed in earlier commits: - projectDir cache (Map keyed by Instance.directory) - injectTrainingOnly header-only return (itemCount guard) - orphaned section headers (first-entry pre-check) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address 6-model consensus review — 4 remaining bugs Fixes from consensus across Claude, GPT 5.2, Gemini 3.1, Kimi K2.5, MiniMax M2.5, and GLM-5: 1. parseTrainingMeta: check safeParse().success before accessing .data (GLM-5 + MiniMax consensus — accessing .data on failed parse returns undefined, could cause downstream errors) 2. Stale detection: use `e.updated` not `e.created` so entries updated recently aren't incorrectly flagged as stale (MiniMax finding) 3. training_list: pass scope/kind filter to count() so summary table matches the filtered entries list (GPT finding) 4. training_remove: show hint entries from same scope only, not all scopes (GPT + MiniMax finding) Prior fixes already addressed: name validation on remove (Gemini), name transform punctuation (Gemini), silent incrementApplied catch (Kimi + GLM-5), regex m flag (MiniMax + Sentry). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
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…148) * Add AI Teammate repositioning design document Comprehensive design for repositioning altimate from "AI tool" to "AI teammate" — including trainable knowledge system (/teach, /train, /feedback), Deep Research mode for multi-step investigations, team memory that persists via git, and UX reframing from "agent modes" to "teammate roles." https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Enrich design doc with OpenClaw research and proactive behaviors Add detailed competitive analysis from OpenClaw (self-improving memory, heartbeat scheduler, meet-users-where-they-are), Devin ($10.2B valuation, "junior partner" framing), and Factory AI (workflow embedding). Add proactive behaviors section with background monitors (cost alerts, freshness checks, schema drift, PII scanning) and auto-promotion of learned corrections. https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Implement AI Teammate training system and Deep Research mode Core training infrastructure built on top of existing memory system: Training Store & Types: - TrainingStore wraps MemoryStore with training-specific conventions - Four knowledge kinds: pattern, rule, glossary, standard - Structured metadata (applied count, source, acceptance tracking) - Training blocks stored in .opencode/memory/training/ (git-committable) - One person teaches, whole team benefits via git Training Tools: - training_save: Save learned patterns, rules, glossary, standards - training_list: List all learned knowledge with applied counts - training_remove: Remove outdated training entries Training Skills: - /teach: Learn patterns from example files in the codebase - /train: Learn standards from documents or style guides - /training-status: Dashboard of all learned knowledge System Prompt Injection: - Training knowledge injected alongside memory at session start - Structured by kind: rules first, then patterns, standards, glossary - Budget-limited to 6000 chars to control prompt size - Zero LLM calls on startup — just reads files from disk Deep Research Agent Mode: - New "researcher" agent for multi-step investigations - 4-phase protocol: Plan → Gather → Analyze → Report - Read-only access to all warehouse, schema, FinOps tools - Structured reports with evidence, root causes, action items Agent Awareness: - All agent prompts updated with training awareness section - Agents offer to save corrections as rules when users correct behavior - Training tools permitted in all agent modes Tests: - 88 new tests across 5 test files (types, store, prompt, tools, integration) - All tests standalone (no Instance dependency) - Full lifecycle tests: save → list → format → inject → remove - Edge cases: budget limits, meta roundtrips, coexistence with memory https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Polish AI Teammate training UX: auto-lowercase names, update detection, budget visibility - Fix researcher agent permissions: add training_save/remove (was read-only) - Auto-lowercase + space-to-hyphen name transform in training_save (ARR → arr) - Detect update vs new save, show "Updated" with preserved applied count - Show training budget usage (chars/percent) on save, list, and remove - Improve training_list: group by kind, show most-applied entries, budget % - Improve training_remove: show available entries on not-found, applied count - Show similar entry names in duplicate warnings (not just count) - Raise content limit from 1800 to 2500 chars - Export TRAINING_BUDGET constant, add budgetUsage() to TrainingPrompt - Add 30 new tests: auto-lowercase, update detection, budget overflow, name collision, scale (80 entries), improved messaging - All 118 training tests + 305 memory tests pass https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Enhance training UX: attribution, correction detection, priority sorting - Builder prompt: add attribution instructions (cite training entries that influenced output), correction detection (explicit + implicit patterns), conflict flagging between contradictory training entries - Add /teach, /train, /training-status to Available Skills list in builder prompt - Sort training entries by applied count (descending) in prompt injection so most-used entries get priority within the 6000-char budget - Restructure Teammate Training section with clear subsections https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Fix experience gaps from user journey simulations Simulation findings and fixes: 1. training_save now echoes back saved content so user can verify what was captured (new saves show content preview, updates show old vs new diff) 2. When training limit is reached, error now lists existing entries sorted by applied count and suggests the least-applied entry for removal 3. Researcher prompt now documents training_save/remove permissions (was contradicting its own permissions by saying "read-only" while having write access to training) 4. Added 10 new tests: content echo, update diff, limit suggestion, special character preservation (SQL -->, Jinja, HTML comments, code blocks), priority sorting verification Verified: --> in content does NOT corrupt meta block (false positive). The non-greedy regex terminates at the meta block's own --> correctly. 128 training tests + 305 memory tests all pass. https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * Add self-improvement loop: applied tracking, insights, staleness detection OpenClaw-inspired self-improvement mechanisms: 1. Wire up incrementApplied at injection time — counters now actually increment once per session per entry (deduped via session-scoped set), making "Most Applied" dashboard and priority sorting meaningful 2. TrainingInsights module analyzes training metadata and surfaces: - Stale entries (7+ days old, never applied) — suggests cleanup - High-value entries (5+ applications) — highlights most impactful - Near-limit warnings (18-19 of 20 entries per kind) - Consolidation opportunities (3+ entries with shared name prefix) 3. Insights automatically shown in training_list output 4. 24 new tests covering all insight types, boundary conditions, session tracking dedup, and format output 152 training tests + 305 memory tests all pass. https://claude.ai/code/session_01V17Kk3qCZFp9ZJiuNYucoq * fix: add dedicated training feature flag and remove unused insight type - Add `ALTIMATE_DISABLE_TRAINING` flag independent of memory's disable flag - Use new flag in session prompt injection and tool registry - Remove unused `budget-warning` insight type from `TrainingInsight` Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: reset training session tracking, add error logging, fix list truncation - Call `TrainingPrompt.resetSession()` at session start (step === 1) to prevent applied counters from growing unbounded across sessions - Add structured error logging to all three training tools - Add truncation indicator (`...`) when training list preview is cut off Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: use `.altimate-code/memory` as primary storage path with `.opencode` fallback Memory store was hardcoded to `.opencode/memory/` but the config system already uses `.altimate-code` as primary with `.opencode` as fallback. Now checks for `.altimate-code/` directory first, falls back to `.opencode/`, and defaults to `.altimate-code/` for new projects. Result is cached per process to avoid repeated filesystem checks. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add Trainer agent mode with pattern discovery and training validation Add dedicated trainer mode — the 8th primary agent — for systematically building the AI teammate's knowledge base. Unlike inline corrections in other modes, trainer mode actively scans codebases, validates training against reality, and guides knowledge curation. Changes: - New `trainer` agent mode with read-only permissions (no write/edit/sql_execute) - New `training_scan` tool: auto-discover patterns in models, SQL, config, tests, docs - New `training_validate` tool: check training compliance against actual codebase - Expand `TrainingKind` to 6 types: add `context` (background "why" knowledge) and `playbook` (multi-step procedures) - Update `count()` to derive from enum (prevents drift when kinds change) - Add KIND_HEADERS for context and playbook in prompt injection - Update injection order: rules first, playbooks last (budget priority) - Update training-save and training-list descriptions for new kinds Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add comprehensive training guide with scenarios and limitations - New `data-engineering/training/index.md` (350+ lines): - Quick start with 3 entry points (trainer mode, inline corrections, /train skill) - Deep dive into all 4 trainer workflows (scan, validate, teach, gap analysis) - 5 comprehensive scenarios: new project onboarding, post-incident learning, quarterly review, business domain teaching, pre-migration documentation - Explicit limitations section (not a hard gate, budget limits, no auto-learning, heuristic validation, no conflict resolution, no version history) - Full reference tables for tools, skills, limits, and feature flag - Updated `agent-modes.md`: add Researcher and Trainer mode sections with examples, capabilities, and "when to use" guidance - Updated `getting-started.md`: add training link to "Next steps" - Updated `mkdocs.yml`: add Training nav section under Data Engineering Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: increase training budget to 16K chars and rewrite docs as harness customization guide Training is not a CLAUDE.md replacement — it's the mechanism by which users customize the data engineering harness for their specific project. The agent works WITH the user to discover what it needs to know, rather than requiring users to write perfect static instructions. Changes: - Increase TRAINING_BUDGET from 6000 to 16000 chars (removes the #1 criticism from user simulations — budget was worse than unlimited CLAUDE.md) - Complete docs rewrite with correct positioning: - "Customizing Your AI Teammate" framing (not "Training Your AI Teammate") - Research-backed "why" section (40-70% knowledge omission, guided discovery) - Clear comparison table: training vs CLAUDE.md (complementary, not competing) - 6 real-world scenarios including Databricks, Salesforce quirks, cost spikes - Honest limitations section (not a linter, not an audit trail, not automatic) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: merge training into memory with context-aware relevance scoring Replace two parallel injection systems (memory 8KB + training 16KB) with a single unified injection that scores blocks by relevance to the current agent. How it works: - All blocks (memory + training) loaded in one pass - Each block scored: agent tag match (+10), training kind relevance per agent (+1-5), applied count bonus (+0-3), recency (+0-2), non-training base (+5) - Builder sees rules/patterns first; analyst sees glossary/context first - Budget is 20KB unified, filled greedily by score - Training blocks still tracked with applied counts (fire-and-forget) Architecture: - memory/prompt.ts: new scoreBlock(), unified inject() with InjectionContext - memory/types.ts: UNIFIED_INJECTION_BUDGET, AGENT_TRAINING_RELEVANCE weights - session/prompt.ts: single inject call with agent context (was 2 separate) - training/prompt.ts: deprecated, delegates to MemoryPrompt (backward compat) No changes to: MemoryStore, TrainingStore, training tools, memory tools. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor: cut training_scan and training_validate, simplify docs Research from 8 independent evaluations + SkillsBench (7,308 test runs) found that compact focused context beats comprehensive docs by 20pp. The training system's value is in correction capture (2-sec saves) and team propagation (git sync) — not in regex scanning or keyword grep. Removed: - training_scan (255 lines) — regex pattern counting, not discovery - training_validate (315 lines) — keyword grep, not validation Simplified: - trainer.txt: removed scan/validate workflows, focused on guided teaching and curation - agent-modes.md: updated trainer section with correction-focused example - training docs: complete rewrite with new pitch: "Correct the agent once. It remembers forever. Your team inherits it." Backed by SkillsBench research showing compact > comprehensive. Net: -753 lines. 152 tests pass. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove dead accepted/rejected fields, add training tips, expand limitations Gaps found by simulation team: 1. Remove `accepted`/`rejected` counters from TrainingBlockMeta — they were never incremented anywhere in the codebase (dead code since inception) 2. Add 5 training discoverability tips to TUI tips (was 0 mentions in 152 tips) 3. Expand limitations section in docs with honest, complete list: context budget, 20/kind limit, no approval workflow, SQL-focused, git discipline required Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: update site-wide docs for training and new agent modes - Homepage: update from "Four agents" to "Seven agents" — add Researcher, Trainer, Executive cards with descriptions - Getting Started: update training link to match new pitch "Corrections That Stick" - Tools index: add Training row (3 tools + 3 skills) with link - All references now consistent with simplified training system Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address Sentry review findings — 7 bugs fixed 1. stripTrainingMeta/parseTrainingMeta regex: remove multiline `m` flag that could match user content starting with `<!-- training` mid-string (types.ts, store.ts) 2. training_save content limit: reduce from 2500 to 1800 chars to account for ~200 char metadata overhead against MemoryStore's 2048 char limit (training-save.ts) 3. injectTrainingOnly: change `break` to `continue` so budget-exceeding section headers skip to next kind instead of stopping all injection (memory/prompt.ts) 4. injectTrainingOnly: track itemCount and return empty string when no items injected (was returning header-only string, inflating budget reports) (memory/prompt.ts) 5. projectDir cache: replace module-level singleton with Map keyed by Instance.directory to prevent stale paths when AsyncLocalStorage context changes across concurrent requests (memory/store.ts) 6. budgetUsage side effect: already fixed — delegates to injectTrainingOnly which is read-only (no applied count increment). Sentry comments were against pre-refactor code. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: CI failure + new Sentry finding — orphaned headers and agent test 1. Agent test: add researcher + trainer to "all disabled" test so it correctly expects "no primary visible agent" when ALL agents are off 2. Orphaned section headers: add pre-check that at least one entry fits before adding section header in both injectTrainingOnly and inject memory section (prevents header-only output inflating budget reports) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address multi-model code review findings Fixes from 6-model consensus review (Claude + GPT + Gemini + Kimi + MiniMax + GLM-5): 1. training_remove: add name validation regex matching training_save (Gemini finding — prevents path traversal via malformed names) 2. training_save: improve name transform to strip ALL non-alphanumeric chars, not just whitespace (Gemini finding — "don't-use-float!" now becomes "don-t-use-float" instead of failing regex) 3. incrementApplied: replace silent `.catch(() => {})` with warning log (Kimi + GLM-5 consensus — fire-and-forget is by design but failures should be visible in logs for debugging) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address new Sentry findings — regex m flag and off-by-one budget check 1. formatTrainingEntry regex: remove multiline `m` flag that could match user content mid-string (memory/prompt.ts:82) 2. Memory block budget check: change `<` to `<=` so blocks that fit exactly into remaining budget are included (memory/prompt.ts:204) 3 prior Sentry findings already fixed in earlier commits: - projectDir cache (Map keyed by Instance.directory) - injectTrainingOnly header-only return (itemCount guard) - orphaned section headers (first-entry pre-check) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address 6-model consensus review — 4 remaining bugs Fixes from consensus across Claude, GPT 5.2, Gemini 3.1, Kimi K2.5, MiniMax M2.5, and GLM-5: 1. parseTrainingMeta: check safeParse().success before accessing .data (GLM-5 + MiniMax consensus — accessing .data on failed parse returns undefined, could cause downstream errors) 2. Stale detection: use `e.updated` not `e.created` so entries updated recently aren't incorrectly flagged as stale (MiniMax finding) 3. training_list: pass scope/kind filter to count() so summary table matches the filtered entries list (GPT finding) 4. training_remove: show hint entries from same scope only, not all scopes (GPT + MiniMax finding) Prior fixes already addressed: name validation on remove (Gemini), name transform punctuation (Gemini), silent incrementApplied catch (Kimi + GLM-5), regex m flag (MiniMax + Sentry). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
This was referenced Mar 17, 2026
anandgupta42
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Mar 22, 2026
- Track loops by `(tool, inputHash)` not just tool name (#2) - Use "Failed after" narrative for error traces (#3) - Add keyboard accessibility to viewer tabs (role, tabindex, Enter/Space) (#4) - Use full command as dedup key, not `slice(0,60)` (#5) - Sort timeline events by time before rendering (#6) - Pass `tracesDir` to footer text in `listRecaps` (#7) - Increase `MAX_RECAPS` to 100, add eviction warning log (#8) - Resolve assistant `parentID` for recap enrichment (#9) - Remove unused `tracer` variable in test (#10) - Clarify `--no-trace` backward-compat flag in docs (#1) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
anandgupta42
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Mar 23, 2026
…381) * feat: rename tracer to recap with loop detection, post-session summary, and enhanced viewer - Rename `Tracer` class to `Recap` with backward-compat aliases - Rename CLI command `trace` to `recap` (hidden `trace` alias preserved) - Add loop detection: flags repeated tool calls with same input (3+ in last 10) - Add post-session summary: `narrative`, `topTools`, `loops` in trace output - New Summary tab (default) in HTML viewer with: - Truncated prompt with expand toggle - Files changed with SQL diff previews - Tool-agnostic outcome extraction (dbt, pytest, Airflow, pip, SQL) - Deduped dbt commands with pass/fail status, clickable to waterfall - Smart command grouping (boring ls/cd collapsed, meaningful shown) - Error details with resolution tracking - Cost breakdown in collapsible section - Virality: Share Recap (self-contained HTML download), Copy Summary (markdown), Copy Link, branded footer - Fix XSS: timeline items escaped with `e()` - Fix memory leak: per-session `sessionUserMsgIds` with cleanup on eviction - Fix JS syntax: onclick quote escaping in collapsible section - Bound `toolCallHistory` to prevent unbounded growth (cap at 200) - Summary view wrapped in try-catch for visible error messages - Update all 13 test files for rename + 8 new adversarial viewer tests - Update docs: `tracing.md` → `recap.md`, CLI/TUI references updated Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: share/copy buttons scoping bug + `t.text` undefined + adversarial viewer tests - Fix critical bug: Share Recap and Copy Summary buttons referenced variables from Summary IIFE scope — rewrote `buildMarkdownSummary` to be self-contained - Fix `t.text` → `t.result` in narrative (was rendering "undefined") - Fix `sessionUserMsgIds` not cleaned on MAX_RECAPS eviction (memory leak) - Fix zero cost display: show `$0.00` instead of em-dash - Add try-catch error boundary around Summary view rendering - Add 8 adversarial viewer tests: XSS, NaN/Infinity, null metadata, 200+ spans, JS syntax validation, tool-agnostic outcomes, backward compat Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address all 10 CodeRabbit review comments - Track loops by `(tool, inputHash)` not just tool name (#2) - Use "Failed after" narrative for error traces (#3) - Add keyboard accessibility to viewer tabs (role, tabindex, Enter/Space) (#4) - Use full command as dedup key, not `slice(0,60)` (#5) - Sort timeline events by time before rendering (#6) - Pass `tracesDir` to footer text in `listRecaps` (#7) - Increase `MAX_RECAPS` to 100, add eviction warning log (#8) - Resolve assistant `parentID` for recap enrichment (#9) - Remove unused `tracer` variable in test (#10) - Clarify `--no-trace` backward-compat flag in docs (#1) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add screenshots and update recap viewer documentation - Add Summary tab and full-page screenshots to docs - Update viewer section with 5-tab description - Detail what Summary tab shows: files changed, outcomes, timeline, cost - Add screenshot at top of recap.md for quick visual reference Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: move Recap to Use section, Telemetry to Reference - Move Recap from Configure > Observability to Use (peer to Commands, Skills) - Move Telemetry from Configure > Observability to Reference (internal analytics) - Remove the Observability section entirely Recap is a feature users interact with after sessions, not a config setting. Telemetry is internal product analytics, not user-facing observability. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: viewer UX improvements from 100-trace analysis - Collapse Files Changed after 5 entries with "Show all N files" toggle - Rename "GENS" → "LLM Calls" in header cards - Hide Tokens card when cost is $0 (not actionable without cost context) - Hide Cost metric card when $0.00 (wasted space) - Add prominent error summary banner right after header metrics - Improved dbt outcome detection: catch [PASS], [ERROR], N of M, Compilation Error - Outcome detection rate improved from 18% → 33% across 100 real traces - Updated doc screenshots with cleaner samples Tested across 100 real production traces: 0 crashes, 0 JS errors. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: always show Cost and Tokens cards $0.00 is a valid cost (Anthropic Max plan). Hiding it implies we don't support cost tracking. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: tool-agnostic outcome extraction for schema, validation, SQL, lineage tools 500-trace analysis revealed: - Schema tasks: 0% outcome visibility → 100% - Validation tasks: 0% outcome visibility → 100% - SQL tasks: 55% outcome visibility → 100% Added outcome extraction for: - schema_inspect, lineage_check, altimate_core_validate results - SQL error messages (not just row counts) - Improved empty session display (shows prompt if available) Tested across 500 diverse synthetic traces (SQL, Airflow, Dagster, Python, schema, validation, migration, connectors) + 100 real traces. 0 crashes, 0 JS errors. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address 4 new CodeRabbit review comments - Add `inputHash` to `TraceFile.summary.loops` schema type (#11) - Replace `startTrace()` API name with plain language in docs (#12) - Use `CSS.escape()` for spanId in querySelector to handle special chars (#13) - Sort spans by startTime before searching for error resolution (#14) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: round 3 review — sort spans once, clean narrative for 0 LLM calls - Sort spans once before error resolution loop instead of per-error (perf) - Narrative omits "Made 0 LLM calls" for tool-only sessions (UX) - Updated tests to match new narrative format Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: add missing `altimate_change` markers for recap rename in upstream-shared files Wrap renamed code (Tracer→Recap, trace→recap) with markers so the Marker Guard CI check passes. The diff-based checker uses -U5 context windows per hunk — markers must be close enough to added lines to appear within each hunk's context. Files fixed: - `trace.ts` — handler body, option descriptions, viewer message, compat alias - `app.tsx` — recapViewerServer return, openRecapInBrowser function - `dialog-trace-list.tsx` — error title, Recaps title, compat alias - `worker.ts` — getOrCreateRecap, part events, session title/finalization - `index.ts` — .command(RecapCommand) registration Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: add altimate_change markers to all upstream-shared files Marker Guard CI was failing — 5 upstream-shared files had custom code (recap rename) without altimate_change markers. Fixed: trace.ts, app.tsx, dialog-trace-list.tsx, worker.ts, index.ts Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: type errors in training-import.test.ts from main merge Pre-existing type issues from main: mock missing `context`/`rule` fields and readFile return type mismatch. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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kulvirgit
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Mar 30, 2026
…381) * feat: rename tracer to recap with loop detection, post-session summary, and enhanced viewer - Rename `Tracer` class to `Recap` with backward-compat aliases - Rename CLI command `trace` to `recap` (hidden `trace` alias preserved) - Add loop detection: flags repeated tool calls with same input (3+ in last 10) - Add post-session summary: `narrative`, `topTools`, `loops` in trace output - New Summary tab (default) in HTML viewer with: - Truncated prompt with expand toggle - Files changed with SQL diff previews - Tool-agnostic outcome extraction (dbt, pytest, Airflow, pip, SQL) - Deduped dbt commands with pass/fail status, clickable to waterfall - Smart command grouping (boring ls/cd collapsed, meaningful shown) - Error details with resolution tracking - Cost breakdown in collapsible section - Virality: Share Recap (self-contained HTML download), Copy Summary (markdown), Copy Link, branded footer - Fix XSS: timeline items escaped with `e()` - Fix memory leak: per-session `sessionUserMsgIds` with cleanup on eviction - Fix JS syntax: onclick quote escaping in collapsible section - Bound `toolCallHistory` to prevent unbounded growth (cap at 200) - Summary view wrapped in try-catch for visible error messages - Update all 13 test files for rename + 8 new adversarial viewer tests - Update docs: `tracing.md` → `recap.md`, CLI/TUI references updated Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: share/copy buttons scoping bug + `t.text` undefined + adversarial viewer tests - Fix critical bug: Share Recap and Copy Summary buttons referenced variables from Summary IIFE scope — rewrote `buildMarkdownSummary` to be self-contained - Fix `t.text` → `t.result` in narrative (was rendering "undefined") - Fix `sessionUserMsgIds` not cleaned on MAX_RECAPS eviction (memory leak) - Fix zero cost display: show `$0.00` instead of em-dash - Add try-catch error boundary around Summary view rendering - Add 8 adversarial viewer tests: XSS, NaN/Infinity, null metadata, 200+ spans, JS syntax validation, tool-agnostic outcomes, backward compat Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address all 10 CodeRabbit review comments - Track loops by `(tool, inputHash)` not just tool name (#2) - Use "Failed after" narrative for error traces (#3) - Add keyboard accessibility to viewer tabs (role, tabindex, Enter/Space) (#4) - Use full command as dedup key, not `slice(0,60)` (#5) - Sort timeline events by time before rendering (#6) - Pass `tracesDir` to footer text in `listRecaps` (#7) - Increase `MAX_RECAPS` to 100, add eviction warning log (#8) - Resolve assistant `parentID` for recap enrichment (#9) - Remove unused `tracer` variable in test (#10) - Clarify `--no-trace` backward-compat flag in docs (#1) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add screenshots and update recap viewer documentation - Add Summary tab and full-page screenshots to docs - Update viewer section with 5-tab description - Detail what Summary tab shows: files changed, outcomes, timeline, cost - Add screenshot at top of recap.md for quick visual reference Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: move Recap to Use section, Telemetry to Reference - Move Recap from Configure > Observability to Use (peer to Commands, Skills) - Move Telemetry from Configure > Observability to Reference (internal analytics) - Remove the Observability section entirely Recap is a feature users interact with after sessions, not a config setting. Telemetry is internal product analytics, not user-facing observability. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: viewer UX improvements from 100-trace analysis - Collapse Files Changed after 5 entries with "Show all N files" toggle - Rename "GENS" → "LLM Calls" in header cards - Hide Tokens card when cost is $0 (not actionable without cost context) - Hide Cost metric card when $0.00 (wasted space) - Add prominent error summary banner right after header metrics - Improved dbt outcome detection: catch [PASS], [ERROR], N of M, Compilation Error - Outcome detection rate improved from 18% → 33% across 100 real traces - Updated doc screenshots with cleaner samples Tested across 100 real production traces: 0 crashes, 0 JS errors. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: always show Cost and Tokens cards $0.00 is a valid cost (Anthropic Max plan). Hiding it implies we don't support cost tracking. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: tool-agnostic outcome extraction for schema, validation, SQL, lineage tools 500-trace analysis revealed: - Schema tasks: 0% outcome visibility → 100% - Validation tasks: 0% outcome visibility → 100% - SQL tasks: 55% outcome visibility → 100% Added outcome extraction for: - schema_inspect, lineage_check, altimate_core_validate results - SQL error messages (not just row counts) - Improved empty session display (shows prompt if available) Tested across 500 diverse synthetic traces (SQL, Airflow, Dagster, Python, schema, validation, migration, connectors) + 100 real traces. 0 crashes, 0 JS errors. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address 4 new CodeRabbit review comments - Add `inputHash` to `TraceFile.summary.loops` schema type (#11) - Replace `startTrace()` API name with plain language in docs (#12) - Use `CSS.escape()` for spanId in querySelector to handle special chars (#13) - Sort spans by startTime before searching for error resolution (#14) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: round 3 review — sort spans once, clean narrative for 0 LLM calls - Sort spans once before error resolution loop instead of per-error (perf) - Narrative omits "Made 0 LLM calls" for tool-only sessions (UX) - Updated tests to match new narrative format Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: add missing `altimate_change` markers for recap rename in upstream-shared files Wrap renamed code (Tracer→Recap, trace→recap) with markers so the Marker Guard CI check passes. The diff-based checker uses -U5 context windows per hunk — markers must be close enough to added lines to appear within each hunk's context. Files fixed: - `trace.ts` — handler body, option descriptions, viewer message, compat alias - `app.tsx` — recapViewerServer return, openRecapInBrowser function - `dialog-trace-list.tsx` — error title, Recaps title, compat alias - `worker.ts` — getOrCreateRecap, part events, session title/finalization - `index.ts` — .command(RecapCommand) registration Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: add altimate_change markers to all upstream-shared files Marker Guard CI was failing — 5 upstream-shared files had custom code (recap rename) without altimate_change markers. Fixed: trace.ts, app.tsx, dialog-trace-list.tsx, worker.ts, index.ts Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: type errors in training-import.test.ts from main merge Pre-existing type issues from main: mock missing `context`/`rule` fields and readFile return type mismatch. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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suryaiyer95
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Apr 17, 2026
Fixes five correctness, reliability, and portability issues surfaced by the consensus code review of this branch. CRITICAL #1 — Cross-dialect partitioned diff (`data-diff.ts`): `runPartitionedDiff` built one partition WHERE clause with `sourceDialect` and passed it as shared `where_clause` to the recursive `runDataDiff`, which applied it to both warehouses identically. Cross-dialect partition mode (MSSQL → Postgres) failed because the target received T-SQL `DATETRUNC`/`CONVERT(DATE, …, 23)`. Now builds per-side WHERE using each warehouse's dialect and bakes it into dialect-quoted subquery SQL for source and target independently. The existing side-aware CTE injection handles the rest. MAJOR #2 — Azure AD token caching and refresh (`sqlserver.ts`): `acquireAzureToken` fetched a fresh token on every `connect()` and embedded it in the pool config with no refresh. Long-lived sessions silently failed when the ~1h token expired. Adds a module-scoped cache keyed by `(resource, client_id)` with proactive refresh 5 min before expiry, parsing `expiresOnTimestamp` from `@azure/identity` or the JWT `exp` claim from the `az` CLI fallback. Exposes `_resetTokenCacheForTests` for isolation. MAJOR #3 — `joindiff` + cross-warehouse guard (`data-diff.ts`): Explicit `algorithm: "joindiff"` combined with different warehouses produced broken SQL (one task referencing two CTE aliases with only one injected). Now returns an early error with a clear message steering users to `hashdiff` or `auto`. Cross-warehouse detection switched from warehouse-name string compare to dialect compare, matching the underlying SQL-divergence invariant. MAJOR #4 — Dialect-aware identifier quoting in CTE wrapping (`data-diff.ts`): `resolveTableSources` wrapped plain-table names with ANSI double-quotes for all dialects. T-SQL/Fabric require `QUOTED_IDENTIFIER ON` for this to work; default for `mssql`/tedious is ON, but user contexts (stored procs, legacy collations) can override. Now accepts source/target dialect parameters and delegates to `quoteIdentForDialect`, which was hoisted to module scope so it can be reused across partition and CTE paths. MAJOR #5 — Configurable Azure resource URL (`sqlserver.ts`, `normalize.ts`): Token acquisition hardcoded `https://database.windows.net/`, blocking Azure Government, Azure China, and sovereign-cloud customers. Now honours an explicit `azure_resource_url` config field and otherwise infers the URL from the host suffix (`.usgovcloudapi.net`, `.chinacloudapi.cn`). Adds the usual camelCase/snake_case aliases in the SQL Server normalizer. Also surfaces Azure auth error causes: if both `@azure/identity` and `az` CLI fail, the thrown error includes both hints (redacted) so users know why rather than seeing the generic "install @azure/identity or run az login" message. Tests: adds `data-diff-cross-dialect.test.ts` covering the cross-dialect partition WHERE routing and the `joindiff` guard; extends `data-diff-cte.test.ts` with dialect-aware quoting assertions for tsql, fabric, and mysql; extends `sqlserver-unit.test.ts` with cache hit / expiry refresh / client-id keyed cache tests, commercial/gov/china/custom resource URL resolution, and the combined-error-hints surface. All 41 sqlserver driver tests, 24 data-diff orchestrator tests, and 214 normalize/connections tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
anandgupta42
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Apr 21, 2026
* fix: use synchronous DuckDB constructor to avoid bun runtime timeout Bun's runtime never fires native addon async callbacks, so the async `new duckdb.Database(path, opts, callback)` form would hit the 2-second timeout fallback on every connection attempt. Switch to the synchronous constructor form `new duckdb.Database(path)` / `new duckdb.Database(path, opts)` which throws on error and completes immediately in both Node and bun runtimes. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * revert: restore async DuckDB constructor — sync change was bogus The async callback form with 2s fallback was already working correctly at e3df5a4. The timeout was caused by a missing duckdb .node binary, not a bun incompatibility. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat: add MSSQL/Fabric dialect mapping and data-parity support - Add `warehouseTypeToDialect()` mapping: sqlserver→tsql, mssql→tsql, fabric→fabric, postgresql→postgres, mariadb→mysql. Fixes critical serde mismatch where Rust engine rejects raw warehouse type names. - Update both `resolveDialect()` functions to use the mapping - Add MSSQL/Fabric cases to `dateTruncExpr()` — DATETRUNC(DAY, col) - Add locale-safe date literal casting via CONVERT(DATE, ..., 23) - Register `fabric` in DRIVER_MAP (reuses sqlserver TDS driver) - Add `fabric` normalize aliases in normalize.ts - Add 15 SQL Server driver unit tests (TOP injection, truncation, schema introspection, connection lifecycle, result format) - Add 9 dialect mapping unit tests Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add Azure AD authentication to SQL Server driver (7 flows) - Support all 7 Azure AD / Entra ID auth types in `sqlserver.ts`: `azure-active-directory-password`, `access-token`, `service-principal-secret`, `msi-vm`, `msi-app-service`, `azure-active-directory-default`, `token-credential` - Force TLS encryption for all Azure AD connections - Dynamic import of `@azure/identity` for `DefaultAzureCredential` - Add normalize aliases for Azure AD config fields (`authentication`, `azure_tenant_id`, `azure_client_id`, `azure_client_secret`, `access_token`) - Add `fabric: SQLSERVER_ALIASES` to DRIVER_ALIASES - Add 10 Azure AD unit tests covering all auth flows, encryption, and `DefaultAzureCredential` with managed identity Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: add MSSQL and Microsoft Fabric documentation to data-parity SKILL.md - Add SQL Server / Fabric schema inspection query in Step 2 - Add "SQL Server and Microsoft Fabric" section with: - Supported configurations table (sqlserver, mssql, fabric) - Fabric connection guide with Azure AD auth types - Algorithm behavior notes (joindiff vs hashdiff selection) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: delegate Azure AD credential creation to tedious and remove underscore column filter - **Azure AD auth**: Pass `azure-active-directory-*` types directly to tedious instead of constructing `DefaultAzureCredential` ourselves. Tedious imports `@azure/identity` internally and creates credentials — avoids bun CJS/ESM `isTokenCredential` boundary issue that caused "not an instance of the token credential class" errors. - **Auth shorthands**: Map `CLI`, `default`, `password`, `service-principal`, `msi`, `managed-identity` to their full tedious type names. - **Column filter**: Remove `_.startsWith("_")` filter from `execute()` result columns — it stripped legitimate aliases like `_p` used by partition discovery, causing partitioned diffs to return empty results. - **Tests**: Remove `@azure/identity` mock (no longer imported by driver), update auth assertions, add shorthand mapping tests, fix column filter test. - **Verified**: All 97 driver tests pass. Full data-diff pipeline tested against real MSSQL server (profile, joindiff, auto, where_clause, partitioned). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: upgrade `mssql` to v12 with `ConnectionPool` isolation and row flattening - Upgrade `mssql` from v11 to v12 (`tedious` 18 → 19) - Use explicit `ConnectionPool` instead of global `mssql.connect()` to isolate multiple simultaneous connections - Flatten unnamed column arrays — `mssql` merges unnamed columns (e.g. `SELECT COUNT(*), SUM(...)`) into a single array under the empty-string key; restore positional column values - Proper column name resolution: compare `namedKeys.length` against flattened row length, fall back to synthetic `col_0`, `col_1`, etc. - Update test mock to export `ConnectionPool` class and `createMockPool` Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: resolve TypeScript spread-type errors in Azure AD conditional options Use ternary expressions (`x ? {...} : {}`) instead of short-circuit (`x && {...}`) to avoid spreading a boolean value. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: resolve cubic review findings on MSSQL/Fabric PR - P1: restrict `flattenRow` to only spread the empty-string key (`""`) where mssql merges unnamed columns, preserving legitimate array values - P2: escape single quotes in `partitionValue` for date-mode branches in `buildPartitionWhereClause` (categorical mode already escaped) - P2: add `fabric` to `PASSWORD_DRIVERS` set in registry for consistent password validation alongside `sqlserver`/`mssql` - P2: fallback to `"(no values)"` when `d.values` is nullish to prevent template literal coercing `undefined` to the string `"undefined"` Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * test: add fabric connection path and flattenRow coverage - sqlserver-unit: 3 tests for unnamed column flattening — verifies only the empty-string key is spread, legitimate named arrays are preserved - driver-normalize: fabric type uses SQLSERVER_ALIASES (server → host, trustServerCertificate → trust_server_certificate) - connections: fabric type is recognized in DRIVER_MAP and listed correctly Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: document minimum versions and make @azure/identity optional - Add "Minimum Version Requirements" table to SKILL.md covering SQL Server 2022+, mssql v12, and @azure/identity v4 with rationale for each - Document auth shorthands (CLI, default, password, service-principal, msi) - Move @azure/identity from dependencies to optional peerDependencies so it is NOT installed by default — only required for Azure AD auth - Add runtime check in sqlserver driver: if Azure AD auth type is requested but @azure/identity is missing, throw a clear install instruction error Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: acquire Azure AD tokens directly to bypass Bun browser-bundle resolution - For `azure-active-directory-default` (CLI/default auth), acquire token ourselves instead of delegating to tedious's internal `@azure/identity` - Strategy: try `DefaultAzureCredential` first, fall back to `az` CLI subprocess - Bypasses Bun resolving `@azure/identity` to browser bundle where `DefaultAzureCredential` is a non-functional stub - Also bypasses CJS/ESM `isTokenCredential` boundary mismatch - All 31 driver unit tests pass, verified against real Fabric endpoint Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: auto-acquire Azure AD token for `azure-active-directory-access-token` when none supplied The `azure-active-directory-access-token` branch passed `token: config.token ?? config.access_token` to tedious. When neither field was set on a connection (e.g. a `fabric-migration` entry that declared the auth type but no token), tedious threw: TypeError: The "config.authentication.options.token" property must be of type string This blocked any Fabric/MSSQL config that relied on ambient credentials (Azure CLI / managed identity) but used the explicit `azure-active-directory-access-token` type instead of the `default` shorthand. Refactor token acquisition (`DefaultAzureCredential` → `az` CLI fallback) into a shared `acquireAzureToken()` helper used by both the `default` path and the `access-token` path when no token was supplied. Callers that pass an explicit token are unchanged. Also harden `mock.module("node:child_process", ...)` in `sqlserver-unit.test.ts` to spread the real module so sibling tests in the same `bun test` run keep access to `spawn` / `exec` / `fork`. Tests: 110 pass, 0 fail in `packages/drivers`. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * fix: side-aware CTE injection for cross-warehouse `data_diff` SQL-query mode When `source` and `target` are both SQL queries, `resolveTableSources` wraps them as `__diff_source` / `__diff_target` CTEs and the executor prepends the combined `WITH …` block to every engine-emitted task. T-SQL and Fabric parse-bind every CTE body even when unreferenced, so a task routed to the source warehouse failed to resolve the target-only base table referenced inside the unused `__diff_target` CTE (and vice versa), producing `Invalid object name` errors from the wrong warehouse. Return side-specific prefixes from `resolveTableSources` alongside the combined one, and have the executor loop in `runDataDiff` pick the source or target prefix per task when `source_warehouse !== target_warehouse`. Same-warehouse behaviour is unchanged. Adds `data-diff-cte.test.ts` covering plain-name passthrough, both-query wrapping, side-specific CTE isolation, and CTE merging with engine-emitted `WITH` clauses (10 tests). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore: regenerate `bun.lock` to match drivers `peerDependencies` layout Commit 333a45c moved `@azure/identity` from `optionalDependencies` to `peerDependencies` with `optional: true` in `packages/drivers/package.json`, but the lockfile was not regenerated. That left CI under `--frozen-lockfile` broken and made fresh installs silently diverge from the committed state. Running `bun install` brings the lockfile in sync: `@azure/identity` is recorded as an optional peer, and its transitive pins (`@azure/msal-browser`, `@azure/msal-common`, `@azure/msal-node`) re-resolve to the versions required by `tedious` and `snowflake-sdk`, matching the reachable runtime surface. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: address all CRITICAL/MAJOR findings from multi-model review Fixes five correctness, reliability, and portability issues surfaced by the consensus code review of this branch. CRITICAL #1 — Cross-dialect partitioned diff (`data-diff.ts`): `runPartitionedDiff` built one partition WHERE clause with `sourceDialect` and passed it as shared `where_clause` to the recursive `runDataDiff`, which applied it to both warehouses identically. Cross-dialect partition mode (MSSQL → Postgres) failed because the target received T-SQL `DATETRUNC`/`CONVERT(DATE, …, 23)`. Now builds per-side WHERE using each warehouse's dialect and bakes it into dialect-quoted subquery SQL for source and target independently. The existing side-aware CTE injection handles the rest. MAJOR #2 — Azure AD token caching and refresh (`sqlserver.ts`): `acquireAzureToken` fetched a fresh token on every `connect()` and embedded it in the pool config with no refresh. Long-lived sessions silently failed when the ~1h token expired. Adds a module-scoped cache keyed by `(resource, client_id)` with proactive refresh 5 min before expiry, parsing `expiresOnTimestamp` from `@azure/identity` or the JWT `exp` claim from the `az` CLI fallback. Exposes `_resetTokenCacheForTests` for isolation. MAJOR #3 — `joindiff` + cross-warehouse guard (`data-diff.ts`): Explicit `algorithm: "joindiff"` combined with different warehouses produced broken SQL (one task referencing two CTE aliases with only one injected). Now returns an early error with a clear message steering users to `hashdiff` or `auto`. Cross-warehouse detection switched from warehouse-name string compare to dialect compare, matching the underlying SQL-divergence invariant. MAJOR #4 — Dialect-aware identifier quoting in CTE wrapping (`data-diff.ts`): `resolveTableSources` wrapped plain-table names with ANSI double-quotes for all dialects. T-SQL/Fabric require `QUOTED_IDENTIFIER ON` for this to work; default for `mssql`/tedious is ON, but user contexts (stored procs, legacy collations) can override. Now accepts source/target dialect parameters and delegates to `quoteIdentForDialect`, which was hoisted to module scope so it can be reused across partition and CTE paths. MAJOR #5 — Configurable Azure resource URL (`sqlserver.ts`, `normalize.ts`): Token acquisition hardcoded `https://database.windows.net/`, blocking Azure Government, Azure China, and sovereign-cloud customers. Now honours an explicit `azure_resource_url` config field and otherwise infers the URL from the host suffix (`.usgovcloudapi.net`, `.chinacloudapi.cn`). Adds the usual camelCase/snake_case aliases in the SQL Server normalizer. Also surfaces Azure auth error causes: if both `@azure/identity` and `az` CLI fail, the thrown error includes both hints (redacted) so users know why rather than seeing the generic "install @azure/identity or run az login" message. Tests: adds `data-diff-cross-dialect.test.ts` covering the cross-dialect partition WHERE routing and the `joindiff` guard; extends `data-diff-cte.test.ts` with dialect-aware quoting assertions for tsql, fabric, and mysql; extends `sqlserver-unit.test.ts` with cache hit / expiry refresh / client-id keyed cache tests, commercial/gov/china/custom resource URL resolution, and the combined-error-hints surface. All 41 sqlserver driver tests, 24 data-diff orchestrator tests, and 214 normalize/connections tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: address PR #705 bot review findings (coderabbitai + cubic + copilot) Addresses the remaining issues raised by coderabbitai, cubic-dev-ai, and the Copilot PR reviewer on top of the multi-model consensus fix. ### CRITICAL - **`@azure/identity` peer dep removed** (`drivers/package.json`) `mssql@12` → `tedious@19` bundles `@azure/identity ^4.2.1` as a regular dependency. Declaring it here as an optional peer was redundant and caused transitive-version-drift concerns. Users get the correct version automatically through the tedious chain; our lazy import handles the browser-bundle edge case itself. ### MAJOR - **Cross-dialect date partition literal normalization** (`data-diff.ts`) `buildPartitionDiscoverySQL` on MSSQL returns a JS `Date` object, stringified upstream as `"Mon Jan 01 2024 …"`. `CONVERT(DATE, …, 23)` rejects that format. Normalize `partitionValue` to ISO `yyyy-mm-dd` before dialect casting so the T-SQL/Fabric path works end-to-end on dates discovered from MSSQL sources. - **`crossWarehouse` uses resolved warehouse identity** (`data-diff.ts`) Previous commit gated on dialect compare, which treated two independent MSSQL instances as "same warehouse" and would have let `joindiff` route a JOIN through a warehouse that can't resolve the other side's base tables. Now resolves both sides' warehouse name (falling back to the default warehouse when a side is omitted) and compares identities — identity-based gating handles both the "undefined vs default" case (cubic) and the "same-dialect, different instance" case (Copilot). - **Drop `mssql.connect()` fallback** (`sqlserver.ts`) `mssql@^12` guarantees `ConnectionPool` as a named export. The fallback silently re-introduced the global-shared-pool bug this branch was added to fix. Now throws a descriptive error if `ConnectionPool` is missing — cross-database pool interference cannot regress. - **Non-string `config.authentication` guarded** (`sqlserver.ts`) Caller passing a pre-built `{ type, options }` block (or `null`) previously crashed with `TypeError: rawAuth.toLowerCase is not a function`. Now only applies the shorthand lookup when `rawAuth` is a string; other values pass through so tedious can handle them or reject them with its own error. - **Unknown `azure-active-directory-*` subtype fails fast** (`sqlserver.ts`) Typos or future tedious subtypes previously dropped through all `else if` branches, producing a config with `encrypt: true` but no `authentication` block. tedious then surfaced an opaque error far from the root cause. Now throws with the offending subtype and the supported list. - **`execSync` replaced with async `exec`** (`sqlserver.ts`) The `az account get-access-token` CLI fallback previously blocked the event loop for up to 15s. Switched to `util.promisify(exec)` so the connection path stays non-blocking. - **Mixed named + unnamed column derivation preserves headers** (`sqlserver.ts`) Previously `SELECT name, COUNT(*), SUM(x)` produced either `["name", ""]` (blank header) or `["col_0", "col_1", "col_2"]` (lost `name`). Rewrote column/row derivation to iterate in one pass, preserving known named columns and synthesizing `col_N` only for expanded `""`-key positions. ### MINOR - **`(no values)` fallback for empty `diff_row.values` array** (`tools/data-diff.ts`) `[].join(" | ") ?? "(no values)"` never fires because `""` is falsy-but-not- nullish. Gate on `d.values?.length` instead. ### Test / docs - `sqlserver-unit.test.ts`: token-cache client-id test now counts actual `getToken` invocations (previous version only verified both got the same mocked token, which proved nothing about keying). - `sqlserver-unit.test.ts`: "empty result" test now mirrors the real mssql shape (`recordset.columns` is a property *on* the recordset array, not a sibling key). - `sqlserver-unit.test.ts`: added mixed-column regression tests — "name + COUNT + SUM" and "single unnamed column" — to lock in the derivation fix. - `sqlserver-unit.test.ts`: stubbed async `exec` via `util.promisify.custom` so tests drive both the `execSync` legacy path and the new async path. - `SKILL.md`: Fabric config fenced block now declares `yaml` (markdownlint MD040). All tests: 43/43 sqlserver driver + 238/238 opencode test suite. Attribution: findings identified by coderabbitai, cubic-dev-ai, and the Copilot PR reviewer. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore: drop stale `@azure/identity` peer-dep entries from `bun.lock` Commit 38cfb0e removed `@azure/identity` from the drivers package's `peerDependencies` (tedious already bundles it), but the lockfile's `packages/drivers` workspace section still carried the corresponding `peerDependencies` and `optionalPeers` blocks. CI running `bun install --frozen-lockfile` would fail on the drift. Minimal edit — just removes the two stale blocks. No resolution changes (`bun install --frozen-lockfile` passes with "no changes"). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: CI — isolate `data-diff-cross-dialect` tests from other files The prior integration-style test mocked the Registry module globally with `mock.module(".../registry", ...)`, which leaks across all test files in bun:test's single-process runner. That caused 14 unrelated tests in `connections.test.ts`, `telemetry-safety.test.ts`, and `dbt-first-execution.test.ts` to fail in CI. Additionally, the test relied on `mock.module("@altimateai/altimate-core")` to supply a fake `DataParitySession`. The npm-published 0.2.6 of that package does not export `DataParitySession` (sessions are only in the locally-built `altimate-core-internal` binary), and Bun's `mock.module` cannot override a package that another test file has already imported — so the integration test was structurally unreliable. Resolution: 1. **Export pure SQL-builder helpers** from `data-diff.ts` (`dateTruncExpr`, `buildPartitionWhereClause`) and unit-test them directly. No module mocking required; the test directly exercises the logic the CRITICAL/MAJOR fix changed. 2. **Move the `joindiff` + cross-warehouse guard earlier** in `runDataDiff` — before the NAPI import. Semantically identical for callers (guard still fires, same error message, `steps: 0`), but now it can be integration-tested without any NAPI mock. Preserves end-to-end wiring coverage for the guard. 3. **Rewrite `data-diff-cross-dialect.test.ts`** as pure-function unit tests for the partition WHERE logic + a real `runDataDiff` call for the joindiff guard. No more cross-file mock pollution. Functionality unchanged: - `runDataDiff` behavior for real callers is identical. The only observable difference is error-ordering: if a caller simultaneously omits NAPI and passes `joindiff + cross-warehouse`, they now get the "joindiff requires same warehouse" error instead of the NAPI-missing error. That's strictly better UX — NAPI availability is a deployment concern, `joindiff`+cross-warehouse is a user error. - `buildPartitionWhereClause` and `dateTruncExpr` are now exported but semantically unchanged — same inputs, same outputs. Test results: - 2821 altimate tests pass, 0 fail - 43 sqlserver driver tests pass, 0 fail - The 19 remaining full-suite failures (`mcp/`, `tool/project-scan`, `plan-approval-phrase`) are pre-existing on `main` and unrelated. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: follow-up PR bot review findings (cubic P1/P2 + coderabbit MAJOR/MINOR) Addresses 5 substantive issues raised by the latest round of bot reviews. ### P1 / MAJOR - **MySQL/MariaDB week-partition values no longer corrupted** (cubic P1, data-diff.ts:610) — the prior ISO `yyyy-mm-dd` normalization applied to every dialect silently rewrote MySQL `DATE_FORMAT(%Y-%u)` outputs like `"2024-42"` into invalid dates, producing WHERE clauses that never match. Scope the normalization to T-SQL / Fabric only — those use `CONVERT(DATE, …, 23)` which is the only code path that requires ISO. Postgres, MySQL, ClickHouse, BigQuery, Oracle all get the raw value verbatim, matching their own `DATE_TRUNC`/`toStartOf*` output. - **Partitioned diff no longer drops extra_columns** (coderabbit MAJOR, data-diff.ts:824) — the partition fix wraps each side as a SELECT subquery before recursing. `discoverExtraColumns` skips SQL queries (only inspects plain table names), so the recursive `runDataDiff` fell through to key-only comparison, silently losing value-level diffs. Now `runPartitionedDiff` runs discovery ONCE on the plain source table up-front and passes the resolved `extra_columns` explicitly to each recursive call. Audit-column exclusion metadata is also propagated to the aggregated result for user reporting. ### P2 / MINOR - **`azure_resource_url` trailing slash normalized** (cubic P2, sqlserver.ts:50) — an explicit `"https://custom-host"` (no slash) would produce an invalid OAuth scope `"https://custom-host.default"`. Enforce a trailing slash in `resolveAzureResourceUrl`. - **`az account get-access-token` uses `execFile`** (coderabbit, sqlserver.ts:200) — replaces `exec(<shell command string>)` with `execFile("az", [args])` so user-supplied `azure_resource_url` can't introduce shell metacharacters into the command string. Also updates the test harness to stub both `exec` and `execFile`. ### Test isolation / coverage - **Added same-dialect cross-warehouse joindiff test** (cubic, data-diff-cross-dialect.test.ts:97) — two MSSQL servers with different hosts must still be gated by the joindiff guard; previous tests only exercised mixed dialects. - **Added MySQL week-partition regression tests** — prevent future revivals of the dialect-unaware ISO rewrite. - **Added trailing-slash `azure_resource_url` test.** Test results: - 44/44 sqlserver driver tests pass - 2824/2824 altimate tests pass, 0 fail - Remaining full-suite failures (`mcp/`, `tool/project-scan`, `plan-approval-phrase`) are pre-existing on `main`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
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Bumps minimatch from 10.0.3 to 10.2.3.
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ea9484010.2.30873fbaupdate depscecaad1more extglob coalescing for performance11d0df6limit nested extglob recursion, flatten extglobsc3448c4update assertValidPattern param type to unknown from any0bf499alimit recursion for **, improve perf considerably9f15c58update depsf42b23910.2.2fa2133bupdate depsb9d0153ci: update action workflowsDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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