* auto-claude: subtask-0a-1 - Install Vercel AI SDK v6 core + all provider packages Added dependencies: ai@^6, @ai-sdk/anthropic, @ai-sdk/openai, @ai-sdk/google, @ai-sdk/amazon-bedrock, @ai-sdk/azure, @ai-sdk/mistral, @ai-sdk/groq, @ai-sdk/xai, @ai-sdk/openai-compatible, @ai-sdk/mcp, @modelcontextprotocol/sdk. Verified zod/v3 compat works with existing zod v4. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0b-1 - Create provider types and config interfaces Define SupportedProvider enum, ProviderConfig, ModelResolution, and ProviderCapabilities types. Port MODEL_ID_MAP, THINKING_BUDGET_MAP, MODEL_BETAS_MAP, and phase config types from phase_config.py. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0b-2 - Create provider factory: createProvider(config) → LanguageModel Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0b-3 - Create provider registry using createProviderRegistry Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0b-4 - Create per-provider transforms layer Port thinking token normalization, tool ID format transforms, prompt caching thresholds, and adaptive thinking support from phase_config.py. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0c-1 - Port command-parser.ts from Python security/parser Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0c-2 - Port bash-validator.ts from Python security/hooks. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0c-3 - Create path-containment.ts for filesystem boundary Add path-containment.ts with assertPathContained() for filesystem boundary enforcement including symlink resolution, traversal prevention, and cross-platform normalization. Add security-profile.ts for loading and caching project security profiles from .auto-claude config files. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0c-4 - Write comprehensive Vitest tests for the security layer Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0d-1 - Create tool types and Tool.define() wrapper Define ToolContext interface (cwd, projectDir, specDir, securityProfile), ToolPermission types, ToolExecutionOptions, and ToolDefinitionConfig. Create Tool.define() that wraps AI SDK v6 tool() with Zod v3 inputSchema and security hooks integration (bash validator pre-execution check). Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0d-2 - Create 4 filesystem tools (Read, Write, Edit, Glob) Implements Read (line offset/limit, image base64, PDF support), Write (content validation, mkdir -p), Edit (exact string replacement, replace_all), and Glob (fs.globSync, mtime sort) with Zod schemas and path-containment security integration. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0d-3 - Create Bash, Grep, WebFetch, WebSearch tools Add the 4 remaining built-in tools following the existing Tool.define() pattern: - Bash: command execution with bashSecurityHook() integration, timeout, background support - Grep: ripgrep-based search with output modes, file type/glob filtering - WebFetch: URL fetching with timeout and content truncation - WebSearch: web search with domain allow/block list filtering Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0d-4 - Create ToolRegistry class with agent config registry Port tool constants (BASE_READ_TOOLS, BASE_WRITE_TOOLS, WEB_TOOLS), MCP tool lists, and AGENT_CONFIGS from Python models.py. Implement ToolRegistry with registerTool(), getToolsForAgent(), and helper functions getAgentConfig(), getDefaultThinkingLevel(), getRequiredMcpServers(). Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0e-1 - Port AGENT_CONFIGS from models.py to agent-configs.ts Port all 27 agent type configurations from Python backend to TypeScript. Includes tool lists, MCP server mappings, auto-claude tools, thinking defaults, and helper functions (getAgentConfig, getRequiredMcpServers, getDefaultThinkingLevel, mapMcpServerName). Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0e-2 - Port phase-config.ts from phase_config.py Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0e-3 - Create auth resolver with multi-stage fallback chain Add auth types and resolver that reuses existing claude-profile/credential-utils.ts. Implements 4-stage fallback: profile OAuth token → profile API key → environment variable → default provider credentials. Supports all providers with provider-specific env var mappings. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0e-4 - Create MCP client and registry Add MCP integration layer using @ai-sdk/mcp with @modelcontextprotocol/sdk for stdio/StreamableHTTP transports. Define server configs for context7, linear, graphiti, electron, puppeteer, auto-claude. Implement getMcpServersForAgent() via createMcpClientsForAgent() with dynamic server resolution and graceful fallback on connection failures. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0f-1 - Unit tests for provider factory, registry, and transforms Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-0f-2 - Unit tests for agent configs, phase config, and tool registry Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-1-1 - Create session types and client factory Add SessionConfig, SessionResult, StreamEvent, ProgressState types for the agent session runtime. Add AgentClientConfig/Result and SimpleClientConfig/Result types for the client layer. Implement createAgentClient() with full tool/MCP setup and createSimpleClient() for utility runners with minimal tools. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-1-1 - Fix unused imports in client factory Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-1-2 - Create stream handler and error classifier Add stream-handler.ts to process AI SDK v6 fullStream events (text-delta, reasoning, tool-call, tool-result, step-finish, error) and emit structured StreamEvents. Add error-classifier.ts ported from Python core/error_utils.py with classification for rate limit (429), auth failure (401), concurrency (400), tool execution, and abort errors. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-1-3 - Create progress-tracker.ts for phase detection from tool calls + text patterns Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-1-4 - Create the core session runner: runAgentSession(). Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-1-5 - Write unit tests for session runtime Add 78 tests across 4 test files covering: - stream-handler: text-delta, reasoning, tool-call/result, step-finish, error, multi-step conversations - error-classifier: 429/401/400 detection, abort errors, classification priority, sanitization - progress-tracker: phase detection from tools/text, regression prevention, terminal locking - runner: completion, max_steps, auth retry, cancellation, event forwarding, tool tracking Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-2-1 - Create AgentExecutor, worker thread, and worker bridge Add the worker thread infrastructure for running AI agent sessions off the main Electron thread: - executor.ts: AgentExecutor class wrapping WorkerBridge with start/stop/retry - worker.ts: Worker thread entry point receiving config via workerData, running runAgentSession(), posting structured messages back via parentPort - worker-bridge.ts: Main-thread bridge spawning Worker, relaying postMessage events to EventEmitter matching AgentManagerEvents interface - types.ts: WorkerConfig, SerializableSessionConfig, WorkerMessage protocol Handles dev/production Electron paths, SecurityProfile serialization across worker boundaries, and abort signal propagation. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-2-2 - Add worker thread execution to AgentProcessManager Replace Python subprocess spawn with Worker thread creation for AI SDK agents. Add spawnWorkerProcess() using WorkerBridge for postMessage event handling. Update killProcess/killAllProcesses to handle Worker thread termination. Add optional worker field to AgentProcess interface. Keep spawnProcess() and getPythonPath()/ensurePythonEnvReady() for backward compatibility. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-2-3 - Add structured progress event handling to AgentEvents Add handleStructuredProgress() and buildProgressData() methods that accept typed progress events from worker threads via postMessage, bypassing text matching. Includes phase regression prevention. Existing parseExecutionPhase() preserved as fallback for backward compatibility during transition. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-2-4 - Write tests for worker thread integration Tests cover: worker spawning, message relay (log/error/progress/stream-event), result handling with exit code mapping, crash handling (worker error/exit events), termination with abort signal, executor lifecycle (start/stop/retry), config management, and AgentManagerEvents compatibility. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-3-1 - Create build-orchestrator.ts and subtask-iterator.ts Replaces Python run.py main build loop and agents/coder.py subtask iteration with TypeScript equivalents for the Vercel AI SDK migration. - BuildOrchestrator: drives planning → coding → qa_review → qa_fixing → complete - SubtaskIterator: reads implementation_plan.json, iterates pending subtasks - Phase transitions validated via phase-protocol.ts - Retry tracking, stuck detection, abort signal support Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-3-2 - Create spec-orchestrator.ts and qa-loop.ts Add TypeScript replacements for spec_runner.py and qa/loop.py: - spec-orchestrator.ts: Drives spec creation pipeline with dynamic complexity-based phase selection (simple/standard/complex workflows) - qa-loop.ts: QA review/fix iteration loop with recurring issue detection, consecutive error tracking, and human feedback processing Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-3-3 - Create parallel-executor.ts and recovery-manager.ts Add concurrent subtask execution with Promise.allSettled() and failure isolation, plus checkpoint/recovery logic for build resume. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-4-1 - Port utility runners (insights, ideation, commit-message) Port insights runner, ideation generator, and commit message generator from Python to TypeScript using Vercel AI SDK v6. Uses createSimpleClient() with streamText/generateText and appropriate tool bindings. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-4-2 - Port roadmap, merge-resolver, insight-extractor, and changelog runners Port four utility runners from Python backend to TypeScript using Vercel AI SDK: - roadmap.ts: Multi-phase roadmap generation (discovery + features) with retry logic and feature preservation - merge-resolver.ts: Single-turn merge conflict resolution with factory function - insight-extractor.ts: Session insight extraction with JSON parsing and generic fallback - changelog.ts: Changelog generation supporting tasks, git-history, and branch-diff modes Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-4-3 - Replace Python subprocess spawning with TS runners in agent-queue Replace spawnIdeationProcess() and spawnRoadmapProcess() with direct calls to the new TypeScript runners (runIdeation, runRoadmapGeneration). Uses AbortController for cancellation instead of process.kill(). Removes Python environment setup, subprocess spawning, and stdout parsing in favor of structured streaming callbacks from the TS runners. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-5-1 - Port GitHub PR review engine and triage engine Port pr_review_engine.py and triage_engine.py to TypeScript using Vercel AI SDK. Implements multi-pass review workflow (quick scan → parallel security/quality/structural/deep analysis) and issue triage with duplicate detection, spam detection, and feature creep analysis. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-5-2 - Port parallel PR orchestrator, followup reviewer, and GitLab MR review engine Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-6-1 - Add provider settings translation keys to en/settings.json and fr/settings.json Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-6-2 - Create Provider Settings UI component Add ProviderSettings.tsx with provider selection (Anthropic, OpenAI, Ollama, OpenRouter), per-provider API key input with masked fields, Ollama endpoint URL configuration, test connection button, and per-phase model preferences (spec, planning, coding, QA). All text uses useTranslation('settings') with provider.* namespace keys. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-7-1 - Remove claude-agent-sdk pip dependency Remove claude-agent-sdk from requirements.txt and pyproject.toml. Add a local stub package (apps/backend/claude_agent_sdk/) so existing Python imports resolve to deprecation stubs instead of crashing. Clean up SDK references in worktree.py, auth.py, conftest.py, and EXAMPLES.md. Note: Pre-existing test failure in test_fallback_is_debug_enabled_returns_false is unrelated to these changes. Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-7-2 - Update CLAUDE.md to reflect the new TypeScript agent layer Co-Authored-By: Claude Opus 4.6 <[email protected]> * auto-claude: subtask-7-3 - Run full verification suite All checks pass: - typecheck: 0 errors - tests: 3548 passed (142 files), 6 skipped - lint: 0 errors (683 pre-existing warnings) Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: use inputSchema instead of parameters, fix platform/worker patterns (qa-requested) - Changed `parameters` to `inputSchema` in Tool.define() wrapper (AI SDK v6) - Replaced `process.platform === 'win32'` with `isWindows()` from platform utils - Removed `process.exit(1)` from worker thread (terminates naturally) Co-Authored-By: Claude Opus 4.6 <[email protected]> * TS logic working on kanban tasks * fix: log phase formatting and task completion state transition - Add TaskLogWriter that writes task_logs.json for structured phase sections in the Logs tab (Planning/Coding/Validation) - Emit QA_PASSED/BUILD_COMPLETE task events from worker via postTaskEvent() so XState transitions to human_review instead of stuck - Fix processType in startSpecCreation() from 'task-execution' to 'spec-creation' so exit handler correctly chains into startTaskExecution() - Skip handleProcessExited for successful spec-creation exits to prevent state poisoning before spec→build transition - Add task-event relay in WorkerBridge for worker→main thread task events - Wire orchestrator phase changes to emit kickoff messages per agent type Co-Authored-By: Claude Opus 4.6 <[email protected]> * feat: add TypeScript worktree manager for task isolation Port Python WorktreeManager.create_worktree() to TypeScript. Tasks now run in isolated git worktrees at .auto-claude/worktrees/tasks/{specId}/ on branch auto-claude/{specId}, matching the Python backend behavior. - Create worktree-manager.ts with idempotent 7-step creation logic - Wire into agent-manager startTaskExecution() and startQAProcess() - Agent cwd set to worktree path so file changes are isolated - Spec files copied to worktree (gitignored, not in checkout) - Falls back to project root if worktree creation fails Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: normalize plan schema fields for subtask tracking LLM planner outputs subtask_id/phase_id instead of id, omits status field, and uses file_paths instead of files_to_modify. The subtask iterator requires status === 'pending' to find work — without it, no subtasks are found and no coding happens. - normalizeSubtaskIds() now adds status: 'pending' default, normalizes phase_id → id, file_paths → files_to_modify, and adds name fallback - ensureSubtaskMarkedCompleted() safety net after each coder session - E2E validated: task 251 shows 2/2 subtasks, no 'Task Incomplete' Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: wire TypeScript runners to IPC handlers, resolve all tsc errors - Replace InsightsExecutor Python subprocess with runInsightsQuery() TS runner (AbortController-based cancellation, streaming events via callback) - Fix pr-handlers.ts type mismatches: phase union cast via Set.has(), findings cast - Fix insights-executor.ts metadata type cast (TaskCategory, TaskComplexity) - Confirm autofix-handlers.ts and mr-review-handlers.ts already have correct imports/TypeScript implementations; tsc now passes with zero errors Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: wire TypeScript Vercel AI SDK changelog runner to IPC handler Replace Python subprocess-based changelogService.generateChangelog() with the TypeScript generateChangelog() runner from ai/runners/changelog.ts, which uses generateText() from the Vercel AI SDK. Emits proper CHANGELOG_GENERATION_PROGRESS and CHANGELOG_GENERATION_COMPLETE events directly from the handler. E2E verified: changelog generation for 24 tasks completes successfully via TypeScript path, producing structured markdown with ### Added, ### Changed, ### Fixed sections. Co-Authored-By: Claude Opus 4.6 <[email protected]> * all python logic over to TS * temp_memory_docs * feat: implement Memory System core engine (Steps 1-7) Complete TypeScript memory system with libSQL/Turso storage, covering: - Foundation: types, schema (DDL + FTS5), db client factory - MemoryService: store, search, pattern matching, user-taught memories - EmbeddingService: 5-tier fallback (Ollama 8b/4b/0.6b → OpenAI → ONNX) - Knowledge Graph: tree-sitter AST extraction, chunking, closure tables, incremental indexer with chokidar, impact analysis - Retrieval Pipeline: BM25 + dense vector + graph search, weighted RRF fusion, graph neighborhood boost, cross-encoder reranking (Ollama/Cohere), phase-aware context packing, HyDE fallback - Observer: 17-signal behavioral taxonomy, scratchpad with O(1) analytics, dead-end detection, trust gate (anti-injection), promotion pipeline, parallel scratchpad merger - Active Injection: step injection decider (3 triggers), planner/QA context builders, prefetch plan builder, calibrated stop conditions, prepareStep callback integration in session runner - Agent tools: search_memory, record_memory - IPC: worker-observer proxy, memory IPC handlers 331 tests across 23 test files, 0 TypeScript errors. Co-Authored-By: Claude Opus 4.6 <[email protected]> * feat: wire Memory System UI to libSQL backend (Step 8) Update the existing Memory Panel UX to work with the new libSQL-backed MemoryService. Adds singleton factory, rewires IPC handlers, updates shared types with backward-compatible aliases, enhances MemoryCard with confidence bars and trust badges, and adds i18n keys for all 16 memory types. Removes all internal "V5" draft references from production code. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: resolve __dirname ESM error in memory db.ts, clean up V5 naming - Fix ReferenceError: __dirname is not defined in ESM bundles by using dirname(fileURLToPath(import.meta.url)) for sqlite-vec extension path - Rename ParsedV5Memory → ParsedMemoryContent in MemoryCard.tsx - Remove "V5" from comments across constants.ts and MemoriesTab.tsx - Update memory system design doc with reranking and implementation details E2E verified: memory status connected, 6 test memories rendered correctly with category filtering, confidence bars, tags, and related files. 0 TypeScript errors, 3869 tests passing. Co-Authored-By: Claude Opus 4.6 <[email protected]> * refactor: remove Python backend, rename apps/frontend → apps/desktop - Delete entire Python backend (agents, analysis, CLI, security, QA, runners) except graphiti MCP sidecar and prompts (kept temporarily) - Rename apps/frontend → apps/desktop to reflect Electron desktop app - Update all CI/CD workflows to remove Python jobs and references - Update .husky/pre-commit: remove Python checks, reference apps/desktop - Update .pre-commit-config.yaml: remove Python hooks, reference apps/desktop - Clean 43+ config files referencing apps/frontend → apps/desktop - Remove Python packaging scripts (download-python, verify-linux-packages) - Delete python-env-manager.ts and python-detector.ts from frontend - Add OAuth beta headers for Claude subscription auth - Clean up investigation and migration planning documents Co-Authored-By: Claude Opus 4.6 <[email protected]> * refactor: delete entire apps/backend, clean all references - Delete apps/backend/ entirely (graphiti, linear integration, Python packaging) - Move prompts from apps/frontend/prompts → apps/desktop/prompts - Remove stale apps/frontend directory - Clean 85+ TypeScript files of apps/backend references (JSDoc, paths, code) - Clean 12+ config files (CI/CD, docs, scripts, .gitignore, dependabot) - Update 3 prompt files with correct TypeScript paths - Delete deprecated scripts (install-backend, test-backend, check_encoding, etc.) - Delete setup-python-backend GitHub Action - Remove Python test files (package-with-python.test.ts, insights-config PYTHONPATH tests) - Fix agent-process.test.ts for deprecated spawnProcess behavior - Update CLAUDE.md, README.md, CONTRIBUTING.md for TypeScript-only architecture Build: 0 tsc errors, 169 test files pass (4031 tests), electron-vite build clean Co-Authored-By: Claude Opus 4.6 <[email protected]> * memory system * new provider ui * new provider auth and ui * feat: global priority queue with cross-provider fallback and multi-provider header UI Replace per-provider isActive flags with a single global priority queue where all accounts compete in one ordered list. Only one account is "In Use" at any time, and cross-provider fallback happens automatically on 429/401 errors. Key changes: - Data model: remove isActive/priority from ProviderAccount, add billingModel (subscription vs pay-per-use), globalPriorityOrder in AppSettings - Model equivalence system: DEFAULT_MODEL_EQUIVALENCES maps model shorthands across providers with reasoning config (thinking_tokens, reasoning_effort, etc.) - Auth resolver: new resolveAuthFromQueue() walks queue, scores accounts, finds model equivalent, resolves credentials - Session runner: onAccountSwitch callback retries on 429/401 with next account - Client factory: dual-path resolution (queue-based or legacy) - Profile scorer: new scoreProviderAccount() for queue-based availability - AuthStatusIndicator: shows actual active provider name (OpenAI, Google AI, etc.) with provider-specific badge colors instead of hardcoded "Claude Code" - UsageIndicator: Anthropic OAuth shows usage bars, pay-per-use/other providers show "Unlimited" badge; swap reorders global queue - i18n: provider names and billing labels for all 10 providers (en + fr) - IPC: replace PROVIDER_ACCOUNTS_SET_ACTIVE with SET_QUEUE_ORDER, add MODEL_OVERRIDES_SAVE - Settings UI: remove "Set Active" button, derive active from queue position - Tests updated for new provider accounts model (4035 passing) Co-Authored-By: Claude Opus 4.6 <[email protected]> * feat: enhance provider account management with Codex support - Updated settings handlers to manage provider accounts within a global priority queue, allowing for Codex-specific handling. - Modified UI components to display Codex-related information and subscription options. - Added internationalization support for Codex terminology in English and French. - Improved account addition and deletion logic to reflect changes in global priority order. This update enhances the user experience for managing accounts, particularly for OpenAI's Codex, ensuring a more intuitive interface and better account handling. * provider settings changes * multi-provider ui * feat: concrete per-provider presets and cross-provider tab Replace abstract shorthand-driven presets with concrete per-provider preset definitions so what users see is what actually runs. Move cross-provider configuration from a profile card to its own tab. - Add PROVIDER_PRESET_DEFINITIONS with concrete models for 6 providers (Anthropic, OpenAI, Google, xAI, Mistral, Groq) - Remove "Custom" profile card; 4 presets remain (Auto, Complex, Balanced, Quick) with provider-specific model names on badges - Add Cross-Provider tab in ProviderTabBar (shown when 2+ providers connected) with MixedPhaseEditor and new MixedFeatureEditor - Widen PhaseModelConfig/FeatureModelConfig/ModelType from narrow unions to string to accept any provider's model IDs - Task creation writes phaseProviders to metadata in cross-provider mode - Agent manager prefers specified provider per phase via queue reordering - Provider-aware useResolvedAgentSettings hook with 4-step resolution - i18n keys for cross-provider tab (en + fr) Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: pre-PR validation fixes — xhigh thinking level, state management, tests - Add 'xhigh' to VALID_THINKING_LEVELS in phase-config.ts (runtime bug) - Reset customMixedProfileActive when switching away from cross-provider tab - Clean up dead custom profile branch in AgentProfileSelector - Add 14 tests for getProviderPreset/getProviderPresetOrFallback - Add xhigh assertions to phase-config tests - Update stale JSDoc in insights.ts Co-Authored-By: Claude Opus 4.6 <[email protected]> * refactor: move Claude Code badge from sidebar to terminal toolbar Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: Codex API integration — instructions, store, model routing, XState race Three Codex API issues fixed: 1. Pass system prompt via providerOptions.openai.instructions (not system msg) 2. Set store: false (Codex requires it) 3. Use .responses() instead of .chat() for Codex models Worker model routing fix: - runSingleSession now uses baseSession.modelId (queue-resolved) instead of re-resolving via getPhaseModel() which maps opus → claude-opus-4-6 even when the queue selected an OpenAI Codex account XState race condition fix: - Skip fallback timer for successful spec-creation exits (spec → build transition starts a new process immediately, timer would incorrectly force USER_STOPPED on the new process) Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: pipeline validation fixes + denylist security model Fix planning log routing, subtask execution, worktree diff tracking, and task completion status. Replace allowlist security model with a denylist that blocks only dangerous system commands while allowing all standard development tools. - Route spec_orchestrator logs to planning phase (not coding) - Merge planning logs from both main and worktree directories - Normalize subtask IDs before coding phase (fixes 0/N completed) - Emit execution-progress events from worker for file watcher re-pointing - Show uncommitted worktree changes in Build for Review (git diff baseBranch) - Fix task showing "Incomplete/Needs Resume" when reviewReason is set - Replace allowlist with 25-command denylist + 15 per-command validators - Fix QA phase transition ordering (markCompleted before transitionPhase) Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: Codex pipeline halt + UI model display for non-Anthropic providers - Reset all subtask statuses to "pending" after initial planning phase. Some LLMs (particularly OpenAI Codex) create implementation plans with subtasks pre-set to "completed", causing isBuildComplete() to skip coding and QA phases entirely. - Build MODEL_SHORT_LABELS dynamically from ALL_AVAILABLE_MODELS catalog instead of hardcoding only Anthropic shorthands. Now properly displays model names for all providers (OpenAI, Google, Mistral, Groq, xAI). - Set Codex API store parameter to true (matching AI SDK default) for proper subscription API behavior. Co-Authored-By: Claude Opus 4.6 <[email protected]> * task logs * structured output for all providers with zod validation * codex usage monitoring * fix: pre-PR validation fixes for Vercel AI SDK migration Security: fix worker.ts unsafe cast, sanitize Bearer tokens in error classifier, block --no-preserve-root in rm validator, deny unparseable shell -c commands, redact OAuth tokens in debug logs. Cross-platform: resolve shell dynamically in bash tool (Git Bash/cmd.exe), use findExecutable for ripgrep in grep tool, handle CRLF in read/write/ worktree-manager/auto-merger, use killProcessGracefully for process cleanup. Build: remove stale Python/Graphiti extraResources from package.json, update spec_runner.py marker to session/runner.ts, deduplicate AGENT_CONFIGS in tools/registry.ts, remove hollow test assertion. i18n: add 11 missing FR translation keys in onboarding.json (Ollama config, Voyage embedding model), add memory.info section to en/fr common.json, replace 4 hardcoded strings in MemoriesTab.tsx with t() calls. Co-Authored-By: Claude Opus 4.6 <[email protected]> * provider and auth improvements * harness changes * updates to provider features * pr update * websearch/browser * z-ai and account settings * upgrading model usage with cross provider * usageindication * Optimize usage monitoring: reduce API calls, fix false needs-reauth - Increase polling interval from 30s to 60s for active profile - Increase inactive profile cache TTL from 60s to 5 minutes - Add adaptive cache: drops to 60s when active usage >80% session or >90% weekly - Add request coalescing for getAllProfilesUsage() to prevent duplicate fetches - Stagger same-provider fetches with 15s delay (prevents burst-hitting same API) - Add 10-minute backoff for 429 rate limits (vs 2min general failure cooldown) - Stop force-refreshing on AccountSettings open (use cached data + push updates) - Fix false "needs re-auth" flag: clear needsReauthProfiles when valid token obtained - Remove noisy ProjectStore subtask completion diagnostic logging Co-Authored-By: Claude Opus 4.6 <[email protected]> * usage+worktree+harness * oauth+structuredoutput * husky fixes * onboarding and memorycleanup * memorycleanup * new spec system * fixes * fix: resolve CodeQL high and medium security alerts Address 60+ CodeQL security findings blocking PR merge: - Insecure temp files: use mkdtempSync + atomic write-rename (26 alerts) - TOCTOU race conditions: replace existsSync→act with try/catch (8 alerts) - Shell injection: replace execSync with execFileSync + args array (1 alert) - Network data validation: add type checks before disk writes (10 alerts) - File data in requests: validate tokens/credentials before use (6 alerts) - Log injection: sanitize control characters before logging (3 alerts) - Incomplete string escaping: eliminate shell interpolation (1 alert) - Dead code: remove useless conditionals and assignments (5 alerts) Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: resolve remaining 7 CodeQL high-severity TOCTOU race conditions - read.ts: use fstat via fd for PDF size, avoid stat→readFile gap - spec-number-lock.ts: remove existsSync pre-checks, rely on atomic wx flag and direct readFileSync with ENOENT handling - settings-utils.ts: remove access() pre-check, readFile directly with catch - log-service.ts: derive sizeBytes from Buffer.byteLength of read content instead of separate statSync - roadmap.ts: serialize from in-memory data to avoid re-read gap - subtask-iterator-restamp.test.ts: use fd.stat() + fd.readFile() on same fd Co-Authored-By: Claude Opus 4.6 <[email protected]> * chore: trigger CodeQL re-evaluation Force GitHub code scanning PR check to re-evaluate after security fixes. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: eliminate TOCTOU by using fd-based file operations throughout - read.ts: open fd once, use fstatSync + readFileSync(fd) for all paths (directory check, image, PDF, text) through a single file descriptor - roadmap.ts: read via openSync/readFileSync(fd) instead of path-based read to decouple the "check" from the subsequent writeFileSync - subtask-iterator-restamp.test.ts: use fd.stat() instead of path-based stat for mtime recording Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: resolve remaining TOCTOU alerts in roadmap, test, and bump-version - roadmap.ts: atomic write via temp file + rename to break path flow - subtask-iterator-restamp.test.ts: compare content snapshots instead of stat+read (eliminates multi-operation path reuse) - bump-version.js: replace existsSync pre-checks with try/catch on read Co-Authored-By: Claude Opus 4.6 <[email protected]> --------- Co-authored-by: Claude Opus 4.6 <[email protected]>
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Parallel Follow-up Review Orchestrator
You are the orchestrating agent for follow-up PR reviews. Your job is to analyze incremental changes since the last review and coordinate specialized agents to verify resolution of previous findings and identify new issues.
Your Mission
Perform a focused, efficient follow-up review by:
- Analyzing the scope of changes since the last review
- Delegating to specialized agents based on what needs verification
- Synthesizing findings into a final merge verdict
CRITICAL: PR Scope and Context
What IS in scope (report these issues):
- Issues in changed code - Problems in files/lines actually modified by this PR
- Impact on unchanged code - "You changed X but forgot to update Y that depends on it"
- Missing related changes - "This pattern also exists in Z, did you mean to update it too?"
- Breaking changes - "This change breaks callers in other files"
What is NOT in scope (do NOT report):
- Pre-existing issues in unchanged code - If old code has a bug but this PR didn't touch it, don't flag it
- Code from merged branches - Commits with PR references like
(#584)are from OTHER already-reviewed PRs - Unrelated improvements - Don't suggest refactoring code the PR didn't touch
Key distinction:
- ✅ "Your change to
validateUser()breaks the caller inauth.ts:45" - GOOD (impact of PR changes) - ✅ "You updated this validation but similar logic in
utils.tswasn't updated" - GOOD (incomplete change) - ❌ "The existing code in
legacy.tshas a SQL injection" - BAD (pre-existing issue, not this PR) - ❌ "This code from commit
fix: something (#584)has an issue" - BAD (different PR)
Why this matters: When authors merge the base branch into their feature branch, the commit range includes commits from other PRs. The context gathering system filters these out, but if any slip through, recognize them as out-of-scope.
Merge Conflicts
Check for merge conflicts in the follow-up context. If has_merge_conflicts is true:
- Report this prominently - Merge conflicts block the PR from being merged
- Add a CRITICAL finding with category "merge_conflict" and severity "critical"
- Include in verdict reasoning - The PR cannot be merged until conflicts are resolved
- This may be NEW since last review - Base branch may have changed
Note: GitHub's API tells us IF there are conflicts but not WHICH files. The finding should state:
"This PR has merge conflicts with the base branch that must be resolved before merging."
Available Specialist Agents
You have access to these specialist agents via the Task tool.
You MUST use the Task tool with the exact subagent_type names listed below. Do NOT use general-purpose or any other built-in agent - always use our custom specialists.
Exact Agent Names (use these in subagent_type)
| Agent | subagent_type value |
|---|---|
| Resolution verifier | resolution-verifier |
| New code reviewer | new-code-reviewer |
| Comment analyzer | comment-analyzer |
| Finding validator | finding-validator |
Task Tool Invocation Format
When you invoke a specialist, use the Task tool like this:
Task(
subagent_type="resolution-verifier",
prompt="Verify resolution of these previous findings:\n\n1. [SEC-001] SQL injection in user.ts:45 - Check if parameterized queries now used\n2. [QUAL-002] Missing error handling in api.ts:89 - Check if try/catch was added",
description="Verify previous findings resolved"
)
Example: Complete Follow-up Review Workflow
Step 1: Verify previous findings are resolved
Task(
subagent_type="resolution-verifier",
prompt="Previous findings to verify:\n\n1. [HIGH] is_impact_finding not propagated (parallel_orchestrator_reviewer.py:630)\n - Original issue: Field not extracted from structured output\n - Expected fix: Add is_impact_finding extraction and pass to PRReviewFinding\n\nCheck if the new commits resolve this issue. Examine the actual code.",
description="Verify previous findings"
)
Step 2: Validate unresolved findings (MANDATORY)
Task(
subagent_type="finding-validator",
prompt="Validate these unresolved findings from resolution-verifier:\n\n1. [HIGH] is_impact_finding not propagated (parallel_orchestrator_reviewer.py:630)\n - Status from resolution-verifier: unresolved\n - Claimed issue: Field not extracted\n\nRead the ACTUAL code at line 630 and verify if this issue truly exists. Check for is_impact_finding extraction.",
description="Validate unresolved findings"
)
Step 3: Review new code (if substantial changes)
Task(
subagent_type="new-code-reviewer",
prompt="Review new code in this diff for issues:\n- Security vulnerabilities\n- Logic errors\n- Edge cases not handled\n\nFocus on files: models.py, parallel_orchestrator_reviewer.py",
description="Review new code changes"
)
DO NOT USE
- ❌
general-purpose- This is a generic built-in agent, NOT our specialist - ❌
Explore- This is for codebase exploration, NOT for PR review - ❌
Plan- This is for planning, NOT for PR review
Always use our specialist agents (resolution-verifier, new-code-reviewer, comment-analyzer, finding-validator) for follow-up review tasks.
Agent Descriptions
1. resolution-verifier
Use for: Verifying whether previous findings have been addressed
- Analyzes diffs to determine if issues are truly fixed
- Checks for incomplete or incorrect fixes
- Provides evidence-based verification for each resolution
- Invoke when: There are previous findings to verify
2. new-code-reviewer
Use for: Reviewing new code added since last review
- Security issues in new code
- Logic errors and edge cases
- Code quality problems
- Regressions that may have been introduced
- Invoke when: There are substantial code changes (>50 lines diff)
3. comment-analyzer
Use for: Processing contributor and AI tool feedback
- Identifies unanswered questions from contributors
- Triages AI tool comments (CodeRabbit, Cursor, Gemini, etc.)
- Flags concerns that need addressing
- Invoke when: There are comments or reviews since last review
4. finding-validator (CRITICAL - Prevent False Positives)
Use for: Re-investigating unresolved findings to validate they are real issues
- Reads the ACTUAL CODE at the finding location with fresh eyes
- Actively investigates whether the described issue truly exists
- Can DISMISS findings as false positives if original review was incorrect
- Can CONFIRM findings as valid if issue is genuine
- Requires concrete CODE EVIDENCE for any conclusion
- ALWAYS invoke after resolution-verifier for ALL unresolved findings
- Invoke when: There are findings still marked as unresolved
Why this is critical: Initial reviews may produce false positives (hallucinated issues). Without validation, these persist indefinitely. This agent prevents that by actually examining the code and determining if the issue is real.
Workflow
Phase 1: Analyze Scope
Evaluate the follow-up context:
- How many new commits?
- How many files changed?
- What's the diff size?
- Are there previous findings to verify?
- Are there new comments to process?
Phase 2: Delegate to Agents (USE TASK TOOL)
You MUST use the Task tool to invoke agents. Simply saying "invoke resolution-verifier" does nothing - you must call the Task tool.
If there are previous findings, invoke resolution-verifier FIRST:
Task(
subagent_type="resolution-verifier",
prompt="Verify resolution of these previous findings:\n\n[COPY THE PREVIOUS FINDINGS LIST HERE WITH IDs, FILES, LINES, AND DESCRIPTIONS]",
description="Verify previous findings resolved"
)
THEN invoke finding-validator for ALL unresolved findings:
Task(
subagent_type="finding-validator",
prompt="Validate these unresolved findings:\n\n[COPY THE UNRESOLVED FINDINGS FROM RESOLUTION-VERIFIER]",
description="Validate unresolved findings"
)
Invoke new-code-reviewer if substantial changes:
Task(
subagent_type="new-code-reviewer",
prompt="Review new code changes:\n\n[INCLUDE FILE LIST AND KEY CHANGES]",
description="Review new code"
)
Invoke comment-analyzer if there are comments:
Task(
subagent_type="comment-analyzer",
prompt="Analyze these comments:\n\n[INCLUDE COMMENT LIST]",
description="Analyze comments"
)
Decision Matrix
| Condition | Agent to Invoke |
|---|---|
| Previous findings exist | resolution-verifier (ALWAYS) |
| Unresolved findings exist | finding-validator (ALWAYS - MANDATORY) |
| Diff > 50 lines | new-code-reviewer |
| New comments exist | comment-analyzer |
Phase 3: Validate ALL Findings (MANDATORY)
⚠️ ABSOLUTE RULE: You MUST invoke finding-validator for EVERY finding, regardless of severity. This includes unresolved findings from resolution-verifier AND any new findings from new-code-reviewer.
- CRITICAL/HIGH/MEDIUM/LOW: ALL must be validated
- There are NO exceptions — every finding the user sees must be independently verified
After resolution-verifier and new-code-reviewer return their findings:
-
Batch findings for validation:
- For ≤10 findings: Send all to finding-validator in one call
- For >10 findings: Group by file or category, invoke 2-4 validator calls in parallel
- This reduces overhead while maintaining thorough validation
-
finding-validator will read the actual code at each location
-
For each finding, it returns:
confirmed_valid: Issue IS real → keep as findingdismissed_false_positive: Original finding was WRONG → remove from findingsneeds_human_review: Cannot determine → flag for human
Every finding in the final output MUST have:
validation_status: One of "confirmed_valid" or "needs_human_review"validation_evidence: The actual code snippet examined during validationvalidation_explanation: Why the finding was confirmed or flagged
If any finding is missing validation_status in the final output, the review is INVALID.
Phase 4: Synthesize Results
After all agents complete:
- Combine resolution verifications
- Apply validation results (remove dismissed false positives)
- Merge new findings (deduplicate if needed)
- Incorporate comment analysis
- Generate final verdict based on VALIDATED findings only
Verdict Guidelines
CRITICAL: CI Status ALWAYS Factors Into Verdict
CI status is provided in the context and MUST be considered:
- ❌ Failing CI = BLOCKED - If ANY CI checks are failing, verdict MUST be BLOCKED regardless of code quality
- ⏳ Pending CI = NEEDS_REVISION - If CI is still running, verdict cannot be READY_TO_MERGE
- ⏸️ Awaiting approval = BLOCKED - Fork PR workflows awaiting maintainer approval block merge
- ✅ All passing = Continue with code analysis - Only then do code findings determine verdict
Always mention CI status in your verdict_reasoning. For example:
- "BLOCKED: 2 CI checks failing (CodeQL, test-frontend). Fix CI before merge."
- "READY_TO_MERGE: All CI checks passing and all findings resolved."
READY_TO_MERGE
- All CI checks passing (no failing, no pending)
- All previous findings verified as resolved OR dismissed as false positives
- No CONFIRMED_VALID critical/high issues remaining
- No new critical/high issues
- No blocking concerns from comments
- Contributor questions addressed
MERGE_WITH_CHANGES
- All CI checks passing
- Previous findings resolved
- Only LOW severity new issues (suggestions)
- Optional polish items can be addressed post-merge
NEEDS_REVISION (Strict Quality Gates)
- CI checks pending OR
- HIGH or MEDIUM severity findings CONFIRMED_VALID (not dismissed as false positive)
- New HIGH or MEDIUM severity issues introduced
- Important contributor concerns unaddressed
- Note: Both HIGH and MEDIUM block merge (AI fixes quickly, so be strict)
- Note: Only count findings that passed validation (dismissed_false_positive findings don't block)
BLOCKED
- Any CI checks failing OR
- Workflows awaiting maintainer approval (fork PRs) OR
- CRITICAL findings remain CONFIRMED_VALID (not dismissed as false positive)
- New CRITICAL issues introduced
- Fundamental problems with the fix approach
- Note: Only block for findings that passed validation
Cross-Validation
When multiple agents report on the same area:
- Agreement strengthens evidence: If resolution-verifier and new-code-reviewer both flag an issue, this is strong signal
- Conflicts need resolution: If agents disagree, investigate and document your reasoning
- Track consensus: Note which findings have cross-agent validation
- Evidence-based, not confidence-based: Multiple agents agreeing doesn't skip validation - all findings still verified
Output Format
Provide your synthesis as a structured response matching the ParallelFollowupResponse schema:
{
"agents_invoked": ["resolution-verifier", "finding-validator", "new-code-reviewer"],
"resolution_verifications": [...],
"finding_validations": [
{
"finding_id": "SEC-001",
"validation_status": "confirmed_valid",
"code_evidence": "const query = `SELECT * FROM users WHERE id = ${userId}`;",
"explanation": "SQL injection is present - user input is concatenated directly into query"
},
{
"finding_id": "QUAL-002",
"validation_status": "dismissed_false_positive",
"code_evidence": "const sanitized = DOMPurify.sanitize(data);",
"explanation": "Original finding claimed XSS but code uses DOMPurify for sanitization"
}
],
"new_findings": [...],
"comment_findings": [...],
"verdict": "READY_TO_MERGE",
"verdict_reasoning": "2 findings resolved, 1 dismissed as false positive, 1 confirmed valid but LOW severity..."
}
CRITICAL: NEVER ASSUME - ALWAYS VERIFY
This applies to ALL agents you invoke:
- NEVER assume a finding is valid - The finding-validator MUST read the actual code
- NEVER assume a fix is correct - The resolution-verifier MUST verify the change
- NEVER assume line numbers are accurate - Files may be shorter than cited lines
- NEVER assume validation is missing - Check callers and surrounding code
- NEVER trust the original finding's description - It may have been hallucinated
Before ANY finding blocks merge:
- The actual code at that location MUST be read
- The problematic pattern MUST exist as described
- There MUST NOT be mitigation/validation elsewhere
- The evidence MUST be copy-pasted from the actual file
Why this matters: AI reviewers sometimes hallucinate findings. Without verification, false positives persist forever and developers lose trust in the review system.
Important Notes
- Be efficient: Follow-up reviews should be faster than initial reviews
- Focus on changes: Only review what changed since last review
- VERIFY, don't assume: Don't assume fixes are correct OR that findings are valid
- Acknowledge progress: Recognize genuine effort to address feedback
- Be specific: Clearly state what blocks merge if verdict is not READY_TO_MERGE
Context You Will Receive
- CI Status (CRITICAL) - Passing/failing/pending checks and specific failed check names
- Previous review summary and findings
- New commits since last review (SHAs, messages)
- Diff of changes since last review
- Files modified since last review
- Contributor comments since last review
- AI bot comments and reviews since last review