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Add unit tests for various components and stores in the terminal dashboard
- Implement tests for AssociationHighlight, DashboardToolbar, QueuePanel, SessionGroupTree, and TerminalDashboardPage to ensure proper functionality and state management. - Create tests for cliSessionStore, issueQueueIntegrationStore, queueExecutionStore, queueSchedulerStore, sessionManagerStore, and terminalGridStore to validate state resets and workspace scoping. - Mock necessary dependencies and state management hooks to isolate tests and ensure accurate behavior.
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catlog22
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.codex/skills/team-perf-opt/instructions/agent-instruction.md
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.codex/skills/team-perf-opt/instructions/agent-instruction.md
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## TASK ASSIGNMENT
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### MANDATORY FIRST STEPS
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1. Read shared discoveries: {session_folder}/discoveries.ndjson (if exists, skip if not)
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2. Read project context: .workflow/project-tech.json (if exists)
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3. Read task schema: .codex/skills/team-perf-opt/schemas/tasks-schema.md
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---
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## Your Task
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**Task ID**: {id}
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**Title**: {title}
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**Description**: {description}
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**Role**: {role}
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**Bottleneck Type**: {bottleneck_type}
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**Priority**: {priority}
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**Target Files**: {target_files}
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### Previous Tasks' Findings (Context)
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{prev_context}
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---
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## Execution Protocol
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1. **Read discoveries**: Load {session_folder}/discoveries.ndjson for shared exploration findings
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2. **Use context**: Apply previous tasks' findings from prev_context above
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3. **Execute by role**:
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**If role = profiler**:
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- Detect project type by scanning for framework markers:
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- Frontend (React/Vue/Angular): render time, bundle size, FCP/LCP/CLS
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- Backend Node (Express/Fastify/NestJS): CPU hotspots, memory, DB queries
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- Native/JVM Backend (Cargo/Go/Java): CPU, memory, GC tuning
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- CLI Tool: startup time, throughput, memory peak
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- Trace hot code paths and CPU hotspots within target scope
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- Identify memory allocation patterns and potential leaks
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- Measure I/O and network latency where applicable
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- Collect quantified baseline metrics (timing, memory, throughput)
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- Rank top 3-5 bottlenecks by severity (Critical/High/Medium)
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- Record evidence: file paths, line numbers, measured values
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- Write `{session_folder}/artifacts/baseline-metrics.json` (metrics)
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- Write `{session_folder}/artifacts/bottleneck-report.md` (ranked bottlenecks)
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**If role = strategist**:
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- Read bottleneck report and baseline from {session_folder}/artifacts/
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- For each bottleneck, select optimization strategy by type:
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- CPU: algorithm optimization, memoization, caching, worker threads
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- MEMORY: pool reuse, lazy init, WeakRef, scope cleanup
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- IO: batching, async pipelines, streaming, connection pooling
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- NETWORK: request coalescing, compression, CDN, prefetching
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- RENDERING: virtualization, memoization, CSS containment, code splitting
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- DATABASE: index optimization, query rewriting, caching layer
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- Prioritize by impact/effort: P0 (high impact+low effort) to P3
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- Assign unique OPT-IDs (OPT-001, 002, ...) with non-overlapping file targets
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- Define measurable success criteria (target metric value or improvement %)
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- Write `{session_folder}/artifacts/optimization-plan.md`
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**If role = optimizer**:
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- Read optimization plan from {session_folder}/artifacts/optimization-plan.md
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- Apply optimizations in priority order (P0 first)
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- Preserve existing behavior -- optimization must not break functionality
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- Make minimal, focused changes per optimization
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- Add comments only where optimization logic is non-obvious
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- Preserve existing code style and conventions
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**If role = benchmarker**:
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- Read baseline from {session_folder}/artifacts/baseline-metrics.json
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- Read plan from {session_folder}/artifacts/optimization-plan.md
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- Run benchmarks matching detected project type:
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- Frontend: bundle size, render performance
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- Backend: endpoint response times, memory under load, DB query times
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- CLI: execution time, memory peak, throughput
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- Run test suite to verify no regressions
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- Collect post-optimization metrics matching baseline format
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- Calculate improvement percentages per metric
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- Compare against plan success criteria
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- Write `{session_folder}/artifacts/benchmark-results.json`
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- Set verdict: PASS (meets criteria) / WARN (partial) / FAIL (regression or criteria not met)
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**If role = reviewer**:
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- Read plan from {session_folder}/artifacts/optimization-plan.md
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- Review changed files across 5 dimensions:
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- Correctness: logic errors, race conditions, null safety
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- Side effects: unintended behavior changes, API contract breaks
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- Maintainability: code clarity, complexity increase, naming
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- Regression risk: impact on unrelated code paths
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- Best practices: idiomatic patterns, no optimization anti-patterns
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- Write `{session_folder}/artifacts/review-report.md`
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- Set verdict: APPROVE / REVISE / REJECT
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4. **Share discoveries**: Append exploration findings to shared board:
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```bash
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echo '{"ts":"<ISO8601>","worker":"{id}","type":"<type>","data":{...}}' >> {session_folder}/discoveries.ndjson
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```
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5. **Report result**: Return JSON via report_agent_job_result
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### Discovery Types to Share
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- `bottleneck_found`: `{type, location, severity, description}` -- Bottleneck identified
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- `hotspot_found`: `{file, function, cpu_pct, description}` -- CPU hotspot
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- `memory_issue`: `{file, type, size_mb, description}` -- Memory problem
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- `io_issue`: `{operation, latency_ms, description}` -- I/O issue
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- `db_issue`: `{query, latency_ms, description}` -- Database issue
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- `file_modified`: `{file, change, lines_added}` -- File change recorded
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- `metric_measured`: `{metric, value, unit, context}` -- Metric measured
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- `pattern_found`: `{pattern_name, location, description}` -- Pattern identified
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- `artifact_produced`: `{name, path, producer, type}` -- Deliverable created
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---
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## Output (report_agent_job_result)
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Return JSON:
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{
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"id": "{id}",
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"status": "completed" | "failed",
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"findings": "Key discoveries and implementation notes (max 500 chars)",
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"verdict": "PASS|WARN|FAIL|APPROVE|REVISE|REJECT or empty",
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"artifacts_produced": "semicolon-separated artifact paths",
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"error": ""
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}
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