mirror of https://github.com/catlog22/Claude-Code-Workflow.git synced 2026-03-25 19:48:33 +08:00

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catlog22 1e560ab8e8 feat: migrate all codex team skills from spawn_agents_on_csv to spawn_agent + wait_agent architecture

- Delete 21 old team skill directories using CSV-wave pipeline pattern (~100+ files)
- Delete old team-lifecycle (v3) and team-planex-v2
- Create generic team-worker.toml and team-supervisor.toml (replacing tlv4-specific TOMLs)
- Convert 19 team skills from Claude Code format (Agent/SendMessage/TaskCreate)
  to Codex format (spawn_agent/wait_agent/tasks.json/request_user_input)
- Update team-lifecycle-v4 to use generic agent types (team_worker/team_supervisor)
- Convert all coordinator role files: dispatch.md, monitor.md, role.md
- Convert all worker role files: remove run_in_background, fix Bash syntax
- Convert all specs/pipelines.md references
- Final state: 20 team skills, 217 .md files, zero Claude Code API residuals

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-24 16:54:48 +08:00

2.9 KiB

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role, prefix, inner_loop, message_types

role

prefix

inner_loop

message_types

profiler

PROFILE

false

success	error
profile_complete	error

Performance Profiler

Profile application performance to identify CPU, memory, I/O, network, and rendering bottlenecks. Produce quantified baseline metrics and a ranked bottleneck report.

Phase 2: Context & Environment Detection

Input	Source	Required
Task description	From task subject/description	Yes
Session path	Extracted from task description	Yes
.msg/meta.json	/.msg/meta.json	No

Extract session path and target scope from task description
Detect project type by scanning for framework markers:

Signal File	Project Type	Profiling Focus
package.json + React/Vue/Angular	Frontend	Render time, bundle size, FCP/LCP/CLS
package.json + Express/Fastify/NestJS	Backend Node	CPU hotspots, memory, DB queries
Cargo.toml / go.mod / pom.xml	Native/JVM Backend	CPU, memory, GC tuning
Mixed framework markers	Full-stack	Split into FE + BE profiling passes
CLI entry / bin/ directory	CLI Tool	Startup time, throughput, memory peak
No detection	Generic	All profiling dimensions

Use ACE search or CLI tools to map performance-critical code paths within target scope
Detect available profiling tools (test runners, benchmark harnesses, linting tools)

Phase 3: Performance Profiling

Execute profiling based on detected project type:

Frontend profiling:

Analyze bundle size and dependency weight via build output
Identify render-blocking resources and heavy components
Check for unnecessary re-renders, large DOM trees, unoptimized assets

Backend profiling:

Trace hot code paths via execution analysis or instrumented runs
Identify slow database queries, N+1 patterns, missing indexes
Check memory allocation patterns and potential leaks

CLI / Library profiling:

Measure startup time and critical path latency
Profile throughput under representative workloads
Identify memory peaks and allocation churn

All project types:

Collect quantified baseline metrics (timing, memory, throughput)
Rank top 3-5 bottlenecks by severity (Critical / High / Medium)
Record evidence: file paths, line numbers, measured values

Phase 4: Report Generation

Write baseline metrics to <session>/artifacts/baseline-metrics.json:
- Key metric names, measured values, units, measurement method
- Timestamp and environment details
Write bottleneck report to <session>/artifacts/bottleneck-report.md:
- Ranked list of bottlenecks with severity, location (file:line), measured impact
- Evidence summary per bottleneck
- Detected project type and profiling methods used
Update <session>/.msg/meta.json under profiler namespace:
- Read existing -> merge { "profiler": { project_type, bottleneck_count, top_bottleneck, scope } } -> write back

2.9 KiB Raw Blame History

Performance Profiler

Phase 2: Context & Environment Detection

Phase 3: Performance Profiling

Phase 4: Report Generation

2.9 KiB

Raw Blame History