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- mateaix/mateclaw
- 최근 소스 활동
- 2026년 5월 1일 14:39
- 감지된 SKILL.md 언어
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설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
SOC 직업 분류 기준
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/mateaix/mateclaw --skill subagent-driven-development명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
Use the optional iOfficeAI/OfficeCLI engine for advanced inspection, validation, copy-on-write editing, template merge, or visual rendering of existing .docx, .xlsx, and .pptx files. Prefer MateClaw's built-in renderDocx/renderXlsx/renderPptx tools for simple new documents. Use this skill when preserving an existing template, modifying complex Office structure, checking formatting issues, validating OpenXML, or rendering a document for visual QA. This integration targets https://github.com/iOfficeAI/OfficeCLI, not the unrelated prompt-generation project with the same name.
当需要主动向某个渠道会话单向推送消息时使用(企业微信、钉钉、飞书、Telegram、Discord、QQ、Slack 等)。适用于任务完成通知、定时提醒告警、异步结果回推等场景。先用 list_channel_sessions 查询目标会话,再用 send_channel_message 发送。
公众号图文创作 / 推文 / 官方号文章 (official account article) — 端到端:选题→搜集→成文→配图→去AI化→公众号内联样式排版→交付/草稿箱。honors user persona & style memory.
| name | subagent-driven-development |
| description | Execute plans via delegate_task subagents (2-stage review). |
| version | 1.1.0 |
| tags | ["delegation","subagent","implementation","workflow","parallel"] |
| author | ported |
Execute implementation plans by dispatching fresh subagents per task with systematic two-stage review.
Core principle: Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration.
Use this skill when:
vs. manual execution:
Read the plan file. Extract ALL tasks with their full text and context upfront. Create a todo list:
# Read the plan
read_file("docs/plans/feature-plan.md")
# Create todo list with all tasks
todo([
{"id": "task-1", "content": "Create User model with email field", "status": "pending"},
{"id": "task-2", "content": "Add password hashing utility", "status": "pending"},
{"id": "task-3", "content": "Create login endpoint", "status": "pending"},
])
Key: Read the plan ONCE. Extract everything. Don't make subagents read the plan file — provide the full task text directly in context.
For EACH task in the plan:
Use delegate_task with complete context:
delegate_task(
goal="Implement Task 1: Create User model with email and password_hash fields",
context="""
TASK FROM PLAN:
- Create: src/models/user.py
- Add User class with email (str) and password_hash (str) fields
- Use bcrypt for password hashing
- Include __repr__ for debugging
FOLLOW TDD:
1. Write failing test in tests/models/test_user.py
2. Run: pytest tests/models/test_user.py -v (verify FAIL)
3. Write minimal implementation
4. Run: pytest tests/models/test_user.py -v (verify PASS)
5. Run: pytest tests/ -q (verify no regressions)
6. Commit: git add -A && git commit -m "feat: add User model with password hashing"
PROJECT CONTEXT:
- Python 3.11, Flask app in src/app.py
- Existing models in src/models/
- Tests use pytest, run from project root
- bcrypt already in requirements.txt
""",
toolsets=['terminal', 'file']
)
After the implementer completes, verify against the original spec:
delegate_task(
goal="Review if implementation matches the spec from the plan",
context="""
ORIGINAL TASK SPEC:
- Create src/models/user.py with User class
- Fields: email (str), password_hash (str)
- Use bcrypt for password hashing
- Include __repr__
CHECK:
- [ ] All requirements from spec implemented?
- [ ] File paths match spec?
- [ ] Function signatures match spec?
- [ ] Behavior matches expected?
- [ ] Nothing extra added (no scope creep)?
OUTPUT: PASS or list of specific spec gaps to fix.
""",
toolsets=['file']
)
If spec issues found: Fix gaps, then re-run spec review. Continue only when spec-compliant.
After spec compliance passes:
delegate_task(
goal="Review code quality for Task 1 implementation",
context="""
FILES TO REVIEW:
- src/models/user.py
- tests/models/test_user.py
CHECK:
- [ ] Follows project conventions and style?
- [ ] Proper error handling?
- [ ] Clear variable/function names?
- [ ] Adequate test coverage?
- [ ] No obvious bugs or missed edge cases?
- [ ] No security issues?
OUTPUT FORMAT:
- Critical Issues: [must fix before proceeding]
- Important Issues: [should fix]
- Minor Issues: [optional]
- Verdict: APPROVED or REQUEST_CHANGES
""",
toolsets=['file']
)
If quality issues found: Fix issues, re-review. Continue only when approved.
todo([{"id": "task-1", "content": "Create User model with email field", "status": "completed"}], merge=True)
After ALL tasks are complete, dispatch a final integration reviewer:
delegate_task(
goal="Review the entire implementation for consistency and integration issues",
context="""
All tasks from the plan are complete. Review the full implementation:
- Do all components work together?
- Any inconsistencies between tasks?
- All tests passing?
- Ready for merge?
""",
toolsets=['terminal', 'file']
)
# Run full test suite
pytest tests/ -q
# Review all changes
git diff --stat
# Final commit if needed
git add -A && git commit -m "feat: complete [feature name] implementation"
Each task = 2-5 minutes of focused work.
Too big:
Right size:
Why fresh subagent per task:
Why two-stage review:
Cost trade-off:
This skill EXECUTES plans created by the writing-plans skill:
Implementer subagents should follow TDD:
Include TDD instructions in every implementer context.
The two-stage review process IS the code review. For final integration review, use the requesting-code-review skill's review dimensions.
If a subagent encounters bugs during implementation:
[Read plan: docs/plans/auth-feature.md]
[Create todo list with 5 tasks]
--- Task 1: Create User model ---
[Dispatch implementer subagent]
Implementer: "Should email be unique?"
You: "Yes, email must be unique"
Implementer: Implemented, 3/3 tests passing, committed.
[Dispatch spec reviewer]
Spec reviewer: ✅ PASS — all requirements met
[Dispatch quality reviewer]
Quality reviewer: ✅ APPROVED — clean code, good tests
[Mark Task 1 complete]
--- Task 2: Password hashing ---
[Dispatch implementer subagent]
Implementer: No questions, implemented, 5/5 tests passing.
[Dispatch spec reviewer]
Spec reviewer: ❌ Missing: password strength validation (spec says "min 8 chars")
[Implementer fixes]
Implementer: Added validation, 7/7 tests passing.
[Dispatch spec reviewer again]
Spec reviewer: ✅ PASS
[Dispatch quality reviewer]
Quality reviewer: Important: Magic number 8, extract to constant
Implementer: Extracted MIN_PASSWORD_LENGTH constant
Quality reviewer: ✅ APPROVED
[Mark Task 2 complete]
... (continue for all tasks)
[After all tasks: dispatch final integration reviewer]
[Run full test suite: all passing]
[Done!]
Fresh subagent per task
Two-stage review every time
Spec compliance FIRST
Code quality SECOND
Never skip reviews
Catch issues early
Quality is not an accident. It's the result of systematic process.
When the orchestration involves significant context usage, long review loops, or complex validation checkpoints, load these references for the specific discipline:
references/context-budget-discipline.md — Four-tier context degradation model (PEAK / GOOD / DEGRADING / POOR), read-depth rules that scale with context window size, and early warning signs of silent degradation. Load when a run will clearly consume significant context (multi-phase plans, many subagents, large artifacts).references/gates-taxonomy.md — The four canonical gate types (Pre-flight, Revision, Escalation, Abort) with behavior, recovery, and examples. Load when designing or reviewing any workflow that has validation checkpoints — use the vocabulary explicitly so each gate has defined entry, failure behavior, and resumption rules.Both references adapted from gsd-build/get-shit-done (MIT © 2025 Lex Christopherson).