Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/demodev-lab/claude-code-plugin-demokit/pdcanpx skills add demodev-lab/claude-code-plugin-demokit --skill pdcagit clone --depth 1 https://github.com/demodev-lab/claude-code-plugin-demokitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00081 | $0.02161 |
| Opus 5 | $0.00041 | $0.01081 |
| Sonnet 5 | $0.00016 | $0.00432 |
| Haiku 4.5 | $0.00008 | $0.00216 |
Grade A, and why
pdca scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/pdca - PDCA 워크플로우
help
인자가 help이면 아래 도움말만 출력하고 실행을 중단한다:
/pdca — PDCA 워크플로우 관리
사용법:
/pdca {subcommand} {feature}
하위 명령:
plan 요구사항 정의 + API 초안 + 데이터 모델 초안
design DB 스키마 상세 + API 상세 + 패키지 구조
do Entity → Repo → Service → Controller → DTO → Test 구현
analyze 설계 vs 구현 Gap 분석
iterate Match Rate < 90% 시 자동 수정 반복
report 완료 보고서 생성
status 현재 PDCA 상태 조회
next 다음 단계 안내
archive 완료된 feature 아카이브
cleanup 완료된 모든 feature 일괄 아카이브
force-stop PDCA 미완료 차단 해제 (강제 종료)
예시:
/pdca plan user-management
/pdca design user-management
/pdca do user-management
/pdca status
/pdca next
/pdca force-stop
/pdca archive user-management
/pdca cleanup
관련 명령:
/crud — CRUD 일괄 생성
/test — 테스트 코드 생성
/loop — 자동 반복 실행
하위 명령
/pdca plan {feature}
요구사항 정의 + API 초안 + 데이터 모델 초안
컨벤션:
templates/shared/ask-user-convention.md참조
- feature명으로 PDCA 상태 파일 생성 (
.pdca/{feature}.status.json) - spring-architect 에이전트 호출
AskUserQuestion도구로 요구사항 수집 (줄글 질문 금지):- Q1 (header: "핵심 기능"): 주요 기능 선택 (multiSelect: true, 도메인에 맞는 선택지 동적 생성)
- Q2 (header: "사용자 역할"): 역할 선택 (multiSelect: true, 예: 일반 사용자/관리자/게스트)
- Q3 (header: "외부 연동"): 외부 시스템 연동 여부 (예: 없음/소셜 로그인/결제 PG/알림 서비스)
- Q4 (header: "인증 방식"): 인증 방식 선택 (예: JWT/Session/OAuth2, 해당 시에만)
- Plan 문서 생성 (
.pdca/{feature}/plan.md):- 요구사항 목록
- API 엔드포인트 초안 (Method + Path + 설명)
- Entity 초안 (이름 + 주요 필드)
- 기술적 고려사항
- 상태 업데이트: plan → completed
/pdca design {feature}
DB 스키마 상세 + API 상세 + 패키지 구조
- Plan 문서 로드 및 확인
- 병렬 설계 — 다음 Task들을 한 메시지에서 동시에 호출:
- Task 1 (domain-expert): DB 스키마 상세 설계 (테이블, 컬럼, 제약조건, 인덱스)
- Task 2 (api-expert): API 상세 스펙 (Request/Response Body, 상태 코드)
- spring-architect가 결과를 통합하여 Design 문서 생성 (
.pdca/{feature}/design.md):- DB 테이블 스키마
- API 상세 스펙
- Entity 관계도
- 패키지별 클래스 목록
- 구현 순서
- 상태 업데이트: design → completed
/pdca do {feature}
Entity → Repo → Service → Controller → DTO → Test 구현
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 206 lines · 81 tokens per session scan A 7ed2c4e014f3
pdca is a skill published in the GitHub repository demodev-lab/claude-code-plugin-demokit (2 stars, last pushed 6mo ago), licensed MIT. It adds 81 tokens to every session and 2,161 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…