Generate a Claude CLI prompt from a structured JSON skill idea. The prompt guides creation of a complete skill directory, including SKILL.md, reference documents, helper scripts, and test scaffolding that follows repository conventions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design new Claude skills from structured idea specifications. Use when the skill auto-generation pipeline needs to produce a Claude CLI prompt that creates a complete skill directory (SKILL.md, references, scripts, tests) following repository conventions.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Generate a comprehensive Claude CLI prompt from a structured skill idea specification. The prompt instructs Claude to create a complete skill directory following repository conventions: SKILL.md with YAML frontmatter, reference documents, helper scripts, and test scaffolding.
claude -preferences/Accept a JSON file (--idea-json) containing:
title: Human-readable idea namedescription: What the skill doescategory: Skill category (e.g., trading-analysis, developer-tooling)Accept a normalized skill name (--skill-name) that will be used as the
directory name and YAML frontmatter name: field.
Run the prompt builder:
python3 skills/skill-designer/scripts/build_design_prompt.py \
--idea-json /tmp/idea.json \
--skill-name "my-new-skill" \
--project-root .
The script:
The calling pipeline pipes the prompt into claude -p:
python3 skills/skill-designer/scripts/build_design_prompt.py \
--idea-json /tmp/idea.json \
--skill-name "my-new-skill" \
--project-root . \
| claude -p --allowedTools Read,Edit,Write,Glob,Grep
After Claude creates the skill, verify:
skills/<skill-name>/SKILL.md exists with correct frontmatterThe script outputs a plain-text prompt to stdout. Exit code 0 on success, 1 if required reference files are missing.
references/skill-structure-guide.md -- Directory structure, SKILL.md format, naming conventionsreferences/quality-checklist.md -- Dual-axis reviewer 5-category checklist (100 points)references/skill-template.md -- SKILL.md template with YAML frontmatter and standard sectionsscripts/build_design_prompt.py -- Prompt builder script (CLI interface)아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Plan and assess backtests, which test trading rules against historical data. Examine sensitivity to rule changes, trading costs, and data bias, then use supplied performance metrics to generate a structured evaluation with reasons that block adoption.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Assess how broadly US stock market gains are shared across stocks using public CSV data and optional charts. Produce English reports covering market conditions, trading signals, scenarios, and positioning for different investment timeframes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze chart images showing how broadly US stocks are participating in market trends. Assess medium-term conditions and short-term signals, then produce an English report with positioning guidance, alternative scenarios, and risks based on the visible data.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Turn VCP screening results, which identify narrowing price swings, into breakout trade plans. Calculate entry, stop-loss, target prices, and position sizes using worst-case entries, while applying portfolio risk limits and producing Alpaca-compatible order templates.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Screen US growth stocks with William O'Neil's CANSLIM method, which evaluates seven factors including earnings, price strength, trading volume, and market direction. Receive ranked candidates with composite scores, factor breakdowns, market warnings, and data quality notes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Combine three reports on crowded positions, failed reactions to news, and weekly price reversals into one trading setup status. Identify missing or invalid evidence and determine whether the setup is ready for planning.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.