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.
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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.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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)还没有评价。欢迎分享你的使用感受。
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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.
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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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。