重要前提
安装AI Skills的关键前提是:必须科学上网,且开启TUN模式,这一点至关重要,直接决定安装能否顺利完成,在此郑重提醒三遍:科学上网,科学上网,科学上网。查看完整安装教程 →
edge-hint-extractor by tradermonty/claude-trading-skills
npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-hint-extractor将原始观测信号(market_summary、anomalies、news reactions)转换为结构化的 edge hints。此技能是拆分工作流的第一阶段:observe -> abstract -> design -> pipeline。
hints.yaml 输入,用于概念合成或自动检测。PyYAMLmarket_summary.json广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
anomalies.jsonnews_reactions.csv 或 news_reactions.jsonhints.yaml,包含:
hints 列表market_summary、anomalies,可选的新闻反应)。scripts/build_hints.py 以生成确定性提示。--llm-ideas-cmd — 将数据通过管道传输到外部 LLM CLI(子进程)。--llm-ideas-file PATH — 从 YAML 文件加载预写的提示(适用于 Claude Code 工作流,其中 Claude 自行生成提示)。hints.yaml 传递给概念合成或自动检测。注意:--llm-ideas-cmd 和 --llm-ideas-file 是互斥的。
仅基于规则(默认输出到 reports/edge_hint_extractor/hints.yaml):
python3 skills/edge-hint-extractor/scripts/build_hints.py \
--market-summary /tmp/edge-auto/market_summary.json \
--anomalies /tmp/edge-auto/anomalies.json \
--news-reactions /tmp/news_reactions.csv \
--as-of 2026-02-20 \
--output-dir reports/
规则 + LLM 增强(外部 CLI):
python3 skills/edge-hint-extractor/scripts/build_hints.py \
--market-summary /tmp/edge-auto/market_summary.json \
--anomalies /tmp/edge-auto/anomalies.json \
--llm-ideas-cmd "python3 /path/to/llm_ideas_cli.py" \
--output-dir reports/
规则 + LLM 增强(预写文件,适用于 Claude Code):
python3 skills/edge-hint-extractor/scripts/build_hints.py \
--market-summary /tmp/edge-auto/market_summary.json \
--anomalies /tmp/edge-auto/anomalies.json \
--llm-ideas-file /tmp/llm_hints.yaml \
--output-dir reports/
skills/edge-hint-extractor/scripts/build_hints.pyreferences/hints_schema.md每周安装量
79
代码仓库
GitHub 星标数
394
首次出现
2026年2月23日
安全审计
安装于
cursor76
gemini-cli75
github-copilot75
amp75
codex75
kimi-cli75
Convert raw observation signals (market_summary, anomalies, news reactions) into structured edge hints. This skill is the first stage in the split workflow: observe -> abstract -> design -> pipeline.
hints.yaml input for concept synthesis or auto detection.PyYAMLmarket_summary.jsonanomalies.jsonnews_reactions.csv or news_reactions.jsonhints.yaml containing:
hints listmarket_summary, anomalies, optional news reactions).scripts/build_hints.py to generate deterministic hints.--llm-ideas-cmd — pipe data to an external LLM CLI (subprocess).--llm-ideas-file PATH — load pre-written hints from a YAML file (for Claude Code workflows where Claude generates hints itself).hints.yaml into concept synthesis or auto detection.Note: --llm-ideas-cmd and --llm-ideas-file are mutually exclusive.
Rule-based only (default output to reports/edge_hint_extractor/hints.yaml):
python3 skills/edge-hint-extractor/scripts/build_hints.py \
--market-summary /tmp/edge-auto/market_summary.json \
--anomalies /tmp/edge-auto/anomalies.json \
--news-reactions /tmp/news_reactions.csv \
--as-of 2026-02-20 \
--output-dir reports/
Rule + LLM augmentation (external CLI):
python3 skills/edge-hint-extractor/scripts/build_hints.py \
--market-summary /tmp/edge-auto/market_summary.json \
--anomalies /tmp/edge-auto/anomalies.json \
--llm-ideas-cmd "python3 /path/to/llm_ideas_cli.py" \
--output-dir reports/
Rule + LLM augmentation (pre-written file, for Claude Code):
python3 skills/edge-hint-extractor/scripts/build_hints.py \
--market-summary /tmp/edge-auto/market_summary.json \
--anomalies /tmp/edge-auto/anomalies.json \
--llm-ideas-file /tmp/llm_hints.yaml \
--output-dir reports/
skills/edge-hint-extractor/scripts/build_hints.pyreferences/hints_schema.mdWeekly Installs
79
Repository
GitHub Stars
394
First Seen
Feb 23, 2026
Security Audits
Gen Agent Trust HubWarnSocketPassSnykWarn
Installed on
cursor76
gemini-cli75
github-copilot75
amp75
codex75
kimi-cli75
AI Elements:基于shadcn/ui的AI原生应用组件库,快速构建对话界面
73,400 周安装