edge-hint-extractor — Claude Code skill: market hints to YAML
Turns daily market observations and news reactions into structured edge hints, written out as hints.yaml.
npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a claude-codeWhat edge-hint-extractor does
The skill reads raw signals — market_summary, anomalies, news reactions — and produces hint objects from them. It sits first in the chain observe -> abstract -> design -> pipeline, so the resulting hints.yaml feeds concept synthesis or auto detection.
One script does the work: scripts/build_hints.py. Rule-based hints come out deterministic. Model-generated ideas arrive by either of two routes, and you pick one: --llm-ideas-cmd pipes data to an external LLM CLI as a subprocess, while --llm-ideas-file PATH loads hints you already wrote into a YAML file — the route meant for Claude, where the agent writes the hints itself. Passing both flags at once fails.
Python 3.9+ and PyYAML are required. Observation files are optional, though there is nothing to build without them: market_summary.json and anomalies.json come from a detector run, and news reactions live in news_reactions.csv or news_reactions.json. Output defaults to reports/edge_hint_extractor/hints.yaml; the schema sits in references/hints_schema.md.
What it helps with
- turn daily market observations into hint objects
- add model-written ideas on top of anomalies and news
- produce a clean
hints.yamlfor concept synthesis - prepare input for auto detection
- consolidate detector signals into one structured file
Example prompts for the agent
- Build hints from
market_summary.jsonandanomalies.jsonas of 2026-02-20 intoreports/ - Add your own ideas about the current anomalies and save
hints.yaml - Run
build_hints.pywith--llm-ideas-file /tmp/llm_hints.yaml
How to install edge-hint-extractor
1Skills CLI (Vercel)
npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a claude-code2Manually: put the files in place
~/.claude/skills/edge-hint-extractor/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/edge-hint-extractor/
1Skills CLI (Vercel)
npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a codex2Manually: put the files in place
.agents/skills/edge-hint-extractor/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a cursor2Manually: put the files in place
.cursor/skills/edge-hint-extractor/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install tradermonty/claude-trading-skills edge-hint-extractor2Manually: put the files in place
.github/skills/edge-hint-extractor/SKILL.md1Skills CLI (Vercel)
npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a gemini-cli2Platform CLI
gemini skills install https://github.com/tradermonty/claude-trading-skills --path skills/edge-hint-extractorManually: put the files in place
.devin/skills/edge-hint-extractor/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about edge-hint-extractor
How do I install edge-hint-extractor in Claude Code?
Run npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a claude-code. The skill lands at ~/.claude/skills/edge-hint-extractor/SKILL.md.
What does edge-hint-extractor do?
It converts market observations and news reactions into structured edge hints and writes them to hints.yaml, along with generation metadata and rule/LLM hint counts.
How do --llm-ideas-cmd and --llm-ideas-file differ?
The first sends data to an external LLM CLI through a subprocess; the second reads hints you prepared in a YAML file. They are mutually exclusive.
Source text
SKILL.md · MIT
--- name: edge-hint-extractor description: Extract edge hints from daily market observations and news reactions, with optional LLM ideation, and output canonical hints.yaml for downstream concept synthesis and auto detection. --- # Edge Hint Extractor ## Overview 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`. ## When to Use - You want to turn daily market observations into reusable hint objects. - You want LLM-generated ideas constrained by current anomalies/news context. - You need a clean `hints.yaml` input for concept synthesis or auto detection. ## Prerequisites - Python 3.9+ - `PyYAML` - Optional inputs from detector run: - `market_summary.json` - `anomalies.json` - `news_reactions.csv` or `news_reactions.json` ## Output - `hints.yaml` containing: - `hints` list - generation metadata - rule/LLM hint counts ## Workflow 1. Gather observation files (`market_summary`, `anomalies`, optional news reactions). 2. Run `scripts/build_hints.py` to generate deterministic hints. 3. Optionally augment hints with LLM ideas via one of two methods: - a. `--llm-ideas-cmd` — pipe data to an external LLM CLI (subprocess). - b. `--llm-ideas-file PATH` — load pre-written hints from a YAML file (for Claude Code workflows where Claude generates hints itself). 4. Pass `hints.yaml` into concept synthesis or auto detection. Note: `--llm-ideas-cmd` and `--llm-ideas-file` are mutually exclusive. ## Quick Commands Rule-based only (default output to `reports/edge_hint_extractor/hints.yaml`): ```bash 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): ```bash 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): ```bash 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/ ``` ## Resources - `skills/edge-hint-extractor/scripts/build_hints.py` - `references/hints_schema.md`
© tradermonty · MIT · tradermonty/claude-trading-skills