edge-candidate-agent
Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.
npx skills add BaggaT236/AI-Trading-Skills --skill edge-candidate-agent --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Edge Candidate Agent ## Overview Convert daily market observations into reproducible research tickets and Phase I-compatible candidate specs. Prioritize signal quality and interface compatibility over aggressive strategy proliferation. This skill can run end-to-end standalone, but in the split workflow it primarily serves the final export/validation stage. ## When to Use - Convert market observations, anomalies, or hypotheses into structured research tickets. - Run daily auto-detection to discover new edge candidates from EOD OHLCV and optional hints. - Export validated tickets as `strategy.yaml` + `metadata.json` for `trade-strategy-pipeline` Phase I. - Run preflight compatibility checks for `edge-finder-candidate/v1` before pipeline execution. ## Prerequisites - Python 3.9+ with `PyYAML` installed. - Access to the target `trade-strategy-pipeline` repository for schema/stage validation. - `uv` available when running pipeline-managed validation via `--pipeline-root`. ## Output - `strategies/<candidate_id>/strategy.yaml`: Phase I-compatible strategy spec. - `strategies/<candidate_id>/metadata.json`: provenance metadata including interface version and ticket context. - Validation s
- Overview
- When to Use
- Prerequisites
- Output
- Position in Split Workflow
- Workflow
- Quick Commands
- Export Rules
- Guardrails
- Resources
- skills/edge-candidate-agent/scripts/exportcandidate.py
- skills/edge-candidate-agent/scripts/validatecandidate.py
- skills/edge-candidate-agent/scripts/autodetectcandidates.py
- references/pipelineifv1.md
python3 skills/edge-candidate-agent/scripts/auto_detect_candidates.py \ python3 skills/edge-candidate-agent/scripts/export_candidate.py \ python3 skills/edge-candidate-agent/scripts/validate_candidate.py \
What does the edge-candidate-agent skill do?
Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill edge-candidate-agent --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.