# edge-candidate-agent

Turns US equity EOD observations into research tickets and exports validated candidates for trade-strategy-pipeline Phase I.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3300 installs · ★ 3267
- Author: tradermonty
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/edge-candidate-agent/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/edge-candidate-agent

## What it does

The skill turns EOD OHLCV observations, anomalies, and hypotheses into reproducible research tickets. `auto_detect_candidates.py` can discover new edge candidates, accept an optional `hints.yaml`, and run an external ideation loop through `--llm-ideas-cmd`. Its daily artifacts include a report, market summary, anomaly list, watchlist, and ticket YAML files.

Once a ticket is ready, `export_candidate.py` creates `strategies/<candidate_id>/strategy.yaml` and `metadata.json`. `validate_candidate.py` checks the `edge-finder-candidate/v1` interface; with `--pipeline-root` and `--stage phase1`, it also checks pipeline schema and stage rules. Export supports `pivot_breakout` with `vcp_detection` and `gap_up_continuation` with `gap_up_detection`; other hypothesis families remain research-only. Python 3.9+, `PyYAML`, access to the `trade-strategy-pipeline` repository, and `uv` for pipeline-managed validation are required. Check that environment before running it.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/edge-candidate-agent/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/edge-candidate-agent/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/edge-candidate-agent/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/edge-candidate-agent/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install tradermonty/claude-trading-skills edge-candidate-agent
```

2. **Manually: put the files in place**
- File: `.github/skills/edge-candidate-agent/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/tradermonty/claude-trading-skills --path skills/edge-candidate-agent
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/edge-candidate-agent/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/edge-candidate-agent/SKILL.md

## When to use it
- turn EOD OHLCV observations into research tickets
- discover new edge candidates from market data
- export a ticket as `strategy.yaml` and `metadata.json`
- check compatibility with `edge-finder-candidate/v1`
- prepare a candidate for a `trade-strategy-pipeline` dry-run

## Example prompts
- Turn these EOD OHLCV observations into research tickets and write the artifacts to `reports/edge_candidate_auto`.
- Export `path/to/ticket.yaml` to the `trade-strategy-pipeline` strategies directory, then validate the candidate.
- Check `strategy.yaml` against `edge-finder-candidate/v1` and the `phase1` rules using `trade-strategy-pipeline`.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 96018cdff1581b71.

BEGIN THIRD-PARTY FILE 96018cdff1581b71
```text
---
name: edge-candidate-agent
description: 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.
---

# 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 status from `scripts/validate_candidate.py` (pass/fail + reasons).
- Daily detection artifacts:
  - `daily_report.md`
  - `market_summary.json`
  - `anomalies.json`
  - `watchlist.csv`
  - `tickets/exportable/*.yaml`
  - `tickets/research_only/*.yaml`

## Position in Split Workflow

Recommended split workflow:

1. `skills/edge-hint-extractor`: observations/news -> `hints.yaml`
2. `skills/edge-concept-synthesizer`: tickets/hints -> `edge_concepts.yaml`
3. `skills/edge-strategy-designer`: concepts -> `strategy_drafts` + exportable ticket YAML
4. `skills/edge-candidate-agent` (this skill): export + validate for pipeline handoff

## Workflow

1. Run auto-detection from EOD OHLCV:
   - `skills/edge-candidate-agent/scripts/auto_detect_candidates.py`
```
END THIRD-PARTY FILE 96018cdff1581b71

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