edge-candidate-agent

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesNot pinnedSome versions float
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

examples/daily-market-dashboard/.claude/skills

numpy, pandas, PyYAML

Audit 0.4.1 · source sha c91a4ba0d6ec · 29 Oct 2026Report an issue or request a re-scan

• Publishertradermonty GitHub userOwner of the repository this skill is read from
• Source foldertradermonty/claude-trading-skills/skills/edge-candidate-agentThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc91a4ba0d6ecA new audit runs when the source changes
• Repository statusNot archivedLast push 2 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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…

---
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`
   - Optional: `--hints` for human ideation input
   - Optional: `--llm-ideas-cmd` for external LLM ideation loop
2. Load the contract and mapping references:
   - `references/pipeline_if_v1.md`
   - `references/signal_mapping.md`
   - `references/research_ticket_schema.md`
   - `references/ideation_loop.md`
3. Build or update a research ticket using `references/research_ticket_schema.md`.
4. Export candidate artifacts with `skills/edge-candidate-agent/scripts/export_candidate.py`.
5. Validate interface and Phase I constraints with `skills/edge-candidate-agent/scripts/validate_candidate.py`.
6. Hand off candidate directory to `trade-strategy-pipeline` and run dry-run first.

## Quick Commands

Daily auto-detection (with optional export/validation):

```bash
python3 skills/edge-candidate-agent/scripts/auto_detect_candidates.py \
  --ohlcv /path/to/ohlcv.parquet \
  --output-dir reports/edge_candidate_auto \
  --top-n 10 \
  --hints path/to/hints.yaml \
  --export-strategies-dir /path/to/trade-strategy-pipeline/strategies \
  --pipeline-root /path/to/trade-strategy-pipeline
```

Create a candidate directory from a ticket:

```bash
python3 skills/edge-candidate-agent/scripts/export_candidate.py \
  --ticket path/to/ticket.yaml \
  --strategies-dir /path/to/trade-strategy-pipeline/strategies
```

Validate interface contract only:

```bash
python3 skills/edge-candidate-agent/scripts/validate_candidate.py \
  --strategy /path/to/trade-strategy-pipeline/strategies/my_candidate_v1/strategy.yaml
```

Validate both interface contract and pipeline schema/stage rules:

```bash
python3 skills/edge-candidate-agent/scripts/validate_candidate.py \
  --strategy /path/to/trade-strategy-pipeline/strategies/my_candidate_v1/strategy.yaml \
  --pipeline-root /path/to/trade-strategy-pipeline \
  --stage phase1
```

## Export Rules

- Keep `validation.method: full_sample`.
- Keep `validation.oos_ratio` omitted or `null`.
- Export only supported entry families for v1:
  - `pivot_breakout` with `vcp_detection`
  - `gap_up_continuation` with `gap_up_detection`
- Mark unsupported hypothesis families as research-only in ticket notes, not as export candidates.

## Guardrails

- Reject candidates that violate schema bounds (risk, exits, empty conditions).
- Reject candidate when folder name and `id` mismatch.
- Require deterministic metadata with `interface_version: edge-finder-candidate/v1`.
- Use `--dry-run` in pipeline before full execution.

## Resources

### `skills/edge-candidate-agent/scripts/export_candidate.py`
Generate `strategies/<candidate_id>/strategy.yaml` and `metadata.json` from a research ticket YAML.

### `skills/edge-candidate-agent/scripts/validate_candidate.py`
Run interface checks and optional `StrategySpec`/`validate_spec` checks against `trade-strategy-pipeline`.

### `skills/edge-candidate-agent/scripts/auto_detect_candidates.py`
Auto-detect edge ideas from EOD OHLCV, generate exportable/research tickets, and optionally export/validate automatically.

### `references/pipeline_if_v1.md`
Condensed integration contract for `edge-finder-candidate/v1`.

### `references/signal_mapping.md`
Map hypothesis families to currently exportable signal families.

### `references/research_ticket_schema.md`
Ticket schema used by `export_candidate.py`.

### `references/ideation_loop.md`
Hint schema and external LLM ideation command contract.

Excerpt, first 5,812 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

uv run python -m pipeline.runner.cli \
uv run python -m pipeline.runner.cli --strategy <candidate_id> --dry-run

git clone https://github.com/tradermonty/claude-trading-skills
git -C claude-trading-skills checkout c91a4ba0d6ec

Works in

1. 2026-10-29
   ✓SAFE B 89 / 100

   first audit · source c91a4ba0d6ec

Questions about edge-candidate-agent

• github.com/tradermonty/claude-trading-skills/tree/main/skills/ed — the audited source at commit c91a4ba0d6ec and every finding aboveObserved · 29 Oct 2026

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