How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-strategy-reviewerBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
Edge Strategy Reviewer
Deterministic quality gate for strategy drafts produced by edge-strategy-designer.
When to Use
- After
edge-strategy-designergeneratesstrategy_drafts/*.yaml - Before exporting drafts to
edge-candidate-agentvia the pipeline - When manually validating a draft strategy for edge plausibility
Prerequisites
- Strategy draft YAML files (output of
edge-strategy-designer) - Python 3.10+ with PyYAML
Workflow
- Load draft YAML files from
--drafts-diror a single--draftfile - Evaluate each draft against 8 criteria (C1-C8) with weighted scoring
- Compute confidence score (weighted average of all criteria)
- Determine verdict: PASS / REVISE / REJECT
- Assess export eligibility (PASS + exportreadyv1 + exportable family)
- Write review output (YAML or JSON) and optional markdown summary
Review Criteria
| # | Criterion | Weight | Key Checks |
|---|---|---|---|
| C1 | Edge Plausibility | 20 | Thesis quality, domain terms, mechanism keywords (continuous 50-95) |
| C2 | Overfitting Risk | 20 | 5-tier filter count scoring (90/80/60/40/10), precise threshold penalty |
| C3 | Sample Adequacy | 15 | Continuous scoring from estimated annual opportunities (10-95) |
| C4 | Regime Dependency | 10 | Cross-regime validation |
| C5 | Exit Calibration | 10 | Stop-loss, reward-to-risk |
| C6 | Risk Concentration | 10 | Position sizing limits |
| C7 | Execution Realism | 10 | Volume filter, export consistency |
| C8 | Invalidation Quality | 5 | Signal count and specificity |
Verdict Logic
- C1 or C2 severity=fail → immediate REJECT
- confidence >= 70, no fail findings → PASS
- confidence < 35 → REJECT
- Otherwise → REVISE (with revision instructions)
Running the Script
# Review all drafts in a directory
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
--drafts-dir reports/edge_strategy_drafts/ \
--output-dir reports/
# Single draft review
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
--draft reports/edge_strategy_drafts/draft_xxx.yaml \
--output-dir reports/
# JSON output with markdown summary
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
--drafts-dir reports/edge_strategy_drafts/ \
--output-dir reports/ \
--format json \
--markdown-summary
# Strict export mode: export-eligible drafts with any warn → REVISE
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
--drafts-dir reports/edge_strategy_drafts/ \
--output-dir reports/ \
--strict-export
# Bias checklist (issue #297): mandatory strategy-research bias gate.
# Uses the bundled checklist unless a custom YAML path is supplied.
# A draft leaving any required item unaddressed is downgraded PASS → REVISE
# and loses export eligibility.
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
--drafts-dir reports/edge_strategy_drafts/ \
--output-dir reports/ \
--bias-checklistOutput Format
Primary output: review.yaml (or review.json)
generated_at_utc: "2026-02-28T12:00:00+00:00"
source:
drafts_dir: "/path/to/strategy_drafts"
draft_count: 4
summary:
total: 4
PASS: 1
REVISE: 2
REJECT: 1
export_eligible: 1
reviews:
- draft_id: "draft_xxx_core"
verdict: "PASS"
confidence_score: 80
export_eligible: true
findings: [...]
revision_instructions: []Resources
references/review_criteria.md— Detailed scoring rubric for C1-C8references/overfitting_checklist.md— Overfitting detection heuristics


