Edge strategy reviewer

Skill BaggaT236/AI-Trading-Skills/skills/edge-strategy-reviewer

Ai agent claude trading skills for disiplined, repeatable trading workflows with a modern typescript platform

Install

npx -y skills add BaggaT236/AI-Trading-Skills --skill edge-strategy-reviewer

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What its author says it does

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Critically review strategy drafts from edge-strategy-designer for edge plausibility, overfitting risk, sample size adequacy, and execution realism. Use when strategy_drafts/*.yaml exists and needs quality gate before pipeline export. Outputs PASS/REVISE/REJECT verdicts with confidence scores.

SKILL.md

3.5 KB, as published. Nobody here has run it

Edge Strategy Reviewer

Deterministic quality gate for strategy drafts produced by edge-strategy-designer.

When to Use

After edge-strategy-designer generates strategy_drafts/*.yaml
Before exporting drafts to edge-candidate-agent via 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-dir or a single --draft file
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 + export_ready_v1 + 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

Output 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-C8
references/overfitting_checklist.md — Overfitting detection heuristics