MileniumTick/edge-strategy-reviewer
skills/edge-strategy-reviewer/SKILL.md
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.
testing
Updated Apr 16, 2026
$ install --global
skillsauthnpx skillsauth add MileniumTick/skills edge-strategy-reviewerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 16, 2026, 12:57 PM6.5s6 files scanned
SKILL.md
- name:
- edge-strategy-reviewer
- description:
- >
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 + 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-C8references/overfitting_checklist.md— Overfitting detection heuristics
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