Skip to content
All Skills

Edge Strategy Reviewer

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.

Data, AI & Research|v1|Updated 7/14/2026|GitHub source
MCP get_skill({ skillId: "edge-strategy-reviewer-642d4adb" })

Use this skill with your agent

Create a free account and connect via MCP

Get Started Free
# 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

1. Load draft YAML files from `--drafts-dir` or a single `--draft` file
2. Evaluate each draft against 8 criteria (C1-C8) with weighted scoring
3. Compute confidence score (weighted average of all criteria)
4. Determine verdict: PASS / REVISE / REJECT
5. Assess export eligibility (PASS + export_ready_v1 + exportable family)
6. 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

```bash
# 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`)

```yaml
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
#work-life#productivity#finance#trading#investing#research#reviewpython

Related Skills

More skills in Data, AI & Research

Ablation Planner

Use when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.

#broad-capability#wanshuiyin-arisMIT

Ablation Planner

Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.

#broad-capability#wanshuiyin-arisMIT

About

Provides information about the bitwize-music plugin, its version, and its creator. Use when the user asks about the plugin, its purpose, version, or capabilities.

#github#broad-capabilityCC0-1.0

Ab Test Analysis

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.

#work-life#productivityMIT

Academic Search

Search and analyze academic literature. Find papers, understand research methodologies, and synthesize academic findings for research projects.

#work-life#officeMIT

Adaptyv

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

#broad-capability#scienceMIT