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name: peer-review description: | Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus.

Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it.

Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s latency is unacceptable, reviewing trivial or low-stakes content.

Negative examples:

• "Check if this date is correct" → No. Just web search it.
• "Review my grocery list" → No. Not worth multi-model inference.
• "I need this answer in 5 seconds" → No. Peer review adds 30-60s latency.

Edge cases:

• Short text (<50 words) → Models may not find meaningful issues. Consider skipping.
• Highly technical domain → Local models may lack domain knowledge. Weight flags lower.
• Creative writing → Factual review doesn't apply well. Use only for logical consistency. version: "1.0"

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Peer Review — Local LLM Critique Layer

Hypothesis: Local LLMs can catch ≥30% of real errors in cloud output with <50% false positive rate.

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Architecture

Cloud Model (Claude) produces analysis
        │
        ▼
┌────────────────────────┐
│   Peer Review Fan-Out  │
├────────────────────────┤
│  Drift (Mistral 7B)   │──► Critique A
│  Pip (TinyLlama 1.1B) │──► Critique B
│  Lume (Llama 3.1 8B)  │──► Critique C
└────────────────────────┘
        │
        ▼
  Aggregator (consensus logic)
        │
        ▼
  Final: original + flagged issues

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Swarm Bot Roles

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Scripts

Usage

# Single file review
bash scripts/peer-review.sh <input_file> [output_dir]

# Batch review
bash scripts/peer-review-batch.sh <corpus_dir> [results_dir]

# Generate test corpus
bash scripts/seed-test-corpus.sh [count] [output_dir]

Scripts live at workspace/scripts/ — not bundled in skill to avoid duplication.

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Critique Prompt Template

You are a skeptical reviewer. Analyze the following text for errors.

For each issue found, output JSON:
{"category": "factual|logical|missing|overconfidence|hallucinated_source",
 "quote": "...", "issue": "...", "confidence": 0-100}

If no issues found, output: {"issues": []}

TEXT:
---
{cloud_output}
---

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Error Categories

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Discord Workflow

1. Post analysis to #the-deep (or #swarm-lab)
2. Drift, Pip, and Lume respond with independent critiques
3. Celeste synthesizes: deduplicates flags, weights by model confidence
4. If consensus (≥2 models agree) → flag is high-confidence
5. Final output posted with recommendation: publish | revise | flag_for_human

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Success Criteria

Scoring Rules

• Flag = true positive if it identifies a real error (even if explanation is imperfect)
• Flag = false positive if flagged content is actually correct
• Duplicate flags across models count once for TPR but inform consensus metrics

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Dependencies

• Ollama running locally with models pulled: mistral:7b, tinyllama:1.1b, llama3.1:8b
• jq and curl installed
• Results stored in experiments/peer-review-results/

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Integration

When peer review passes validation:

• Package as Reef API endpoint: POST /review
• Agents call before publishing any analysis
• Configurable: model selection, consensus threshold, categories
• Log all reviews to #reef-logs with TPR tracking