Stage 1: Mechanical Verification ($0 cost)
- Lint checks, build validation, test execution
- Static analysis, coverage measurement
- Fails fast if mechanical checks don't pass
Stage 2: Semantic Evaluation (Standard tier)
- AC compliance assessment
- Goal alignment scoring
- Drift measurement
- Reasoning explanation
Stage 3: Multi-Model Consensus (Frontier tier, optional)
- Multiple models vote on approval
- Only triggered by uncertainty or manual request
- Majority ratio determines outcome

Instructions

When the user invokes this skill:

Load MCP Tools (Required first)

The Ouroboros MCP tools are often registered as deferred tools that must be explicitly loaded before use. You MUST perform this step before proceeding.

Use the ToolSearch tool to find and load the evaluate MCP tool:
```
ToolSearch query: "+ouroboros evaluate"
```
The tool will typically be named mcp__plugin_ouroboros_ouroboros__ouroboros_evaluate (with a plugin prefix). After ToolSearch returns, the tool becomes callable.
If ToolSearch finds the tool → proceed with the MCP-based evaluation below. If not → skip to Fallback section.

IMPORTANT: Do NOT skip this step. Do NOT assume MCP tools are unavailable just because they don't appear in your immediate tool list. They are almost always available as deferred tools that need to be loaded first.

Evaluation Steps

Determine what to evaluate:
- If session_id provided: Use it directly
- If no session_id: Check conversation for recent execution session IDs
Gather the artifact to evaluate:
- If user specifies a file: Read it with Read tool
- If recent execution output exists in conversation: Use that
- Ask user if unclear what to evaluate

Call the ouroboros_evaluate MCP tool:

Tool: ouroboros_evaluate
Arguments:
  session_id: <session ID>
  artifact: <the code/output to evaluate>
  seed_content: <original seed YAML, if available>
  acceptance_criterion: <specific AC to check, optional>
  artifact_type: "code"  (or "docs", "config")
  trigger_consensus: false  (true if user requests Stage 3)

Present results clearly:
- Show each stage's pass/fail status
- Highlight the final approval decision
- If rejected, explain the failure reason
- Suggest fixes if evaluation fails
- Always end with a 📍 suggestion based on the outcome:
  - APPROVED: 📍 Done! Your implementation passes all checks. Optional: ooo evolve to iteratively refine
  - REJECTED at Stage 1 (mechanical, code_changes_detected: true): 📍 Next: Fix the build/test failures above, then ooo evaluate — or ooo ralph for automated fix loop
  - REJECTED at Stage 1 (mechanical, code_changes_detected: false): 📍 Next: Run ooo run first to produce code, then ooo evaluate
  - REJECTED at Stage 2 (semantic): 📍 Next: ooo run to re-execute with fixes — or ooo evolve for iterative refinement
  - REJECTED at Stage 3 (consensus): 📍 Next: ooo interview to re-examine requirements — or ooo unstuck to challenge assumptions

Fallback (No MCP Server)

If the MCP server is not available, use the ouroboros:evaluator agent to perform a prompt-based evaluation:

Delegate to ouroboros:evaluator agent
The agent performs qualitative evaluation based on the seed spec
Results are advisory (no numerical scoring without Python core)

Example

User: /ouroboros:evaluate sess-abc-123

Evaluation Results
============================================================
Final Approval: APPROVED
Highest Stage Completed: 2

Stage 1: Mechanical Verification
  [PASS] lint: No issues found
  [PASS] build: Build successful
  [PASS] test: 12/12 tests passing

Stage 2: Semantic Evaluation
  Score: 0.85
  AC Compliance: YES
  Goal Alignment: 0.90
  Drift Score: 0.08

📍 Done! Your implementation passes all checks. Optional: `ooo evolve` to iteratively refine

evaluate

/ouroboros:evaluate

Usage

How It Works

More from q00/ouroboros

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