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Evaluate

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Q00
evaluate

Evaluate execution with three-stage verification pipeline

Overview

PublisherQ00
Repositoryouroboros
Skill nameevaluate
Stars
6K
Forks
605
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Q00 on GitHub. Read the source before you install it.

Installation

Install the Evaluate AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Q00/ouroboros.git /tmp/ouroboros
mkdir -p .claude/skills
cp -r /tmp/ouroboros/skills/evaluate .claude/skills/evaluate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evaluate in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evaluate on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evaluate is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/ouroboros:evaluate

Evaluate an execution session using the three-stage verification pipeline.

Usage

/ouroboros:evaluate <session_id> [artifact]

Trigger keywords: "evaluate this", "3-stage check"

How It Works

The evaluation pipeline runs three progressive stages:

  1. Stage 1: Mechanical Verification ($0 cost)

    • Lint checks, build validation, test execution
    • Static analysis, coverage measurement
    • Fails fast if mechanical checks don't pass
  2. Stage 2: Semantic Evaluation (Standard tier)

    • AC compliance assessment
    • Goal alignment scoring
    • Drift measurement
    • Reasoning explanation
  3. 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.

  1. Use the active runtime's tool-discovery capability to find and load the evaluate MCP tools:
    tool discovery query: "+ouroboros evaluate"
  2. The tool will typically be named mcp__plugin_ouroboros_ouroboros__ouroboros_start_evaluate (with a plugin prefix). After runtime tool discovery returns, the tool becomes callable.
  3. If the tool is callable — already exposed, or loaded by discovery — proceed with the MCP-based evaluation below. An empty discovery result for an already-exposed tool is expected, not a failure. Skip to the Fallback section only if the tool is genuinely absent (no Ouroboros MCP server).

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.

CRITICAL — deferred-schema guard (prevents "Invalid tool parameters"): This skill can call ouroboros_start_evaluate after a fresh turn. A deferred tool's schema loaded on one turn is NOT guaranteed to still be loaded on the next. If you call it while its schema is not loaded in the current turn, the runtime rejects the call with "Invalid tool parameters" before it reaches the server. Therefore: immediately before EVERY ouroboros_start_evaluate call in this skill, re-run tool discovery query: "+ouroboros evaluate" (idempotent — a no-op when already loaded). If the load returns no matching tool (and the tool is not already callable — an empty load for an already-exposed tool is an expected no-op, not absence), switch to the documented fallback instead of retrying the failing call.

Evaluation Steps

  1. Determine what to evaluate:

    • If session_id provided: Use it directly
    • If no session_id: Check conversation for recent execution session IDs
  2. 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

2.5. Acting verification — reproduce and OBSERVE (do not skip for behaviour-bearing work): Stage 1 already runs mechanical checks (build/test). Go further when the runtime exposes acting tools — computer-use / browser, Bash/shell, file reads: don't just reason over the diff, run the result and observe the real effect (the command's output, the endpoint's response, the rendered UI via a screenshot). Do it via a dedicated verification sub-agent to keep the main session lean — or inline in the main session where the runtime restricts sub-agent spawning (the observation is what matters; the delegation is only an optimization). Probe the acceptance criteria against the ACTUAL observable behaviour and the adversarial classes (misleading_output, hung_command, stale_state, dirty_worktree, …). Feed the captured evidence (commands, outputs, artifact paths) into the evaluate call as part of the artifact. If acting tools are unavailable, note that behaviour was not observed and evaluate on the text alone.

  1. Call the background ouroboros_start_evaluate MCP tool so rejected verdicts can continue through the configured Ralph convergence chain:

    Tool: ouroboros_start_evaluate
    Arguments:
      session_id: <session ID>
      artifact: <the code/output to evaluate, plus observed-behaviour evidence from 2.5>
      seed_content: <original seed YAML, if available>
      acceptance_criterion: <specific AC to check, optional>
      artifact_type: "code"  (or "docs", "config")
      working_dir: <absolute project root, recommended>
      trigger_consensus: false  (true if user requests Stage 3)
      auto_evolve: <optional override; omit to use execution.auto_evolve>

    working_dir controls both Stage 1 command execution and Stage 2 source-file visibility. Pass the absolute project root whenever available; if omitted, the MCP handler falls back to the registered brownfield default, seed project metadata, then the MCP server cwd.

  2. Observe the returned evaluation job. If its terminal result contains chained_ralph_job_id, follow that Ralph job to terminal before presenting the convergence outcome. A missing Seed produces chained_ralph_skipped: seed_unavailable; preserve the rejected verdict and explain that automatic continuation was safely skipped. In OpenCode plugin mode, auto_evolve=true intentionally returns this pollable parent-owned job; with automatic evolution disabled, the plugin child remains the terminal surface and job_id is None.

  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 state breadcrumb based on the outcome:
      • APPROVED: ◆ Evaluation approved → next: accept, or ooo evolve to iteratively refine
      • REJECTED at Stage 1 (mechanical, code_changes_detected: true): ◆ Current state → 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): ◆ Current state → next: Run ooo run first to produce code, then ooo evaluate
      • REJECTED at Stage 2 (semantic): ◆ Current state → next: ooo run to re-execute with fixes — or ooo evolve for iterative refinement
      • REJECTED at Stage 3 (consensus): ◆ Current state → 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:

  1. Delegate to ouroboros:evaluator agent
  2. The agent performs qualitative evaluation based on the seed spec
  3. 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

◆ Evaluation approved → next: accept, or `ooo evolve` to iteratively refine

RFC #1392 State Breadcrumb Footer

Your final response MUST end with exactly one breadcrumb footer line:

◆ <current state> → next: <recommended action>

Derive <current state> from live session state via ouroboros_session_status when that MCP projection is available; otherwise derive it from this skill's actual outcome. Never use a linear Step N of M footer because Ouroboros is an evolutionary loop. When the next action is genuinely a choice, list 2-3 honest options in the next: clause. The breadcrumb line must be the last line of the response.

Frequently asked questions

What does the Evaluate AI skill do?

Evaluate execution with three-stage verification pipeline

Why use Evaluate on TypingMind?

Because you install it once and use it with any model. Evaluate is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evaluate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Q00/ouroboros/tree/main/skills/evaluate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Evaluate?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evaluate?

As many as you like. As long as a model supports skills, you can use Evaluate with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evaluate AI skill free?

Yes. It is published on GitHub by Q00 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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