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Genesis Evals

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danielmeppiel
genesis-evals

Use this skill to run the genesis maintainer-side eval suite against a target model (default: claude-opus-4.7). Activate when validating a genesis PR, when changing the genesis catalogue (architectural-patterns, primitives, design-patterns, refactor-patterns, composition-substrate, pattern-tradeoffs, SKILL.md), or when the operator asks to "run evals" or "regenerate the eval matrix". This skill orchestrates parallel cold sub-agent spawns via the harness's task tool, scores deterministically, and converges P>=0.8 / N>=0.8 / R==1.0 within max 3 iteration loops. This skill is contributor-only -- it lives under dev/skills/ (OUTSIDE .apm/) and is NOT shipped inside the user-facing skills/genesis/ bundle (BUNDLE LEAKAGE discipline). See "Why this lives outside .apm/" below.

Overview

Publisherdanielmeppiel
Repositorygenesis
Skill namegenesis-evals
Stars
77
Forks
11
Bundled files
19
LicenseApache-2.0
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.

  • 19 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Genesis Evals 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.
    https://github.com/danielmeppiel/genesis/tree/main/dev/skills/genesis-evals
  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/danielmeppiel/genesis.git /tmp/genesis
mkdir -p .claude/skills
cp -r /tmp/genesis/dev/skills/genesis-evals .claude/skills/genesis-evals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Genesis Evals 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 Genesis Evals 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 Genesis Evals 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.

genesis-evals: maintainer-side eval runner

Run the genesis self-eval suite. Steers the parent LLM session to orchestrate cold sub-agent spawns, capture responses, score deterministically, and report convergence.

Why this lives outside .apm/

Genesis ships to USERS via npx / apm install. Eval scenarios LOOK LIKE real user requests (that is the point). Colocating them under skills/genesis/evals/ would risk DISPATCH CONTAMINATION (an over-eager harness loader pulling scenario prompts into the active context) and PAYLOAD BLOAT for users who never run evals.

We also keep this OUTSIDE .apm/ because APM treats .apm/ as the publishable source root: its local-content scanner picks up anything under .apm/skills/ regardless of dev-marker, so apm pack --format plugin would leak this maintainer-only skill into the shipped artifact. Living under dev/skills/ keeps it scanner-invisible while still letting apm install --dev deploy it via the local-path devDependency in the root apm.yml.

This is the inverse of PHANTOM DEPENDENCY (referenced-but-not-bundled): BUNDLE LEAKAGE (bundled-but-not-consumed-at-runtime). See skills/genesis/assets/composition-substrate.md "Anti-patterns flagged at this step".

When to activate

  • Validating a genesis PR before merge
  • Any change to a file under skills/genesis/ (catalogue or SKILL.md)
  • Operator says "run evals", "regenerate eval matrix", "score on Opus"
  • Adding a new scenario (run validate first)

Hard rules

  • The model: field in every scenario YAML is REQUIRED. The runner REFUSES to spawn if missing. No silent default. The model is the single biggest variable in eval results.
  • Pre-spawn: ALWAYS call spawn_record.py to write the immutable <id>__<half>.spawn.json BEFORE invoking the harness's task tool. This is the source of truth for "what we asked for".
  • Cold spawn: each (scenario, half) is a SEPARATE task-tool call with fresh context. Never reuse a session across scenarios.
  • Determinism: scoring is python (score_run.py), not LLM-judged. Pass gates are substring matches against the schema.
  • Scenarios are FROZEN once landed. Removing a scenario requires setting retired_in: <version> (never deletion).
  • After 3 iteration loops without convergence, escalate via B10 HUMAN CHECKPOINT. Do NOT loop indefinitely.

Process

   1 validate scenarios   <-- python validate_scenarios.py
        v
   2 mint run-id          <-- yyyymmddTHHMMSSZ
        v
   3 fan-out spawns        <-- for each scenario:
        v                       a) spawn_record.py for each half
        v                       b) task-tool cold spawn (parallel)
        v                       c) write child reply to <id>__<half>.response.txt
        v
   4 score                <-- python score_run.py
        v
   5 read summary.md
        v
   6 converged?  --YES--> done; report run-id + summary path
        |
        NO
        v
   7 loop count < 3?
        |
        +-- YES: identify failing scenario IDs; goto step 3 (failures only)
        |
        +-- NO: B10 HUMAN CHECKPOINT
                (declare ITERATION REQUIRED -- design implicated)

Step 1 -- validate

python dev/skills/genesis-evals/scripts/validate_scenarios.py \
  --scenarios-dir dev/skills/genesis-evals/scenarios \
  --schema        dev/skills/genesis-evals/schema/scenario.schema.json

If exit non-zero, STOP. Fix the schema/yaml violations before any spawn. No partial runs.

Step 2 -- mint run-id

RUN_ID=$(date -u +%Y%m%dT%H%M%SZ)
RUNS_DIR=dev/skills/genesis-evals/runs
mkdir -p "$RUNS_DIR/$RUN_ID"

Step 3 -- fan-out spawns

For EACH scenario YAML in scenarios/:

Determine halves

  • category: P -> two halves: with and without
  • category: N -> one half: single (loaded_skills as declared)
  • category: R -> one half: single

Per (scenario, half), in this order

  1. Record the spawn intent (deterministic, pre-spawn):

    python dev/skills/genesis-evals/scripts/spawn_record.py \
      --scenario dev/skills/genesis-evals/scenarios/<id>.yml \
      --half     <with|without|single> \
      --run-id   "$RUN_ID" \
      --runs-dir "$RUNS_DIR"

    This writes <runs-dir>/<run-id>/<id>__<half>.spawn.json. If the script exits non-zero, the scenario is REJECTED (no model field, or retired). Skip this scenario; do NOT spawn.

  2. Cold-spawn via the harness task tool, with:

    • agent_type: general-purpose (or harness equivalent that supports a fresh context window)
    • model: read from the spawn.json requested_model field -- PASS THIS EXPLICITLY to the task-tool call. Do NOT rely on the parent's default.
    • prompt: read from spawn.json prompt field, VERBATIM. No prefix, no suffix, no parent-context bleed.
    • For with half (P category, includes genesis in loaded_skills_requested): prepend a single line instruction telling the child it has the genesis skill available and should consult it. For without half: no genesis instruction.
    • For N and R single half: same treatment as with if genesis is in loaded_skills_requested.
  3. Capture the child's reply to: <runs-dir>/<run-id>/<id>__<half>.response.txt The full text, verbatim. No truncation. No paraphrase.

Parallelism

Genesis itself names A1 PANEL with B1 fan-out as the right pattern for this shape. Spawn as many independent (scenario, half) jobs in parallel as the harness permits. Do NOT serialize unless forced.

Step 4 -- score

python dev/skills/genesis-evals/scripts/score_run.py \
  --run-id        "$RUN_ID" \
  --runs-dir      "$RUNS_DIR" \
  --scenarios-dir dev/skills/genesis-evals/scenarios

Writes <runs-dir>/<run-id>/summary.md. Exit code: 0 if converged, 1 if not, 2 on infrastructure error.

Step 5 -- read summary

view the summary.md. Note per-category ratios.

Step 6 -- decide convergence

CategoryGateFailure shape
Pratio >= 0.80with-skill missing the catalogue label OR without-skill leaking it
Nratio >= 0.80over-eager activation: genesis vocabulary leaking into off-topic responses
Rratio == 1.00regression of a previously-fixed behaviour (PR #4 substrate gates)

If ALL three pass: STOP. Report run-id, summary.md path, ratios.

Step 7 -- iterate (cap = 3)

If NOT converged AND loop count < 3:

  1. Identify failing scenario IDs from summary.md
  2. Re-spawn ONLY those failures (same scenario YAMLs, new run-id)
  3. Goto step 4

If loop count == 3 and still not converged: B10 HUMAN CHECKPOINT. Report which scenarios failed twice (genuine signal, not noise) and which architectural surface they implicate. Do NOT auto-edit genesis catalogue files.

Updating a PR description with the matrix

After convergence, paste the contents of summary.md into the PR body under a "Verification" section. Replace any prior ad-hoc matrix.

Adding a new scenario

  1. Pick the next ord for the category: <p|n|r>-<NNN>-<slug>.yml
  2. Set frozen_since to the current genesis version (read apm.yml)
  3. Run validate_scenarios.py
  4. Land in the same PR as the genesis change it gates

Gate-authoring tips (lessons from RUN_ID=20260426T104629Z)

  • Forbidden lists must target ACTIVATION vocabulary, not SCOPE vocabulary. A polite-decline response correctly names the skill's scope ("genesis is for designing primitive modules -- not relevant here"). Words like primitive module or agentic primitive appear in BOTH activation AND decline, so they fail to discriminate. Forbid only words that ONLY appear on activation: Step 1, composition substrate, design artifact, codified pattern labels (R1 SPLIT, A10 GOVERNED, A1 PANEL).
  • Required-substring lists must be one-of, not all-of, when testing for "the catalogue produced ANY structured output". Demanding both SoC AND R1 SPLIT is over-specification. Pick the single most discriminating label and require only that. The scorer is substring_all (intersection); express alternatives by minimising the set, not by enumerating synonyms.
  • The "without-genesis" half is NOT a clean baseline. The cwd contains genesis source files; sub-agents may read and cite them. This is acceptable for gate evaluation (gates are substring checks on SPECIFIC vocabulary), but do not treat without-half responses as evidence of "what a fresh LLM would do without the skill".
  • File-naming MUST match scenario id exactly. Saved responses are read as <scenario-id>__<half>.response.txt. Trailing slugs in filenames break the scorer silently.

Retiring a scenario

Set retired_in: <version>. The runner will skip it. NEVER delete the file (provenance / audit trail).

Setup (one-time)

pip install -r dev/skills/genesis-evals/requirements.txt

(deps: pyyaml, jsonschema. Maintainer-side only. Users never see this requirements.txt.)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Genesis Evals AI skill do?

Use this skill to run the genesis maintainer-side eval suite against a target model (default: claude-opus-4.7). Activate when validating a genesis PR, when changing the genesis catalogue (architectural-patterns, primitives, design-patterns, refactor-patterns, composition-substrate, pattern-tradeoffs, SKILL.md), or when the operator asks to "run evals" or "regenerate the eval matrix". This skill orchestrates parallel cold sub-agent spawns via the harness's task tool, scores deterministically, and converges P>=0.8 / N>=0.8 / R==1.0 within max 3 iteration loops. This skill is contributor-only...

Why use Genesis Evals on TypingMind?

Because you install it once and use it with any model. Genesis Evals 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 Genesis Evals in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielmeppiel/genesis/tree/main/dev/skills/genesis-evals. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Genesis Evals?

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 Genesis Evals?

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

Is the Genesis Evals AI skill free?

Yes. It is published on GitHub by danielmeppiel under the Apache-2.0 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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