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Evaluate Presets

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mikeyobrien
evaluate-presets

Use when testing Ralph's hat collection presets, validating preset configurations, or auditing the preset library for bugs and UX issues.

Overview

Publishermikeyobrien
Repositoryralph-orchestrator
Skill nameevaluate-presets
Stars
3.2K
Forks
299
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 mikeyobrien on GitHub. Read the source before you install it.

Installation

Install the Evaluate Presets 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/mikeyobrien/ralph-orchestrator/tree/main/.claude/skills/evaluate-presets
  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/mikeyobrien/ralph-orchestrator.git /tmp/ralph-orchestrator
mkdir -p .claude/skills
cp -r /tmp/ralph-orchestrator/.claude/skills/evaluate-presets .claude/skills/evaluate-presets
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evaluate Presets 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 Presets 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 Presets 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.

Evaluate Presets

Overview

Systematically test all hat collection presets using shell scripts. Direct CLI invocation—no meta-orchestration complexity.

When to Use

  • Testing preset configurations after changes
  • Auditing the preset library for quality
  • Validating new presets work correctly
  • After modifying hat routing logic

Quick Start

Evaluate a single preset:

bash
./tools/evaluate-preset.sh tdd-red-green claude

Evaluate all presets:

bash
./tools/evaluate-all-presets.sh claude

Arguments:

  • First arg: preset name (without .yml extension)
  • Second arg: backend (claude or kiro, defaults to claude)

Bash Tool Configuration

IMPORTANT: When invoking these scripts via the Bash tool, use these settings:

  • Single preset evaluation: Use timeout: 600000 (10 minutes max) and run_in_background: true
  • All presets evaluation: Use timeout: 600000 (10 minutes max) and run_in_background: true

Since preset evaluations can run for hours (especially the full suite), always run in background mode and use the TaskOutput tool to check progress periodically.

Example invocation pattern:

Bash tool with:
  command: "./tools/evaluate-preset.sh tdd-red-green claude"
  timeout: 600000
  run_in_background: true

After launching, use TaskOutput with block: false to check status without waiting for completion.

What the Scripts Do

evaluate-preset.sh

  1. Loads test task from tools/preset-test-tasks.yml (if yq available)
  2. Creates merged config with evaluation settings
  3. Runs Ralph with --record-session for metrics capture
  4. Captures output logs, exit codes, and timing
  5. Extracts metrics: iterations, hats activated, events published

Output structure:

.eval/
├── logs/<preset>/<timestamp>/
│   ├── output.log          # Full stdout/stderr
│   ├── session.jsonl       # Recorded session
│   ├── metrics.json        # Extracted metrics
│   ├── environment.json    # Runtime environment
│   └── merged-config.yml   # Config used
└── logs/<preset>/latest -> <timestamp>

evaluate-all-presets.sh

Runs all 12 presets sequentially and generates a summary:

.eval/results/<suite-id>/
├── SUMMARY.md              # Markdown report
├── <preset>.json           # Per-preset metrics
└── latest -> <suite-id>

Presets Under Evaluation

PresetTest Task
tdd-red-greenAdd is_palindrome() function
adversarial-reviewReview user input handler for security
socratic-learningUnderstand HatRegistry
spec-drivenSpecify and implement StringUtils::truncate()
mob-programmingImplement a Stack data structure
scientific-methodDebug failing mock test assertion
code-archaeologyUnderstand history of config.rs
performance-optimizationProfile hat matching
api-designDesign a Cache trait
documentation-firstDocument RateLimiter
incident-responseRespond to "tests failing in CI"
migration-safetyPlan v1 to v2 config migration

Interpreting Results

Exit codes from evaluate-preset.sh:

  • 0 — Success (LOOP_COMPLETE reached)
  • 124 — Timeout (preset hung or took too long)
  • Other — Failure (check output.log)

Metrics in metrics.json:

  • iterations — How many event loop cycles
  • hats_activated — Which hats were triggered
  • events_published — Total events emitted
  • completed — Whether completion promise was reached

Hat Routing Performance

Critical: Validate that hats get fresh context per Tenet #1 ("Fresh Context Is Reliability").

What Good Looks Like

Each hat should execute in its own iteration:

Iter 1: Ralph → publishes starting event → STOPS
Iter 2: Hat A → does work → publishes next event → STOPS
Iter 3: Hat B → does work → publishes next event → STOPS
Iter 4: Hat C → does work → LOOP_COMPLETE

Red Flags (Same-Iteration Hat Switching)

BAD: Multiple hat personas in one iteration:

Iter 2: Ralph does Blue Team + Red Team + Fixer work
        ^^^ All in one bloated context!

How to Check

1. Count iterations vs events in session.jsonl:

bash
# Count iterations
grep -c "_meta.loop_start\|ITERATION" .eval/logs/<preset>/latest/output.log

# Count events published
grep -c "bus.publish" .eval/logs/<preset>/latest/session.jsonl

Expected: iterations ≈ events published (one event per iteration) Bad sign: 2-3 iterations but 5+ events (all work in single iteration)

2. Check for same-iteration hat switching in output.log:

bash
grep -E "ITERATION|Now I need to perform|Let me put on|I'll switch to" \
    .eval/logs/<preset>/latest/output.log

Red flag: Hat-switching phrases WITHOUT an ITERATION separator between them.

3. Check event timestamps in session.jsonl:

bash
cat .eval/logs/<preset>/latest/session.jsonl | jq -r '.ts'

Red flag: Multiple events with identical timestamps (published in same iteration).

Routing Performance Triage

PatternDiagnosisAction
iterations ≈ events✅ GoodHat routing working
iterations << events⚠️ Same-iteration switchingCheck prompt has STOP instruction
iterations >> events⚠️ Recovery loopsAgent not publishing required events
0 events❌ BrokenEvents not being read from JSONL

Root Cause Checklist

If hat routing is broken:

  1. Check workflow prompt in hatless_ralph.rs:

    • Does it say "CRITICAL: STOP after publishing"?
    • Is the DELEGATE section clear about yielding control?
  2. Check hat instructions propagation:

    • Does HatInfo include instructions field?
    • Are instructions rendered in the ## HATS section?
  3. Check events context:

    • Is build_prompt(context) using the context parameter?
    • Does prompt include ## PENDING EVENTS section?

Autonomous Fix Workflow

After evaluation, delegate fixes to subagents:

Step 1: Triage Results

Read .eval/results/latest/SUMMARY.md and identify:

  • ❌ FAIL → Create code tasks for fixes
  • ⏱️ TIMEOUT → Investigate infinite loops
  • ⚠️ PARTIAL → Check for edge cases

Step 2: Dispatch Task Creation

For each issue, spawn a Task agent:

"Use /code-task-generator to create a task for fixing: [issue from evaluation]
Output to: .ralph/tasks/preset-fixes/"

Step 3: Dispatch Implementation

For each created task:

"Use /code-assist to implement: .ralph/tasks/preset-fixes/[task-file].code-task.md
Mode: auto"

Step 4: Re-evaluate

bash
./tools/evaluate-preset.sh <fixed-preset> claude

Prerequisites

  • yq (optional): For loading test tasks from YAML. Install: brew install yq
  • Cargo: Must be able to build Ralph

Related Files

  • tools/evaluate-preset.sh — Single preset evaluation
  • tools/evaluate-all-presets.sh — Full suite evaluation
  • tools/preset-test-tasks.yml — Test task definitions
  • tools/preset-evaluation-findings.md — Manual findings doc
  • presets/ — The preset collection being evaluated

Frequently asked questions

What does the Evaluate Presets AI skill do?

Use when testing Ralph's hat collection presets, validating preset configurations, or auditing the preset library for bugs and UX issues.

Why use Evaluate Presets on TypingMind?

Because you install it once and use it with any model. Evaluate Presets 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 Presets in TypingMind?

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

Which AI models can use Evaluate Presets?

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 Presets?

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

Is the Evaluate Presets AI skill free?

Yes. It is published on GitHub by mikeyobrien 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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