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Goal Test

CommunityPopular
undefined-ui
goal-test

Turn a vague task into a testable definition of done and generate an executable goal-test script for it, optionally with a bounded retry loop around a headless agent. Use when the user wants to run an agent in a loop, asks how to know when an agent task is finished, or says a goal like "improve X" needs to become checkable. Do NOT use for building eval suites over many cases — that is evals-bootstrap.

Overview

Publisherundefined-ui
Repositorysecond-brain-os
Skill namegoal-test
Stars
1K
Forks
166
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 undefined-ui on GitHub. Read the source before you install it.

Installation

Install the Goal Test 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/undefined-ui/second-brain-os/tree/main/plugins/agents-course/skills/goal-test
  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/undefined-ui/second-brain-os.git /tmp/second-brain-os
mkdir -p .claude/skills
cp -r /tmp/second-brain-os/plugins/agents-course/skills/goal-test .claude/skills/goal-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Goal Test 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 Goal Test 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 Goal Test 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.

Write done as a script

Theory: The four parts of a loop and the goal test build page. "Improve the error handling" cannot terminate a loop, because nothing can ever say it is finished. The goal must be phrased so that a program — not a person, not the model — returns true or false against it.

Workflow

  1. Extract the claim. Ask what the user wants true at the end, then restate it as verifiable facts: which command exits 0, which file exists, which string appears, which number crosses which threshold. If a part cannot be checked by a program, say so and negotiate it down to what can.
  2. Prefer checks that already exist. The test suite, the linter, the build, the type checker. A goal test that shells out to make test is better than one that reimplements it.
  3. Write goal-test.sh (or .py if the checks are easier there) in the project root: runs every check, prints one line per check with pass/fail, exits 0 only when all pass. Keep it under ~40 lines; it must run in seconds and be safe to run repeatedly.
  4. Run it now. It should fail before the work is done — a goal test that passes on the current state is testing nothing. Show the failing output.
  5. Offer the loop. If the user wants the agent driven until done, wrap it:
bash
#!/usr/bin/env bash
# goal-loop.sh — run a headless agent until the goal test passes or attempts run out.
set -u
MAX_ATTEMPTS=5
for i in $(seq 1 "$MAX_ATTEMPTS"); do
  FEEDBACK=$(./goal-test.sh 2>&1) && { echo "done in $i attempt(s)"; exit 0; }
  claude -p "Goal: <the goal>. The goal test currently fails with:
$FEEDBACK
Fix the code so the goal test passes." \
    --permission-mode acceptEdits --output-format json > ".attempt-$i.json"
done
echo "goal test still failing after $MAX_ATTEMPTS attempts — falling back to a human"
exit 1

Adjust the agent command to whatever CLI the user runs. Keep the three brakes visible and named: the checker outside the model (goal-test.sh), the stop rule (test passes), the budget (MAX_ATTEMPTS, plus a dollar cap read from the JSON output if they want one).

Rules

  • The checker never asks the model's opinion; self-review is not a checker.
  • Feed the checker's output back verbatim — the failure text is the prompt.
  • If the user cannot state done as a check, they do not have a loopable task yet; tell them that plainly and suggest an interactive session instead.

Frequently asked questions

What does the Goal Test AI skill do?

Turn a vague task into a testable definition of done and generate an executable goal-test script for it, optionally with a bounded retry loop around a headless agent. Use when the user wants to run an agent in a loop, asks how to know when an agent task is finished, or says a goal like "improve X" needs to become checkable. Do NOT use for building eval suites over many cases — that is evals-bootstrap.

Why use Goal Test on TypingMind?

Because you install it once and use it with any model. Goal Test 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 Goal Test in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/undefined-ui/second-brain-os/tree/main/plugins/agents-course/skills/goal-test. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Goal Test?

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 Goal Test?

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

Is the Goal Test AI skill free?

Yes. It is published on GitHub by undefined-ui 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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