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Define Goal

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openai
define-goal

Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.

Overview

Publisheropenai
Repositoryskills
Skill namedefine-goal
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Bundled files
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  • 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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Define Goal 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/openai/skills/tree/main/skills/.curated/define-goal
  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/openai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/.curated/define-goal .claude/skills/define-goal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Define Goal 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 Define Goal 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 Define Goal 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.

Define Goal

Overview

Shape the user's intent into an objective an agent can pursue honestly. Prefer measurable outcomes, explicit evidence, and bounded scope over activity descriptions.

This skill covers goal definition and goal-tool creation only. Do not create intermediate planning artifacts, durable snapshots, ledgers, decision logs, or resume files from this skill.

Workflow

  1. Confirm that goal definition is actually needed.

    • Use this skill when the user asks for $define-goal, asks to create or set a goal, asks for the goal tool, or wants help turning an intention into a clear objective.
    • If the user only asks for ordinary implementation work, do the work directly instead of forcing goal creation.
  2. Restate the likely goal in concrete terms. A usable goal names:

    • the specific outcome that will be true
    • the main artifact, system, repo, environment, or user-facing behavior involved
    • how completion will be verified
    • what is in scope
    • what is out of scope when ambiguity would matter
    • the stop condition for asking the user instead of grinding
  3. Make it quantitative when the domain supports it. Prefer numbers that represent real success, not decorative precision:

    • pass/fail validators: exact tests, checks, CI jobs, evals, commands, or acceptance criteria
    • quality thresholds: latency, error rate, cost, accuracy, recall, precision, coverage, flake rate, bundle size, memory, uptime, completion rate, or manual review criteria
    • artifact constraints: file paths, affected modules, allowed commands, output formats, target environments, deadlines, or maximum blast radius
    • evidence counts: number of reproduced failures, successful reruns, reviewed examples, migrated records, addressed comments, or verified cases
  4. Repair weak goals before setting them.

    • Rewrite vague goals into measurable objectives when local context makes the rewrite safe.
    • Ask one concise clarification question when the missing detail changes the intended outcome or validation.
    • Reject pure activity goals such as "make progress," "keep investigating," "improve things," or "work on X" unless they are sharpened into a verifiable outcome.
  5. Check active goal state before creating a goal.

    • Call get_goal.
    • If there is no active goal and the objective meets the quality bar, call create_goal.
    • If there is an active goal that still matches the user's intent, continue using it instead of creating a duplicate.
    • If there is an active goal that conflicts with the new request, ask whether to finish the current goal, mark it complete if done, or start a separate goal-backed thread.
  6. Create the goal only after it passes the quality bar.

    • Use a single concise objective string.
    • Include the verification evidence in the objective itself.
    • Include scope bounds when they constrain the work.
    • Include a token budget only when the user explicitly requested one.
    • Do not call create_goal for an ordinary multi-step task unless the user explicitly asked for goal-backed work.

Goal Quality Bar

Before create_goal, the objective should answer:

  • What concrete thing will be true when this is done?
  • What evidence will prove it?
  • What quantitative or binary threshold defines success?
  • What scope boundaries matter?
  • What should cause the agent to stop and ask?

Good:

Reduce checkout API p95 latency below 250 ms for the documented slow path by making the smallest safe server-side change, then verify with npm run test:checkout and the existing local latency benchmark showing p95 under 250 ms across 3 consecutive runs.

Good:

Resolve the open review comments on PR 123 that request code changes, update only the affected auth files and tests, and verify with the targeted auth test command plus gh pr view 123 showing no unresolved change-request threads.

Weak:

Make checkout faster.

Weak:

Keep investigating the PR comments.

Quantification Heuristics

  • For bugs, define success as reproduction first, fix second, and a failing-then-passing validator when possible.
  • For tests, name the exact command and required pass condition.
  • For performance, name the metric, target threshold, measurement method, and number of runs.
  • For quality work, define an observable acceptance bar such as reviewed examples, lint/typecheck/test pass, or user-approved artifact.
  • For research, define the decision the research must enable, the sources or systems in scope, and the evidence standard.
  • For operations, define healthy state, monitoring window, failure threshold, and rollback or escalation trigger.

Clarifying Questions

Ask only when a reasonable rewrite would risk pursuing the wrong outcome. Keep the question short and oriented around the missing validator or scope boundary.

Useful question shapes:

  • "What metric should define success here: latency, cost, accuracy, or user-visible behavior?"
  • "Which environment should I verify against: local, staging, or production?"
  • "What is the minimum evidence you want before I mark this goal complete?"

If the user cannot provide a metric, propose the most honest binary validator available and ask for confirmation.

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 Define Goal AI skill do?

Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.

Why use Define Goal on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openai/skills/tree/main/skills/.curated/define-goal. 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 Define Goal?

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 Define Goal?

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

Is the Define Goal AI skill free?

It is published on GitHub by openai. Check the repository for licensing terms. 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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