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Goal Draft

OrganizationPopular
QwenLM
goal-draft

Turn a fuzzy intention into a /goal objective the Goal verifier can actually judge - one outcome, numbered binary "Done when" checks that leave evidence in the transcript, guardrails, a budget, and a block protocol. Use when the user wants to set or define a goal, asks whether a goal is good enough, or says "keep going until X". Usage - /goal-draft <what you want done>, or /goal-draft <existing goal> to tighten it. This skill only writes the objective; it never starts the work.

Overview

PublisherQwenLM
Repositoryqwen-code
Skill namegoal-draft
Stars
27.9K
Forks
3.1K
Bundled files
1
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.

  • 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 QwenLM on GitHub. Read the source before you install it.

Installation

Install the Goal Draft 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/QwenLM/qwen-code.git /tmp/qwen-code
mkdir -p .claude/skills
cp -r /tmp/qwen-code/packages/core/src/skills/bundled/goal-draft .claude/skills/goal-draft
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Goal Draft 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 Draft 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 Draft 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.

/goal-draft — write a Goal the verifier can judge

You are already inside the loaded goal-draft skill — do not call the skill tool to invoke it again; start with Step 0.

You are drafting the text for /goal set. You are NOT doing the work the goal describes. Do not edit files, do not run the checks, do not start on the task. Deliver a concise objective for approval or a command the user can run; if essential information is missing, deliver only a draft marked "Needs clarification".

How Goals are judged (why the format below matters)

An active Goal is re-fed to the model every turn, and its completion is judged by an independent verifier that sees ONLY transcript evidence, read from the most recent record backwards until its request is full:

  • Visible assistant output, tool results, and the user's own messages recorded for the Goal count as evidence; a user message proves only what the user said, chose, or approved. The objective itself and hidden reasoning do not, and in a long Goal the oldest records no longer reach the verifier.
  • delivered_output evidence proves only that text was printed. It cannot prove that tests passed, files changed, or remote state changed — those need a tool result in the transcript (an external_fact).
  • A claim that the user confirmed, chose, or approved something needs a real user message as evidence; otherwise the completion proposal is rejected.
  • Vague, subjective, or open-ended conditions never produce decisive evidence; the loop then runs until a limit is hit.

So a good objective makes the agent PRODUCE evidence at the end: run the named check immediately before proposing completion and paste the decisive output line. A check that ran long before may have to run again when completion is proposed.

Step 0 — should this be a Goal at all?

Say no, briefly, when the request is a normal one-shot task, needs a design or product judgement call, or has no way to be checked from the agent's own output. Offer to just do it, or to write a plan instead. A goal that cannot be checked is a prompt, not a goal.

Step 1 — check the active Goal

Call get_goal. If a Goal is active, preserve the user's explicit choice to edit it (same goal, tighter wording → /goal edit) or replace it (/goal set). If that choice is unclear, include it in Step 3's single round of questions; do not choose on the user's behalf. Never draft a second concurrent goal.

Step 2 — ground the draft in the workspace

Before asking anything, verify what you can with read_file, glob, and grep_search: that named files and packages exist, and what the real check commands are (package.json scripts, Makefile, CI workflow, test config). Use those exact commands in "Done when". Never invent paths, IDs, or commands; write <TODO: …> for anything you cannot confirm.

Read only what is needed to establish the scope and verification path. Stop exploring once those are grounded; do not audit the implementation, reproduce failures, run builds or tests, install dependencies, or start services while drafting. A requested new output file is a proposed destination, not evidence that the file already exists.

Step 3 — at most one round of questions

Ask with ask_user_question, 1–3 questions in one call, each with options and a recommended default. Ask only when the answer changes the check, the scope, or the budget. Typical questions:

  • Which check defines success: a test command, a build, a metric threshold, a file or state assertion?
  • Which environment: local, CI, staging?
  • What is off limits: which files, which actions (push, delete, publish)?
  • How long to try before stopping as blocked?

Rules for the questions:

  • Never ask what you could find out by reading the workspace.
  • Batch them into one ask_user_question call; do not drip one per turn.
  • Only ask things only the user can answer: what counts as done, what is off limits, how long to try.
  • If you cannot find a concrete way to verify the outcome, you MUST ask, offering 2–3 candidate checks. Do not skip this and do not invent one.

If you cannot ask (headless, or a client without prompts), use a recommended default only for nonessential choices and mark it [ASSUMPTION] in Context. An unknown success criterion, unverified command or input path, or unresolved edit-versus-replace choice stays <TODO: …>; do not invent an answer just to finish the hand-off. If essential information is still missing after the single question round, keep the draft incomplete rather than starting another round.

Step 4 — draft the objective

Use exactly these labels, in this order. Keep the whole objective on one line when you hand it over — the /goal parser joins lines with spaces, so number items instead of relying on newlines. Body text follows the user's language; labels stay English so the verifier can match them.

text
Outcome: <one sentence: what is true when done>
Done when: 1) <command> exits 0 and its output shows <…> (paste that line); 2) <file/state assertion provable via read or grep>; 3) …
Must not: <files not to touch; tests/thresholds not to weaken; irreversible actions not to take>
Budget: <user's advisory stopping agreement; otherwise stop as blocked after 20 turns, and mark that default [ASSUMPTION] in Context>
On block: propose blocked with the exact blocker and the decision a human must make; never claim completion without evidence for every Done-when item
Context: <only facts the agent cannot derive: paths, branch, environment, earlier decisions>

Rules of thumb:

  • One Outcome. Several outcomes = several goals, or a checklist file plus a single "every item in <file> is checked" goal.
  • Usually 3–5 Done-when checks suffice; use fewer when enough. Do not add checks just to reach a count, and preserve explicit user requirements.
  • Every Done-when item is binary, and at least one is tool-observable (a command with an exit code or output line, a file that exists, a grep that matches).
  • For audits and exploratory work, agree on bounded coverage and require evidence for each conclusion. Zero defects is a valid result: never require a positive defect count. Do not invent minimum scenario counts, evidence-file counts, or exploration-round quotas; include them only when the user requested them.
  • Prefer "the smallest safe change in <scope>" over open-ended refactors.
  • Put anything that must not change on the way into Must not — this is what stops the loop from deleting a failing test to "pass".
  • Keep it short: everything the agent can derive from the workspace stays out. Aim for under ~1200 characters.
  • Budget is a stopping agreement for the model, not a runtime-enforced turn or wall-clock limit. Do not claim that writing it configures a timer or changes the Goal token budget. When the user needs an enforced ceiling, tell them to set model.goalMaxTurns or model.goalMaxActiveMinutes in their settings -- never write the setting into the objective, which the Goal's own model cannot act on -- and say that either takes effect after a restart and only for Goals created afterwards -- neither bounds a Goal that is already running. Preserve a user-specified budget; otherwise mark the default [ASSUMPTION] in Context.

For example, an audit's Done-when checks can require a report covering the agreed scenarios, observed results and evidence for each scenario, and reproduction steps for each confirmed defect (or an explicit "no confirmed defects" result). Ground the scenarios and report destination before offering the objective for use.

Weak → strong

WeakStrong
make checkout fasterOutcome: checkout API p95 is below 250 ms on the documented slow path. Done when: 1) npm run bench:checkout exits 0 and prints a p95 below 250 (paste the line); 2) npm test exits 0. Must not: change the benchmark, skip tests, touch files outside src/checkout. Budget: as model guidance, stop as blocked after 20 turns. On block: report the measured p95 and what blocks it. Context: [ASSUMPTION] the 20-turn budget is the drafter's default, not the user's.
keep handling the PR commentsOutcome: every unresolved review thread on PR #123 is fixed or answered. Done when: 1) the review-threads query shows zero unresolved threads (paste the count); 2) CI on the head commit is green (paste the check summary). Must not: force-push, resolve a thread without replying to it. Budget: as model guidance, stop as blocked after 30 turns. On block: list the threads that need a maintainer decision.
clean up the auth moduleNot a goal — "clean" has no check. Ask what would be observable (zero lint warnings in src/auth? a file count? a coverage threshold?) or offer a refactor plan instead.
get the release outNot a goal as written — publishing is irreversible. Either narrow it to a checkable pre-release state (tag exists, changelog entry present, npm run release:dry-run exits 0) and put "do not publish" in Must not, or leave publishing to a human.

Step 5 — self-check, then hand off

Check every line before printing:

  1. "Done when" exists, its items are numbered, and each is binary.
  2. At least one item names a command, exit code, file, or grep pattern and asks to paste the output.
  3. No subjective adjectives as conditions (clean, better, robust, elegant, reasonable, …).
  4. No "after the user confirms/approves" as a completion condition — that belongs in On block as a decision a human must make.
  5. Budget or On block is present.
  6. Exactly one Outcome.
  7. Every path and command in Context was verified in the workspace; an unverified path or command is essential and stays <TODO: …>.
  8. Under ~1200 characters.
  9. Irreversible actions (push, delete, publish) are listed in Must not, or the user explicitly allowed them.
  10. No invented coverage quotas, required defect findings, or claims that a prose budget is enforced by the runtime; an unrequested default Budget is marked [ASSUMPTION] in Context.

If an essential item remains unresolved — an unknown success criterion, an unverified command or input path, or an unresolved edit-versus-replace choice (each written as <TODO: …>) —, print only a draft marked "Needs clarification" (in the user's language) and briefly list what is missing. Do not call propose_goal or print a runnable /goal set or /goal edit line. Stop here; the ready-objective hand-off below does not apply.

Then hand off, and nothing else:

If the propose_goal tool is available and no Goal is active, call it with the objective on one line. propose_goal refuses an objective over 1,500 characters: tighten a longer draft before calling it, never cut it off mid-check. The user approves or declines it in a dialog; only their approval sets the Goal. If they decline you will not be told why: stop, do not ask about it, and do not propose the same or a reworded objective again. After approval, acknowledge it in one sentence and end the turn — the Goal runtime starts the first Goal turn on its own.

Otherwise (a client without Goal proposal support, headless, the tool is disabled, or a Goal is active), print:

  1. The objective in a fenced code block.
  2. One line the user can run as-is: /goal set <objective on one line> (or /goal edit … when tightening the active goal). Print it as plain text with no code markers, so it can be copied verbatim.
  3. One sentence explaining that the draft has not been applied and the user must run the command to apply it; include any [ASSUMPTION] items. Do not promise a dialog when the tool is unavailable.

Do not run /goal yourself. Do not begin the task. Stop and wait for the user.

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 Goal Draft AI skill do?

Turn a fuzzy intention into a /goal objective the Goal verifier can actually judge - one outcome, numbered binary "Done when" checks that leave evidence in the transcript, guardrails, a budget, and a block protocol. Use when the user wants to set or define a goal, asks whether a goal is good enough, or says "keep going until X". Usage - /goal-draft <what you want done>, or /goal-draft <existing goal> to tighten it. This skill only writes the objective; it never starts the work.

Why use Goal Draft on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/QwenLM/qwen-code/tree/main/packages/core/src/skills/bundled/goal-draft. 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 Goal Draft?

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 Draft?

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

Is the Goal Draft AI skill free?

Yes. It is published on GitHub by QwenLM 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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