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Goal Prompt

OrganizationPopular
trailofbits
goal-prompt

Drafts copy-paste-ready /goal commands for goal mode in Claude Code and Codex. Use when the user asks to create, write, rewrite, improve, compress, clean up, or prepare a goal prompt, goal condition, /goal command, goal-mode objective, or copy-ready long-running task objective.

Overview

Publishertrailofbits
Repositoryskills
Skill namegoal-prompt
Stars
7.1K
Forks
611
Bundled files
3
LicenseCC-BY-SA-4.0
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Goal Prompt 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/trailofbits/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/goal-prompt/skills/goal-prompt .claude/skills/goal-prompt
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Goal Prompt 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 Prompt 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 Prompt 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 Prompt

/goal keeps the agent working until a completion condition is met. Both Claude Code and Codex take it as one line, max 4,000 characters. In Claude Code a small model re-judges the condition after each turn from the transcript alone — it cannot run commands.

Draft a condition that can terminate, then format it. A goal fits work bigger than one turn with a checkable finish line; chain small goals with review between them rather than writing one giant goal.

Draft

Include, joined with AND — never "or", the loop takes the cheaper branch:

  1. End state, not activity — "all legacyAuth() call sites use auth.verify()", not "migrate the auth code". An activity can be claimed; an end state is true or false.
  2. Scope to read first — the files, issue, logs, or plan to read before acting.
  3. Stated check — the exact command and its observable result ("npm test exits 0"), plus an instruction to run it and show the output; a result that never lands in the transcript does not exist to the evaluator.
  4. Invariants — what must not change ("without modifying vendor/"), always including "do not weaken, skip, or edit the checks themselves".
  5. Stop bound or blocked clause — "or stop after 20 turns", "if blocked, stop and report the blocker". Without one, a mis-stated condition loops forever; the formatter warns when it is missing. (Claude Code resets the turn counter on session resume, so a turn bound silently extends across resumes.)

Keep it small. Every constraint narrows the state space the model can explore. Collapse to one terminating criterion when possible, move scope and definitions into a referenced file, and drop non-goals — a constraint earns its place only by closing a real easy-out.

For long goals, also name the final evidence (diff, report, artifact) and require a progress log file — durable state across compaction and resume. If the brief exceeds 4,000 characters, put the details in a GOAL.md and reference that file from the objective.

Never invent missing elements. Ground every element in the user's request, the conversation, or the repository — look things up rather than guessing. If an element cannot be filled from available information, still optimize and format what the user provided, leave the element out, and flag it as missing (see Format). A goal with an invented success condition terminates on the wrong contract.

Close the easy-outs

Before formatting, reread the drafted condition as a lazy model would: what is the cheapest way to make every check pass without doing the intended work? Close the cheapest ones — prefer pairing checks you already have over adding constraints, and do not enumerate every conceivable out into a non-goal list. The recurring outs:

  • Delete or stub instead of fix — "search prints nothing" also holds when the callers are gone; pair such checks with one that proves the feature still works.
  • Pass on a subset — running one test file, narrowing the search path, excluding directories from the check.
  • Game the gate — skipping/xfail-ing tests, hardcoding expected outputs, special-casing the test inputs, editing the check (the invariants rule).
  • Claim without running — declaring done or blocked with no check output in the transcript (the show-the-output rule).

Same discipline as above: an out you cannot close from available information goes in the Missing: list as a warning — an invented or absurd constraint is worse than a flagged gap.

Security research goals

Collapse audit goals to one terminating criterion, such as identifying, triggering, and validating one high-severity vulnerability valid under a referenced threat-model file. That file, not the goal, carries scope, attacker powers, severity baseline, and known findings to skip. Use neutral wording ("trigger and validate", not "prove this is exploitable"), require demonstrated preconditions — assumed attacker access is the most common false positive — and stop for human review after each finding rather than piling up untriaged reports. Validate findings with a second pass by a fresh agent, never the finder alone.

Format

Run uv run --no-project {baseDir}/scripts/format_goal_prompt.py --fenced on the draft (file or stdin). It collapses whitespace to one line, strips /goal prefixes, quotes, and fences, warns on a missing stop clause, and rejects output over 4,000 characters — shorten or move detail to a file and rerun.

Return exactly one fenced text block, one line:

text
/goal <single normalized objective>

Add no prose around it — except when checklist elements could not be grounded: then follow the block with a Missing: list, one line per gap, telling the user what to supply.

Example

Draft:

/goal Migrate the auth module:
  - replace legacyAuth() with auth.verify()
  - make sure the tests still work

Redrafted and formatted:

text
/goal All legacyAuth() call sites use auth.verify(): `rg "legacyAuth\(" -t ts` prints nothing AND `npm test` exits 0 (run both, show the output), without modifying vendor/ or weakening any test. If blocked, stop and report attempted paths and the blocker, or stop after 20 turns.

Here npm test came from the repo's package.json — not a guess — and pairing it with the zero-matches check closes the cheapest out: deleting the call sites instead of migrating them. When nothing grounds an element, format what exists and flag the gaps:

Draft: make checkout faster, with no metric or benchmark anywhere in context:

text
/goal Make checkout faster

Missing:

  • measurable end state — which metric and threshold count as "faster"
  • verification — the benchmark or command that proves it
  • stop bound — e.g. "or stop after 20 turns"

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 Prompt AI skill do?

Drafts copy-paste-ready /goal commands for goal mode in Claude Code and Codex. Use when the user asks to create, write, rewrite, improve, compress, clean up, or prepare a goal prompt, goal condition, /goal command, goal-mode objective, or copy-ready long-running task objective.

Why use Goal Prompt on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trailofbits/skills/tree/main/plugins/goal-prompt/skills/goal-prompt. 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 Prompt?

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 Prompt?

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

Is the Goal Prompt AI skill free?

Yes. It is published on GitHub by trailofbits under the CC-BY-SA-4.0 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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