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Agent skill

Setup Goal

by iurysza in iurysza/module-graph

Turns an idea into an approved goal package under ai-artifacts.

MITAuto-check passedAgent Workflows

Install Setup Goal

skills CLI
$ npx skills add iurysza/module-graph --skill setup-goal -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install iurysza/module-graph setup-goal --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/iurysza/module-graph.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/setup-goal .claude/skills/setup-goal && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
setup-goal
GitHub stars
419
Token cost
~1.5k tokens
SKILL.md length
690 words
Files
2
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

Turns an idea into an approved goal package under ai-artifacts.

  • Works in 5 steps: Rearticulate the goal → Extract intent → Build the fact contract → …
  • Work needs clarified intent
  • SKILL.md covers Goal package, Choose the interaction path, 1. Rearticulate the goal and 2. Extract intent, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Setup Goal is an agent skill from iurysza/module-graph. Turns an idea into an approved goal package under ai-artifacts. Use when work needs clarified intent, scope, accepted facts, an implementation plan, and an explicit handoff.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `PLANNOTATOR.md`).

It sits in Agent Workflows, covering Planning. The repository describes itself as: A Gradle Plugin for visualizing your project's structure, powered by mermaidjs. The licence is MIT.

When your agent uses it

  • Work needs clarified intent
  • An implementation plan
  • An explicit handoff

Example prompts

  • “Use the setup-goal skill to turn an idea into an approved goal package under ai-artifacts”
  • “/setup-goal”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Rearticulate the goal
  2. Extract intent
  3. Build the fact contract
  4. Produce the plan
  5. Finalize the package

What it can do on your machine

Read from SKILL.md and the folder at commit 15b0135. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Setup Goal loads about 1.5k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 690 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from iurysza/module-graph at commit 15b0135, republished under its MIT licence (© iurysza). 690 words, ~1,545 tokens.

Download SKILL.mdSave it as .claude/skills/setup-goal/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
setup-goal
description
Turns an idea into an approved goal package under ai-artifacts. Use when work needs clarified intent, scope, accepted facts, an implementation plan, and an explicit handoff.
metadata.category
planning-architecture

Setup Goal

Turn an idea into an execution-ready package at ai-artifacts/goals/<slug>/. Extract intent, resolve material uncertainty, agree on testable facts, and produce a concrete plan before implementation begins.

Do not implement the goal while running this skill.

Goal package

text
ai-artifacts/goals/<slug>/
├── intent.md
├── facts.md
├── facts.meta.json
├── plan.md
├── dev-log.md
└── goal.md

Create files only as their phase is reached. Markdown files are the human-readable source of truth. Tool-specific working files may live beside them but must not become required inputs for execution.

Choose the interaction path

Inspect which question and review tools are available before asking the user anything.

  • For several material decisions, prefer Plannotator when the plannotator CLI is available. Read PLANNOTATOR.md and use its interview and review adapters.
  • Otherwise, use the host's structured question or interview tool.
  • If no question tool exists, present a short, answerable bundle in chat.
  • For one quick decision, use the lightest available question tool instead of opening a full interview.

Never require or install Plannotator. The goal workflow must work without it.

1. Rearticulate the goal

State the intended outcome in two or three sentences. Include who benefits and what changes. If the request is vague, perform only enough repository exploration to ground the restatement.

Ask the user to correct the restatement before continuing. Do not ask them to repeat details already present in the conversation.

Create ai-artifacts/goals/<slug>/ after the outcome and slug are clear.

2. Extract intent

Inspect repository instructions, relevant code, docs, tests, configuration, prior decisions, and similar implementations. Answer factual questions from evidence instead of asking the user.

Separate what you learn into:

  • Outcome: the observable result the user wants.
  • Audience: who uses or operates it.
  • Problem: why the change matters.
  • Scope: behavior included in this goal.
  • Non-goals: nearby behavior intentionally excluded.
  • Constraints: technical, product, security, time, compatibility, or policy limits.
  • Decisions: choices only the user can make.
  • Assumptions: low-risk defaults accepted provisionally.
  • Unknowns: unresolved items that could materially change the work.

Ask only questions whose answers could change scope, user-visible behavior, architecture, data, permissions, safety, rollout, or success criteria. Each question should include context, meaningful options when possible, and a recommended answer.

Prefer fewer high-leverage questions over exhaustive questionnaires. Do not use a relentless one-question loop. Bundle related decisions when the available tool supports it.

Write intent.md:

md
# Intent

## Outcome

## Audience and problem

## Scope

## Non-goals

## Constraints

## Decisions

## Assumptions

## Open questions

Do not leave a blocking question unresolved. The user may explicitly accept a named assumption instead.

Show full SKILL.md (308 more words)Show less

3. Build the fact contract

Convert the approved intent into a flat list of concrete, testable facts. A fact describes observable behavior, a boundary, or a verifiable constraint. Keep implementation choices out unless they are themselves approved requirements.

Review the facts with the user. They must be able to accept, edit, or remove each fact. Do not proceed until the fact set is explicitly approved.

Write accepted facts to facts.md:

md
# Facts

- The system ...
- A user can ...
- The change does not ...

Write matching metadata to facts.meta.json:

json
[
  {
    "id": "fact-1",
    "text": "The accepted fact text.",
    "comment": "",
    "recommendedAutomatedVerification": true,
    "automatedVerification": true
  }
]

Use stable IDs. recommendedAutomatedVerification records the agent's recommendation; automatedVerification records the user's accepted choice. Set them to true when a concrete automated check should prove the fact. Preserve user comments that affect interpretation.

4. Produce the plan

Explore the implementation path for every accepted fact. Trace existing code and name exact files or systems where possible.

Write plan.md with:

  • a brief solution approach;
  • ordered, bounded implementation steps;
  • files or systems touched by each step;
  • concrete verification for each step;
  • coverage for every fact marked automatedVerification: true;
  • risks, accepted assumptions, and remaining non-blocking unknowns.

Review the plan with the user using the best available review tool. Prefer Plannotator annotation when available; otherwise request explicit approval through the host's question tool or chat. If rejected, revise and review again.

5. Finalize the package

Create dev-log.md:

md
# Dev Log

Status: Not started

Do not fabricate progress. Execution appends work and evidence later.

Create goal.md:

md
# Goal

{One to three sentences describing the approved outcome.}

## Contract

- [Intent](./intent.md)
- [Facts](./facts.md)
- [Fact metadata](./facts.meta.json)
- [Plan](./plan.md)
- [Dev log](./dev-log.md)

## Done when

{Concise completion condition derived from the accepted facts and plan.}

Before finishing, verify that every link resolves, every fact has matching metadata, the plan covers every fact, and no blocking decision remains.

Tell the user the package path and that it is ready for the goal execution skill. If the host exposes a /goal command, it may be launched as:

text
/goal ai-artifacts/goals/<slug>/goal.md

Revisions

When intent or facts change, return the package to setup rather than silently changing the execution contract. Update intent.md, review affected facts again, revise the plan, and preserve stable fact IDs where their meaning did not change.

© iurysza, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/setup-goal of iurysza/module-graph.

  • SKILL.md
  • PLANNOTATOR.md

Open the folder on GitHubat commit 15b0135

Compare with similar skills

Setup Goal next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Setup Goal compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Setup Goal this skilliurysza/module-graph419—~1.5kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers296k2 repos~5.1kAutomated safety check: PassMIT
Interview Meaddyosmani/agent-skills103k6 repos~3.8kAutomated safety check: PassMIT
OpenSpec Guided OnboardingFission-AI/OpenSpec71k1 repos~4.5kAutomated safety check: PassMIT
Writing Plansgeeksblabla/stateofdev.ma16357 repos~661Automated safety check: PassNone
Subagent Driven DevelopmentAsvarox/allkaraoke26138 repos~1.2kAutomated safety check: PassNone

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  • Executing Plans Inline

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Categories

Questions about Setup Goal

What does Setup Goal do?

Turns an idea into an approved goal package under ai-artifacts. Setup Goal is an agent skill from iurysza/module-graph. Turns an idea into an approved goal package under ai-artifacts.

When should I use Setup Goal?

Setup Goal fits situations like: work needs clarified intent; an implementation plan; an explicit handoff.

How do I install Setup Goal in Claude Code?

Run `npx skills add iurysza/module-graph --skill setup-goal -a claude-code`. Or copy the skill folder (.agents/skills/setup-goal in iurysza/module-graph) into .claude/skills/setup-goal in your project. Claude Code loads it when a task matches its description.

How do I install Setup Goal in Codex?

Run `npx skills add iurysza/module-graph --skill setup-goal -a codex`. Or copy the skill folder (.agents/skills/setup-goal in iurysza/module-graph) into .agents/skills/setup-goal in your project. Codex loads it when a task matches its description.

Can I use Setup Goal in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add iurysza/module-graph --skill setup-goal -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/setup-goal, .gemini/skills/setup-goal, .github/skills/setup-goal and .opencode/skills/setup-goal in your project.

What does Setup Goal need to run?

SKILL.md names no scripts, command-line tools or credentials: Setup Goal is instructions for the agent only.

Does Setup Goal access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Setup Goal safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Setup Goal use?

Setup Goal is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Setup Goal use?

About 1.5k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Setup Goal?

Skills that share tags, products or a category with Setup Goal: Executing Plans Inline (obra/superpowers, 296k stars), Interview Me (addyosmani/agent-skills, 103k stars), OpenSpec Guided Onboarding (Fission-AI/OpenSpec, 71k stars) and Writing Plans (geeksblabla/stateofdev.ma, 163 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Setup Goal?

iurysza (a GitHub user) maintains it in iurysza/module-graph, which has 419 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 7, 2026.

Source: iurysza/module-graph on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.