# Define Goal

Help the user define a concrete, measurable goal before starting work, especially when...

- Repository: https://github.com/openai/skills (28445 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-10-28
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add openai/skills --skill define-goal -g -a claude-code -y`
Codex: `npx skills add openai/skills --skill define-goal -g -a codex -y`
Cursor: `npx skills add openai/skills --skill define-goal -g -a cursor -y`
Gemini CLI: `npx skills add openai/skills --skill define-goal -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/openai/skills/blob/HEAD/skills/.curated/define-goal/SKILL.md