Define Goal
OpenAIOfficialPublished by OpenAI
- Skill
- Meta (authoring, orchestration, planning, memory)
- Agent tooling
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Define Goal provides a structured approach for AI agents to clarify user intent before initiating work. It focuses on converting fuzzy requests into precise, quantitative objectives that include clear success criteria, scope boundaries, and verification methods. By enforcing a quality bar for goal creation, this skill ensures that agents pursue tasks with measurable outcomes rather than open-ended activities, reducing ambiguity and improving task completion reliability.
What it does
- Evaluates user requests to determine if formal goal definition is required.
- Refines vague intentions into concrete, measurable objectives.
- Identifies specific verification evidence and success thresholds for tasks.
- Checks existing goal states to prevent duplication or conflict.
- Provides a structured quality bar for validating objectives before creation.
- Suggests concise clarification questions to resolve ambiguity in scope or metrics.
When to use it
- When a user asks to set a goal, create an objective, or use a goal tool.
- When a user's request is too vague to determine a clear 'done' state.
- When you need to establish explicit success criteria for a complex task.
- When you need to define scope boundaries to prevent scope creep.
- When you need to determine the specific evidence required to verify task completion.
Advantages
- Ensures tasks have a clear, verifiable definition of success.
- Reduces ambiguity by forcing the definition of scope and stop conditions.
- Promotes the use of quantitative metrics for performance and quality tasks.
- Prevents the creation of redundant or conflicting goals.
- Improves agent reliability by aligning work with explicit user expectations.
Usage examples
- “I need to make the checkout process faster.”
- The agent will ask for specific metrics (e.g., p95 latency) and the target threshold, then propose a goal like 'Reduce checkout API p95 latency below 250ms'.
- “Can you help me finish the work on PR 123?”
- The agent will identify the specific review comments, define the scope to only affected files, and set a goal to resolve comments verified by a specific test command.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/openai/define-goal/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip define-goal.zip -d .agents/skills
Documentation by OpenAI
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
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.
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
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
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.
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.
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_goalfor 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:checkoutand 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 123showing 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.
Files
- ATTRIBUTION.mdAdded by AutoGineer870 B
- LICENSE.txt10.5 KiB
- SKILL.md5.5 KiB
- agents/openai.yaml208 B
Requirements and compatibility
Written for: OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Node.js
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- OpenAI
- Publisher
- OpenAI (Official)
- Source
- openai/skills · skills/.curated/define-goal · commit 49f948f
- Licence
- Apache License 2.0 (redistribution allowed)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by OpenAI.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download define-goal.zip