Atlas / Skills / openai / Define Goal

Define GoalSAFE

skills/openai/define-goal

Skills Catalog for Codex

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
—
Stars
28,282
01

Overview

Skills Catalog for Codex

Read from source at commit 21ca169598cfOBSERVED · 2026-10-24
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02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: define-goal
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.
---

# 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

4. 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.

5. 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.

6. 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_goal` for 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:checkout` and 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 123` showing 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.
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-24 · audit v0.4.1 · source sha 21ca169598cffull audit observations/trust-audit/skill/openai__define-goal.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-2421ca169598cfSAFEB89first audit
05

Questions

What does the define-goal — openai/skills skill do?

Skills Catalog for Codex

Is define-goal — openai/skills safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can define-goal — openai/skills access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (21ca169598cf), read on 2026-10-24. The repository is watched, and a new audit runs when it changes — this is the first audit.

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