
Define Goal
Skill在开始工作前帮用户把模糊意图改写成具体、可量化的目标:写明要达成的结果、验证证据、成功阈值、范围边界与何时停下来提问,达标后才用目标工具(get_goal、create_goal)创建。收录于 OpenAI 官方技能库。
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
-
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.
- Use this skill when the user asks for
-
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.
- Call
-
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.
相關資源

Grill Me
Skill动手前让 agent 围绕计划或设计连续追问,每轮附上推荐答案,直到每个决策分支都问清楚。它会调用同仓库的 grilling 技能,安装时需要一并装上。
mattpocock/skills321k
Brainstorming
Skill写代码前通过逐个提问澄清意图、约束与成功标准,把模糊想法细化成可评审的设计,得到确认后再进入实施。Superpowers 的核心技能。
obra/superpowers316k
BMad Method
Skill敏捷 AI 驱动开发方法:内置分析师、产品经理、UX 设计师等角色技能,把想法整理成产品简报、PRD 与 PRFAQ,再拆成任务交给 agent 实现。需 Claude Code 等支持技能的工具,并装好 Node.js、Git 与 uv,之后让 bmad 技能执行 bmad setup。
bmad-code-org/BMAD-METHOD55.2k
alirezarezvani/claude-skills
Skill380 多个技能的大合集,覆盖工程、产品、营销、合规、财务与高管顾问等岗位,附大量纯标准库 Python 脚本;Claude Code 可按领域以插件安装,兼容 Codex、Gemini CLI 等十余种 agent。
alirezarezvani/claude-skills29.6k
CCPM
Skill软件项目的规格驱动管理流程:先引导头脑风暴写出 PRD,再拆成 Epic 与带依赖的任务并同步为 GitHub Issues,可随时询问站会进度、下一步与阻塞项,也能派多个 agent 并行写代码。需要 Git、已登录的 gh 命令行和一个 GitHub 仓库。
automazeio/ccpm8.4k
notion-spec-to-implementation
Skill从 Notion 读取 PRD 或功能规格,梳理需求与待澄清点,生成实施计划页并拆出任务,把规格、计划与任务互相关联并持续更新进度。收录于 OpenAI 官方插件仓库(开发者标注为 Notion),需要连接 Notion MCP。
openai/plugins7.9k