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clarkchenkai

Goal Clarifier

作者 Cubic AI · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install goal-clarifier-clarkchenkai
功能描述
Goal Clarifier — A purpose-first skill for turning vague requests into executable briefs before design or execution begins. Inspired by Aristotle's idea of t...
使用说明 (SKILL.md)

Goal Clarifier — Define Telos Before Design

Use this skill when the user starts with a solution-shaped request, but the real objective is still ambiguous.

Activation Triggers

Activate when the user says things like:

  • "Help me build an agent"
  • "Make a dashboard for this"
  • "Automate this workflow"
  • "Optimize this project"
  • "Set up a hiring system"
  • "We need a better process"

The common pattern is not lack of effort. It is lack of a stable goal definition.

Core Protocol

Follow this four-step protocol in order. Do not skip to system design, tool choice, or implementation until the output contract is complete.

Step 1: Translate Means into Ends

Detect where the user is naming a mechanism instead of a purpose.

  • "Build a recruiting agent" may mean screening applicants faster, scheduling interviews, improving candidate quality, or raising offer acceptance.
  • "Make a dashboard" may mean spotting risk earlier, aligning stakeholders, or replacing manual weekly reporting.

Ask one direct question:

"If this works, what changes in the world that matters?"

Reference: references/aristotle.md

Step 2: Force the Three Essentials

The brief is not valid until all three are explicit:

  • Goal: the concrete outcome that should exist if the work succeeds
  • Constraints: time, budget, policy, risk, data, people, tooling, and reversibility limits
  • Success Criteria: observable evidence that the outcome was achieved

If any essential is missing, ask a short targeted follow-up instead of guessing.

Reference: references/patterns.md

Step 3: Define Boundaries

Clarify what is out of scope.

  • Which users, teams, or cases are excluded?
  • Which nice-to-haves should not be included in v1?
  • Which decisions must remain human-owned?

This prevents scope creep disguised as ambition.

Step 4: Separate Blocking Ambiguities from Working Assumptions

List unresolved issues in two groups:

  • Blocking ambiguities: must be answered before design or execution
  • Working assumptions: can be temporarily assumed, but must be called out clearly

Do not let the conversation hide these in prose.

Reference: references/high-risk.md

Output Contract

Always end with a six-part brief using these exact headings:

## Goal
[What outcome matters]

## Constraints
[Time, risk, policy, resources, data, ownership]

## Success Criteria
[How success will be measured]

## Scope Boundary
[What is explicitly not included]

## Key Ambiguities
[Blocking questions and working assumptions]

## Recommended Next Step
[The smallest safe action from here]

High-Risk Rules

When the request touches irreversible or high-stakes decisions, slow down.

Examples:

  • hiring, firing, promotion, compensation, admissions
  • medical, legal, tax, finance, compliance, security
  • surveillance, fraud, moderation, eligibility, trust and safety
  • automated action against real people with material consequences

In these cases:

  1. Require explicit success criteria and human ownership.
  2. Mark the decision boundary that must stay with a human.
  3. Refuse to "just automate it" if the purpose is still unclear.
  4. Do not output an execution plan disguised as a clarified brief.

Question Strategy

  • Prefer 1-3 sharp questions over a long questionnaire.
  • Ask about outcomes before architecture.
  • Ask about constraints before features.
  • Ask about success criteria before milestones.
  • Ask about exclusions before integrations.

If the user is impatient, compress harder, but do not skip the structure.

Response Style

  • Be direct, not bureaucratic.
  • Replace fuzzy nouns with observable outcomes.
  • Surface hidden tradeoffs plainly.
  • Treat "faster" or "better" as incomplete until tied to a metric or decision.
  • Preserve the user's domain language when it helps, but normalize the brief structure.

Boundaries

This skill does not:

  • design the whole system
  • choose the full architecture
  • produce a project plan
  • justify unclear goals with confident assumptions

Its job is to make downstream design safer and sharper.

安全使用建议
This skill appears coherent and low-risk: it only changes how the agent asks questions and produces a brief. Before installing, consider: (1) provenance — the owner and homepage are missing, so if provenance matters to you, prefer skills with identifiable maintainers; (2) implicit invocation — the skill is allowed to trigger automatically on matching prompts, so disable implicit invocation if you don't want it to interrupt flows; (3) review the SKILL.md yourself to ensure its question style fits your workflow. If those points are acceptable, the skill is safe to use for clarifying goals but remember it only guides conversation — it does not perform actions or access secrets.
功能分析
Type: OpenClaw Skill Name: goal-clarifier-clarkchenkai Version: 1.0.0 The skill bundle is a purely instructional tool designed to help users clarify goals and constraints before proceeding with technical implementation. It contains only markdown documentation and configuration files (SKILL.md, agents/openai.yaml) that guide the AI agent's conversational logic. There is no executable code, network activity, or data access, and the instructions include explicit safety protocols for high-risk domains like legal or medical advice.
能力评估
Purpose & Capability
The name and description ('Goal Clarifier') match the SKILL.md: it only asks the agent to ask questions and produce a six-part brief. There are no unrelated environment variables, binaries, or config paths requested. (Note: the package has no homepage and an unknown owner ID, which is a provenance/traceability issue but does not affect capability alignment.)
Instruction Scope
The runtime instructions are limited to conversational behavior: detect solution-shaped requests, ask targeted follow-ups, and emit a constrained brief. The SKILL.md does not instruct reading files, environment variables, network endpoints, or performing external actions.
Install Mechanism
This is an instruction-only skill with no install spec and no code files to execute; therefore it has minimal disk/execution risk.
Credentials
The manifest declares no required environment variables, credentials, or config paths. The instructions also do not reference any secrets or external service credentials.
Persistence & Privilege
always is false (normal). The agents/openai.yaml sets policy.allow_implicit_invocation: true, which permits implicit invocation when triggers match; this is reasonable for a conversational clarifier but you may prefer to restrict implicit triggering depending on your policy.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install goal-clarifier-clarkchenkai
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /goal-clarifier-clarkchenkai 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release — clarify vague requests into actionable briefs before design or execution. - Introduces a four-step protocol to define goals, constraints, success criteria, and scope. - Provides a structured six-part output contract for every clarified brief. - Emphasizes purpose-first questioning before discussing solutions or tools. - Includes special safeguards for high-stakes or irreversible requests. - Outlines direct, outcome-focused response style and limits on overreach.
元数据
Slug goal-clarifier-clarkchenkai
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Goal Clarifier 是什么?

Goal Clarifier — A purpose-first skill for turning vague requests into executable briefs before design or execution begins. Inspired by Aristotle's idea of t... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 90 次。

如何安装 Goal Clarifier?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install goal-clarifier-clarkchenkai」即可一键安装,无需额外配置。

Goal Clarifier 是免费的吗?

是的,Goal Clarifier 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Goal Clarifier 支持哪些平台?

Goal Clarifier 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Goal Clarifier?

由 Cubic AI(@clarkchenkai)开发并维护,当前版本 v1.0.0。

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