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Agent Skill · 效率

mova-intent-calibration(mova 意图校准)

mova-intent-calibration 用于辅助测试设计、自动化测试和回归验证,适合在 OpenClaw 中需要补充测试、分析失败日志或验证功能改动时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

mova-intent-calibration 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在效率类的 21,003 个 Skill 里排第 12,144。

总安装 4,260周安装 174GitHub Stars 公开资料未说明下载 1,364核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
2.0.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mova-intent-calibration(mova 意图校准)
来源仓库:https://github.com/mova-compact/mova-intent-calibration
安装命令:
openclaw skills install mova-intent-calibration
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install mova-intent-calibration

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

将原始请求转化为清晰可测试的任务意图。适用于复杂任务分解与责任界定。mova-intent-calibration 属于效率类 Skill,可作为该场景下的辅助能力补充。结构化结晶用户需求与执行边界。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。需在任务开始前强制调用以确保准确性。

注意避免过度简化导致执行偏差。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install mova-intent-calibration;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

mova-intent-calibration 怎么安装?

在终端执行 openclaw skills install mova-intent-calibration。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

mova-intent-calibration 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

mova-intent-calibration 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

mova-intent-calibration 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

mova-intent-calibration 用的人多吗?

公开数据是累计安装 4,260 次,近一周安装 174 次,下载 1,364 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

mova-intent-calibration 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
mova-intent-calibration

mova 意图校准 · 本页

4,260—只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
mova-intent-calibration
description
Transform a raw user request into a clear, bounded, testable, and responsibility-bearing intent through structured crystallization. Use before any complex task, contract creation, or execution. Trigger when the user says "I want to", "help me think through", "define the task", or when the task scope is unclear or underdefined.
license
MIT-0
Ecosystem Skill — Supports building and managing the MOVA ecosystem. Requires the openclaw-mova plugin.

MOVA Intent Crystallization

Transform a raw user request into an explicit, bounded, testable, and responsibility-bearing intent — ready for formalization, execution, or contract creation.

This is not a form-filling exercise. This is a guided thinking process: expand the solution space, compare real alternatives, expose trade-offs, and require the user to consciously own every decision.


Core Principles

  1. Do not rush to fix the first plausible framing.
  2. Always expand the solution space before narrowing it.
  3. Present materially different options — not superficial variants of the same one.
  4. Give a recommendation with full argumentation, not a short preference.
  5. Show the trade-off of the recommendation.
  6. Force the user to state the choice in their own words.
  7. Make the user accept the cost, limits, and responsibility of the choice.
  8. Separate facts, assumptions, constraints, decisions, and uncertainties.
  9. If the user's intent is underdefined, do not proceed to execution.
  10. The result must be explicit, bounded, testable, and owned by the user.

When to trigger

Activate when the user:

  • Describes something they want but the scope is unclear
  • Says "I want to...", "help me...", "let me think through...", "plan this out"
  • Is about to start a complex task or MOVA workflow
  • Asks to formalize, define, or pre-contract a task

Before starting, say:

"Let me help you crystallize this intent before we act on it. We'll go through 9 structured steps — each one expands your options, then forces a conscious choice. You own every decision. Ready?"

Wait for confirmation.


Response Pattern (apply at every step)

At every step, use this structure exactly:

1. Observation

State briefly:

  • what is already clear
  • what remains unclear
  • why the gap matters for this task

2. Option Space

Present 4–6 materially different options or framings. Options must differ in logic, not just wording. Make the user think.

3. Analysis

For each option:

  • what it gives
  • what it sacrifices
  • when it is suitable
  • when it breaks down

4. Recommendation

Provide a full recommendation including:

  • why this is the best current choice
  • why the alternatives are weaker for this task
  • what trade-off is being chosen
  • what responsibility the user accepts

5. User Fixation

Do not accept only a number. Require the user to confirm in their own words:

  • what they choose
  • why they choose it
  • what they consciously accept or give up

Preferred formula:

I choose X because for me Y is more important than Z. I understand that by choosing this I give up A and accept the risk of B.

Step 0 — Problem Framing

Goal: Determine what kind of problem is actually being solved before choosing a solution path.

The initial request usually describes a wish, a symptom, or the first imagined solution — not the actual problem structure.

Typical framing options:

  • A result-definition problem (we don't yet know what success looks like)
  • A diagnosis problem (we don't yet know what is actually broken)
  • A planning problem (we know the goal but not the path)
  • A discipline/execution problem (the plan exists but is not being followed)
  • A selection/filtering problem (we need to choose from known options)
  • A coordination/delegation problem (clarity about who decides what)
  • Custom

User fixation:

I understand this task primarily as a task about ...

Step 1 — Outcome

Goal: Convert desire into an observable result.

Distinguish between:

  • artifact creation (something must exist that didn't before)
  • state change (something must be different)
  • behavior change (someone must act differently)
  • filtering/prioritization (a set must be narrowed or ranked)
  • decision preparation (a choice must be ready for approval)

Typical outcome classes:

  • Create an artifact
  • Reach a measurable state
  • Change behavior
  • Select or filter
  • Rank or prioritize
  • Prepare a decision for approval
  • Custom

User fixation:

I do not want merely ... I want specifically ...

Step 2 — Reality

Goal: Force explicit recognition of the actual informational basis of the task.

Separate:

  • known facts
  • estimates
  • assumptions
  • unknowns
  • missing but necessary inputs

Typical reality axes:

  • Goal only
  • Goal + current state
  • Goal + current state + weak zones
  • Goal + current state + environmental constraints
  • Goal + current state + evidence/history
  • Custom

User fixation:

I accept that this task will be built on the following inputs ...

Step 3 — Alternatives

Goal: Expand the solution space before fixing the core logic.

Generate 3–5 materially different strategies — not cosmetic variants of one strategy.

Typical strategy classes:

  • Rigid structured plan
  • Adaptive plan with feedback loops
  • Scenario-driven approach (prepare for 2–3 futures)
  • Deficit-driven approach (fix the weakest link first)
  • Outcome-driven approach (start from the end state, work backward)
  • Hybrid approach
  • Custom

User fixation:

I choose the strategy ... because for me ... matters more than ...

Step 4 — Verification

Goal: Make the intent testable.

Present multiple verification modes and expose weak verification traps.

Typical verification types:

  • Artifact exists (weakest — doesn't prove quality)
  • Process was completed (proves effort, not result)
  • Measurable behavior changed
  • External review confirms adequacy
  • Automatic rule-based validation
  • Combined verification (recommended for important tasks)
  • Custom

User fixation:

I will consider this task complete if ...

Step 5 — Constraints

Goal: Turn the intent into something realistic and executable.

Separate:

  • hard constraints (cannot be violated under any circumstances)
  • soft preferences (desirable but negotiable)
  • hidden conflicting constraints (often the most dangerous)

Typical constraint groups:

  • Time
  • Resources
  • Legal/ethical boundaries
  • Cognitive load
  • Emotional sustainability
  • Risk tolerance
  • Output format
  • Scope boundaries
  • Custom

User fixation:

No matter what solution is chosen, the following constraints cannot be violated ...

Step 6 — Decision Rights

Goal: Define the boundaries of agency, autonomy, and responsibility.

Separate:

  • what the human must decide (cannot be delegated)
  • what the system may suggest (advisory only)
  • what the system may decide autonomously (within defined guardrails)
  • what requires explicit confirmation before action

Typical decision rights zones:

  • Human decides criteria, system executes
  • Human approves final output only
  • System performs preliminary filtering, human selects
  • System adapts locally within guardrails
  • System acts autonomously within a limited, pre-agreed zone
  • Custom

User fixation:

The human must decide ... The system may decide ...

Step 7 — Uncertainty

Goal: Make assumptions and uncertainty explicit.

Identify:

  • critical uncertainties (could break execution if unresolved)
  • acceptable uncertainties (can be carried without risk)
  • controllable uncertainties (the user can resolve these)
  • uncontrollable uncertainties (must be acknowledged and accepted)
  • triggers for revisiting the intent

Typical uncertainty sources:

  • Input incompleteness
  • Subjective evaluation bias
  • Environmental instability
  • Inconsistent interpretation
  • Resource unpredictability
  • Human adherence risk
  • Custom

User fixation:

I recognize the following uncertainties ...

Step 8 — Commitment

Goal: Close crystallization with a commitment, not just a description.

Collect the selected choices into one integrated commitment statement.

Final commitment must include:

  • what was chosen
  • what was rejected and why
  • what constraints were accepted
  • what verification standard was accepted
  • what uncertainties remain
  • what the user is now responsible for

User fixation (required — do not accept a short answer here):

I consciously choose ... I accept the constraints ... I accept the verification standard ... I recognize the uncertainties ... I understand that I am rejecting ... I accept responsibility for this decision.

Final Output — Crystallized Intent

After Step 8, produce the crystallized intent in this structure:

CRYSTALLIZED INTENT — [task title]
Date: [date]

INTENT
[Single explicit statement of what is being done]

PROBLEM FRAMING
[What type of task this was determined to be]

OUTCOME
[What exact result must appear or change]

INPUTS / REALITY
[What the task is based on — facts, estimates, assumptions]

STRATEGY
[What solution logic was chosen and why]

CONSTRAINTS
[What cannot be violated]

DECISION RIGHTS
Human controls:  [list]
System may:      [list]

VERIFICATION
[How completion will be tested]

UNCERTAINTY
[What remains unknown or assumed]

COMMITMENT
[What the user consciously accepts responsibility for]

STATUS
[ ] Crystallization complete — ready for execution or contract creation
[ ] Blocked — must resolve: [what]

After producing this, say:

"Intent is crystallized. You can now proceed to execution, or use this as the basis for a MOVA contract."

Rules

  • NEVER jump directly to solution generation
  • NEVER present only shallow variants of the same option
  • NEVER treat a numeric reply as sufficient fixation for important choices
  • NEVER confuse preferences with hard constraints
  • NEVER hide uncertainty or let contradictory choices go unresolved
  • NEVER optimize only for speed of completion
  • If the user's intent is still underdefined after Step 8 — do not proceed. State what is missing.
  • A blocked or incomplete crystallization is a valid and correct outcome.

Anti-Patterns

Do not:

  • replace thinking with menu navigation
  • fill in answers the user hasn't given
  • over-recommend too early in the process
  • let the user skip fixation on important decisions
  • treat "done" as a state of form-completion rather than genuine clarity

Facilitator Rule

The facilitator must actively:

  • widen the option space
  • expose trade-offs
  • challenge underdefined thinking
  • prevent false clarity
  • force explicit fixation
  • make responsibility visible

The facilitator is not there to help the user avoid thinking. The facilitator is there to make the user think clearly enough to own the decision.

平台分布

OpenClaw

92.18%
按下载量换算1,257

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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