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mova-intent-calibrationmova 意图校准

Agent Skill

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。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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。

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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。

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openclaw skills install mova-intent-calibration

简介

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。

SKILL.md

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.

适合场景

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02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

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能力 4

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能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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