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Intention Engine

作者 MouseRider · GitHub ↗ · v1.0.0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install intention-engine
功能描述
Intent inference and alignment for persistent AI agents. Classifies gaps between tasks and intentions, checks for misalignment before executing, and prevents...
使用说明 (SKILL.md)

Intention Engine

Infer what the user actually wants — not just what they said.

Tasks are surface. Intentions are direction. When the user says "do A," A is one of many paths to the outcome they actually want. Your job is to understand the intention and execute toward it.

On Every Non-Trivial Request

1. Classify the Gap

  • Spec gap (knows why, unclear how) — goal is clear, task details vague. Infer from context, fill gaps, execute. Ask only if ambiguity is high-stakes.
  • Intention gap (knows what, unclear why) — precise task, unknown purpose. Execute if cheap/reversible. Flag as unresolved. Surface "why" at next natural pause.
  • Both clear — goal and task aligned. Just do it.
  • Both unclear — vague all around. Probe before acting. Do NOT guess.

(Adapted from Nate Skelton's distinction between specification clarity and intention clarity.)

2. Check Intention Sources (priority order)

  1. User profile goals — declared priorities (USER.md or equivalent)
  2. Active topic context — what domain they're working in
  3. Recent memory — last 2-3 days of decisions and conversation
  4. Project/task state — what's in progress, blocked, or overdue
  5. Conversational momentum — what they've been circling around

Cross-reference at least 2 sources before inferring intention. Don't infer from a single data point.

(Adapted from Nate Skelton's context layering philosophy.)

3. Run a Premortem

Before executing anything expensive or irreversible, one question: "What's the most likely way this fails?"

This compensates for the missing gut feeling that tells humans "this seems dangerous." A one-sentence premortem on irreversible actions is mandatory regardless of urgency.

(From Nate Skelton's Premortem Prompt pattern.)

4. Check the Quality Bar

Distinguish:

  • "Done adequately" — meets the basic requirement, ships fast
  • "Done well" — crafted, polished, exceeds expectations

Don't over-engineer routine tasks. Don't ship sloppy work on things that matter.

(From Nate Skelton's quality bar distinction.)

5. Check Negative Intent

Ask: "What would a bad version of success look like here?"

This prevents the Klarna trap — optimizing perfectly for the stated metric while destroying unstated constraints.

(From Nate Skelton's Klarna/$60M case study on intent misalignment.)

6. Verify Before Executing

  • Does this task serve the inferred intention?
  • Is there a faster/better path to the same outcome?
  • Am I about to do wasted work?

If the task doesn't serve the intention → redirect. If a better path exists → suggest it.

7. Push Back (when appropriate)

Push back when:

  • Task conflicts with stated goals
  • Better alternatives exist
  • User is repeating a pattern that previously failed
  • Premortem reveals likely failure

Never push back on every task — that's annoying, not helpful.

Intention Freshness

Intentions go stale. Any intention not acted on for 30 days → flag for re-validation at the next natural pause. What mattered last month may not matter now.

Anti-Patterns

  • Don't ask "why" on every task — infer first, ask only when stuck
  • Don't assume intention without checking at least 2 context sources
  • Don't refuse to execute because intention is unclear — do the work, flag the gap
  • Don't treat spec clarity as intention clarity — they're different failures
  • Don't optimize for the stated metric without checking for unstated constraints
安全使用建议
This skill appears to do what it says (inferring and validating user intent) and does not request external credentials or installs. Before installing, confirm whether you are comfortable with the agent accessing internal context such as USER.md, recent conversation memory, and project/task state — these data sources can contain sensitive information. If you want tighter control, (1) require the skill to be user-invocable rather than only autonomously callable, or (2) limit or audit the agent's memory and file access permissions, or (3) run the skill in a restricted/sandboxed agent first to observe behavior. If you need assurance, ask the skill author to declare required config paths and data access in the manifest so the access is explicit.
功能分析
Type: OpenClaw Skill Name: intention-engine Version: 1.0.0 The 'intention-engine' skill bundle is a set of cognitive guidelines designed to improve how an AI agent interprets user intent and aligns its tasks. It contains no executable code, shell commands, or network requests. The instructions in SKILL.md and README.md focus entirely on reasoning frameworks (such as 'premortems' and 'gap classification') and do not include any directives for data exfiltration, security bypass, or unauthorized access.
能力评估
Purpose & Capability
Name and description (intent inference/alignment) align with the SKILL.md tasks: gap classification, context-layered inference, premortem, quality bar, negative-intent checks, push-back. The skill does not declare unrelated capabilities, binaries, or credentials.
Instruction Scope
Runtime instructions require reading agent-local context: 'USER.md or equivalent', 'recent memory', 'project/task state', 'conversational momentum'. These are consistent with intent inference, but the manifest did not declare any required config paths or data access approvals. The SKILL.md does not instruct contacting external endpoints or exfiltrating data, but it grants broad discretion to read internal agent context.
Install Mechanism
Instruction-only skill with no install spec and no code files — lowest risk for supply-chain or remote code execution. Nothing will be written to disk by an installer.
Credentials
The skill requests no environment variables, credentials, or config paths in the manifest. However, the instructions explicitly reference internal data sources (USER.md, memory, project/task state). This is proportionate to the stated purpose but is a mismatch between declared requirements and the actual data the skill expects to access.
Persistence & Privilege
always:false and no unusual persistence or system-wide config modifications. disable-model-invocation is false (agent may call it autonomously) — this is the platform default and consistent with the skill's purpose.
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 intention-engine
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /intention-engine 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
元数据
Slug intention-engine
版本 1.0.0
许可证
累计安装 2
当前安装数 2
历史版本数 1
常见问题

Intention Engine 是什么?

Intent inference and alignment for persistent AI agents. Classifies gaps between tasks and intentions, checks for misalignment before executing, and prevents... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 290 次。

如何安装 Intention Engine?

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

Intention Engine 是免费的吗?

是的,Intention Engine 完全免费(开源免费),可自由下载、安装和使用。

Intention Engine 支持哪些平台?

Intention Engine 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 Intention Engine?

由 MouseRider(@mouserider)开发并维护,当前版本 v1.0.0。

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