Skill: mouserider/intention-engine (version 1.0.0)

--- name: intention-engine version: 1.0.0 description: "Intent inference and alignment for persistent AI agents. Classifies gaps between tasks and intentions, checks for misalignment before executing, and prevents wasted work." metadata: {"openclaw":{"emoji":"🧠"}} user-invocable: false --- # 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

path: README.md
content: # Intention Engine

**Intent inference and alignment for persistent AI agents.**

Reduce your human's cognitive load. They throw raw thoughts, the agent infers the task — and verifies it's the *right* task before executing.

## The Problem

When humans direct AI agents, there's a constant gap between what they *say* and what they *mean*. The task is "do A." The intention is *why* — what outcome they're actually driving toward. A is just one of many possible paths.

Most agents execute the literal task. Good agents understand the intention and execute toward *it*.

## What This Skill Does

The Intention Engine gives your agent a protocol for:

1. **Gap classification** — distinguish spec gaps (unclear how) from intention gaps (unclear why). Different gaps need different fixes.
2. **Context-layered inference** — stack user goals, topic context, recent memory, project state, and conversational momentum to infer intent.
3. **Premortem checks** — before executing anything expensive or irreversible, ask "what's the most likely way this fails?"
4. **Quality bar assessment** — distinguish "done adequately" from "done well" and match the right bar to the task.
5. **Negative intent checks** — identify what NOT to optimize for, preventing the Klarna trap.
6. **Wasted work detection** — verify the task serves the intention before executing.
7. **Calibrated push-back** — challenge tasks that conflict with stated goals or when better alternatives exist.

## What This Skill Does NOT Do

This skill focuses on *understanding intent* and *aligning execution*. It does not cover:

- **How to think about problems** — see [Activated Thinker](../activated-thinker/) for anti-binary thinking, gardener mindset, friction protocol, and capability building
- **Behavioral mode detection** — see [Activated Thinker](../activated-thinker/) for crunch vs exploratory mode

These skills complement each other: Intention Engine tells you *what* to do, Activated Thinker tells you *how to approach* doing it.

## Installation

```bash
clawhub install mouserider/intention-engine
```

Or copy the skill folder into your OpenClaw workspace's `skills/` directory.

## Inspirations & Attribution

This skill is directly inspired by and built upon **[Nate Skelton's Intent Engineering](https://natesnewsletter.substack.com/)** framework:

- *Intent engineering* — the distinction between task execution and intention alignment
- *Premortem prompting* — forcing failure imagination before committing to a plan
- *Quality bar distinction* — "done adequately" vs "done well"
- *Context layering* — structured stacking of context for richer inference
- *Spec clarity ≠ intention clarity* — they fail differently and need different fixes
- *The Klarna/$60M case study* — the danger of optimizing for stated metrics while destroying unstated constraints

## License

MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)