Owner Intent Audit logo

Owner Intent Audit

Organization
sno-ai
owner-intent-audit

Check agent-produced plans, tests, or code against the owner's actual intent. Find missing meaning, unrequested work, stale truth, unreachable decisions, unsupported completion claims, and implementation debt. Use before approval or merge.

Overview

Publishersno-ai
Repositorysno-station-skills
Skill nameowner-intent-audit
Stars
604
Forks
415
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sno-ai on GitHub. Read the source before you install it.

Installation

Install the Owner Intent Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sno-ai/sno-station-skills/tree/main/skills/J-peer-review-and-audit/owner-intent-audit/skill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sno-ai/sno-station-skills.git /tmp/sno-station-skills
mkdir -p .claude/skills
cp -r /tmp/sno-station-skills/skills/J-peer-review-and-audit/owner-intent-audit/skill .claude/skills/owner-intent-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Owner Intent Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Owner Intent Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Owner Intent Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Owner Intent Audit

The question this review answers is not "is the artifact good" but "is this what the owner meant, and what did the agent leave behind while making it?" (The owner is whoever owns the work and decides what it should be; often that is the user.) An agent can produce a PRD (product requirements document), a plan, or a diff that is locally excellent and still specify a different product, carry work nobody asked for, or hide the debts agents characteristically create. Reviewing the artifact for its own defects — bugs, security holes, silent death — is peer-review's job, not this one; the two are run separately and neither substitutes for the other.

  • Plan mode: PRDs, designs, specs, task lists, refactor plans, and test plans.
  • Code mode: diffs, files, tests, or subsystems substantially written by agents.

Start with the ordinary user and system flow. Realistic common-path failure outranks exotic edge cases.

What must be established

0. Did the artifact preserve the product meaning?

This is not “did every source bullet appear?” A locally plausible artifact can retain the same nouns while specifying a different product.

Determine:

  • who needs the outcome and who decides;
  • what result they actually need;
  • when the behavior applies and when it must not;
  • which exceptions, prohibitions, priorities, and trade-offs matter;
  • how separate answers constrain or explain each other;
  • what observable result distinguishes success from an easier proxy.

Find that meaning in the owner's raw request, the complete question-and-answer record, and the settled decisions. If the project keeps no such record, say so and use incomplete-evidence. Find loss of meaning in the target's requirements, ordinary flow, boundaries, design choices, and acceptance criteria. In code mode, also look through the governing contract (the spec, interface, or acceptance text the code is supposed to satisfy), callers, state changes, and user-visible result.

Important shapes include a condition becoming unconditional, an actor changing, an example becoming the whole boundary, a prohibition disappearing, related answers becoming isolated facts, or a familiar implementation replacing the requested outcome.

Keyword overlap and row-count traceability are not proof. Return real ambiguity to the owner; do not invent meaning.

1. What did the agent over-build?

Run the less-is-more skill in cut mode on the diff (code mode). In plan mode, apply its cut list to the plan as a checklist: what is proposed that the owner did not ask for, and what could be deleted without losing the requested outcome. Every item it reports is a finding here. In plan mode, also ask what wording would invite a literal agent to build it. Work that is necessary but belongs in a runbook, an orchestration step, or a test harness rather than the product contract is misplaced process work: report where it should move, without dropping its constraint.

2. Does the artifact graph resolve to one identity?

The plan or spec and everything tied to it must name the same current artifact by path, hash, and status, and every referenced file must exist. For example, where the project has them: a PRD, its acceptance file, its test commands, its evidence, and its status header. A stale identity or an unreachable verifier is a finding. Validate an absence check with a known positive control.

3. What is current truth?

Separate current contract (what must be delivered), current state (what ran, passed, or remains), and historical narrative (why it changed). History may explain current truth; it cannot override it. A load-bearing claim that no longer matches reality — in a PRD, plan, comment, default, or completed task — is a finding, because it misleads the next agent.

4. Is the decision model executable?

Check decision tables, state machines, ordered guards, schemas, fixtures, and tests. Every claimed branch must be reachable, branches claimed as exclusive must not overlap, and duplicate or shadowed branches must not masquerade as distinct outcomes.

5. Did claimed work run and reach its consumer?

“Ran,” “wired,” “enabled,” “deployed,” or “passed” require effect proof, not a flag, log line, or clean exit. Trace the ordinary entrypoint to an observable result; run the agentic-walkthrough skill when it is installed.

Evidence and judgment boundaries

Owner-intent preservation needs the raw request, complete Q&A, and settled decisions. Missing, partial, stale, or non-blind intent evidence means incomplete-evidence, not clean; it limits only the semantic verdict, and every other check still runs and reports. Intent evidence is non-blind when the reviewer had already read the artifact under review before reconstructing the owner's meaning, so the comparison is not independent; say so.

A behavior finding needs a realistic trigger and a material consequence; test it against guards and reachability before keeping it. An over-build finding from less-is-more needs no trigger: the extra code is the consequence. Do not report style debt.

Output

By default this audit is one judgment pass: do not rerun it, or run another model review, to confirm the edits that follow it; a project may choose otherwise. Never write a clean verdict before the audit has run. Keep the actual report immutable, record how each finding was handled, fix the material blockers in the work under review, prove the fixes deterministically, and continue without a model confirmation review (unless the project requires one).

markdown
## Owner Intent Audit — <target>
Mode: plan | code
Verdict: clean | findings | incomplete-evidence
### Semantic preservation
Owner-intent status: preserved | findings | not-assessable | non-blind
Contract-conformance status: preserved | findings | not-assessable
Evidence: <what supports the judgment and what is missing>
### Over-built
<the less-is-more cut output, plus any misplaced process work, or "Lean already.">
### Findings
[critical|high|medium] <finding> — <location>
Scenario: <realistic trigger and concrete consequence; omit for over-build>
Correction: <smallest outcome that removes the risk>
### Guardrails
<plan mode only; concise outcome constraints for the implementing agent>

clean requires every check, adequate intent evidence, and no unresolved material blocker in the work under review—not zero raw findings. Resolved findings and non-blocking debt never force another review.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Owner Intent Audit AI skill do?

Check agent-produced plans, tests, or code against the owner's actual intent. Find missing meaning, unrequested work, stale truth, unreachable decisions, unsupported completion claims, and implementation debt. Use before approval or merge.

Why use Owner Intent Audit on TypingMind?

Because you install it once and use it with any model. Owner Intent Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Owner Intent Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sno-ai/sno-station-skills/tree/main/skills/J-peer-review-and-audit/owner-intent-audit/skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Owner Intent Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Owner Intent Audit?

As many as you like. As long as a model supports skills, you can use Owner Intent Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Owner Intent Audit AI skill free?

Yes. It is published on GitHub by sno-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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