Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/owl-listener/ai-design-skills/write-policy)<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/write-policy"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/write-policy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/write-policy"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/write-policy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00013 | $0.00547 |
| Opus 5 | $0.00006 | $0.00273 |
| Sonnet 5 | $0.00003 | $0.00109 |
| Haiku 4.5 | $0.00001 | $0.00055 |
Grade A, and why
write-policy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are drafting an AI behavior policy. Use only skills from the ai-alignment-reasoning plugin. Follow this process:
Step 1: Establish the Value Foundation
Using value-specification:
- What are the organisation's core values relevant to AI?
- How do these translate to AI behavior principles?
- Create a value hierarchy with clear conflict resolution
- Identify key stakeholder perspectives (users, legal, brand, ethics)
Step 2: Define Behavioral Boundaries
Using guardrail-design:
- What will the AI always do? (mandatory behaviors)
- What will the AI never do? (prohibited behaviors)
- What requires human approval? (escalation behaviors)
- What varies by context? (conditional behaviors)
- Write each rule clearly enough to be implemented and tested
Step 3: Specify Tone and Voice
Using value-specification and guardrail-design (tone guardrails):
- How should the AI sound? Define voice attributes.
- What tone shifts are appropriate in different contexts?
- What language is off-limits?
- How does the AI handle sensitive topics?
Step 4: Define Transparency Requirements
Using transparency-patterns:
- What must the AI disclose to users?
- How should uncertainty be communicated?
- What source attribution is required?
- When must the AI identify itself as AI?
Step 5: Specify Consent and Data Practices
Using consent-and-agency:
- What data does the AI use and how?
- What consent is required from users?
- What opt-out mechanisms must exist?
- What override capabilities must users have?
Step 6: Plan for Harm Prevention
Using harm-anticipation and escalation-design:
- What harm scenarios has the policy been designed to prevent?
- What escalation procedures exist?
- How are incidents reported and handled?
- What review cadence keeps the policy current?
Step 7: Address Bias
Using bias-detection-design:
- What bias monitoring is required?
- How often are bias audits conducted?
- What mitigation processes exist?
- Who is accountable for bias-related issues?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 60 lines · 13 tokens per session scan A 674f96fbc456
write-policy is a command published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 13 tokens to every session and 547 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.