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/audit-prompt)<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/audit-prompt"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/audit-prompt/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/audit-prompt"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/audit-prompt.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 | $0.00504 |
| Opus 5 | $0.00006 | $0.00252 |
| Sonnet 5 | $0.00003 | $0.00101 |
| Haiku 4.5 | $0.00001 | $0.00050 |
Grade A, and why
audit-prompt 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 11d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are auditing an existing prompt. Use only skills from the prompt-architecture plugin. Follow this process:
Step 1: Structural Analysis
Using system-prompt-structure:
- Does the prompt have clear, separated sections?
- Is identity/role defined clearly?
- Are behavioral rules explicit and non-contradictory?
- Are output specifications concrete?
- Is the most important content at the beginning?
- Score structure quality (1-5)
Step 2: Constraint Analysis
Using constraint-specification:
- Are constraints specific and measurable?
- Are there constraint conflicts?
- Is there a clear priority hierarchy?
- Are there missing constraints that should be added?
- Score constraint quality (1-5)
Step 3: Example Analysis
Using few-shot-patterns:
- Are there examples? Are there enough?
- Do examples demonstrate the right behavior?
- Are examples diverse and high-quality?
- Do examples match the stated constraints?
- Score example quality (1-5)
Step 4: Context Analysis
Using context-engineering:
- Is the context budget well-allocated?
- Is information ordered effectively?
- Is there unnecessary content consuming context space?
- Are context injection points well-designed?
- Score context design quality (1-5)
Step 5: Edge Case Testing
Generate 5 challenging inputs and predict how the prompt would handle them:
- An ambiguous request
- A request at the boundary of the prompt's scope
- A very simple request (is the prompt over-engineered for simple cases?)
- A very complex request (does the prompt handle complexity?)
- An adversarial or tricky request
Step 6: Versioning Assessment
Using prompt-versioning:
- Is the prompt versioned and tracked?
- Is there documentation for why it's written this way?
- Are there test cases?
- Is there a review process?
Output
Deliver a prompt audit report:
- Overall quality score (1-5) with justification
- Section-by-section analysis with scores
- Issues found: Issue | Severity | Category | Recommendation
- Edge case analysis with predicted behavior
- Top 5 improvements ranked by expected impact
- Rewritten prompt (improved version) if significant changes are needed
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.
- 11d ago First seen · 57 lines · 13 tokens per session scan A 1ba744e5c587
audit-prompt 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 504 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.
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