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-interaction)<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/audit-interaction"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/audit-interaction/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-interaction"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/audit-interaction.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 | $0.00011 | $0.00454 |
| Opus 5 | $0.00005 | $0.00227 |
| Sonnet 5 | $0.00002 | $0.00091 |
| Haiku 4.5 | $0.00001 | $0.00045 |
Grade A, and why
audit-interaction 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.
What it actually says
You are auditing an existing human-AI interaction. Use only skills from the model-interaction-design plugin. Follow this process:
Step 1: Classify the Interaction Mode
Using conversation-patterns, identify:
- What dialogue structure is being used? (interview, co-creation, instruction-execution, exploration, guided workflow)
- Is this the right structure for the task?
- Are there moments where the structure breaks down?
Step 2: Evaluate Turn-Taking
Using conversation-patterns:
- Are turns appropriately sized for the task?
- Does the AI know when to stop talking?
- Are there awkward turn boundaries?
- Rate turn-taking quality (1-5) with justification
Step 3: Assess Initiative Balance
Using mixed-initiative-flow:
- Who leads at each stage? Is this appropriate?
- Are handoffs clean or confusing?
- Does the AI take initiative when it should? Hold back when it should?
- Are there initiative anti-patterns (whiplash, passive AI, overbearing AI)?
Step 4: Check Repair Mechanisms
Using conversation-patterns and feedback-loops:
- When misunderstandings happen, how are they repaired?
- Can the user correct the AI easily?
- Does the AI acknowledge and recover from errors?
- Are there grounding checkpoints?
Step 5: Evaluate Disclosure
Using progressive-disclosure:
- Does the user know what the AI can do?
- Are capabilities revealed at the right pace?
- Is there evidence of underuse (user doesn't know about features)?
Step 6: Review Context Design
Using context-window-design:
- Does the AI maintain context across the conversation?
- Are there moments where context is lost?
- Is memory handled gracefully?
Output
Deliver an interaction audit report:
- Overall quality score (1-5) with justification
- Findings table: Issue | Severity | Skill Area | Recommendation
- Top 3 improvements ranked by impact
- Revised conversation flow showing recommended changes
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 · 46 lines · 11 tokens per session scan A 1e452ab846ba
audit-interaction is a command published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 11 tokens to every session and 454 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.