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/map-initiative)<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/map-initiative"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/map-initiative/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/map-initiative"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/map-initiative.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 | $0.00012 | $0.00423 |
| Opus 5 | $0.00006 | $0.00211 |
| Sonnet 5 | $0.00002 | $0.00085 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
map-initiative 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.
What it actually says
You are creating an initiative map for an AI-powered workflow. Use only skills from the model-interaction-design plugin. Follow this process:
Step 1: Break Down the Workflow
Identify every stage in the workflow from start to finish:
- What triggers the workflow?
- What are the key decision points?
- What are the outputs at each stage?
- Where does the workflow end?
Step 2: Assign Initiative
Using mixed-initiative-flow, for each stage determine:
- Who leads: User, AI, or shared
- Why: What makes this the right assignment?
- Autonomy level: Full autonomy, supervised autonomy, advisory, or passive
Step 3: Design Handoff Points
Using mixed-initiative-flow, for each transition:
- What triggers the handoff?
- Is it explicit, implicit, negotiated, or forced?
- What information transfers with control?
- What could go wrong at this handoff?
Step 4: Identify Feedback Points
Using feedback-loops, mark where:
- The user can course-correct
- The AI should check in before proceeding
- Implicit feedback is being collected
- Explicit feedback should be requested
Step 5: Stress Test
Using mixed-initiative-flow (anti-patterns):
- Where might initiative whiplash occur?
- Where might the AI be too passive or too aggressive?
- What happens if the user tries to take control at an AI-led stage?
- What happens if the user goes silent at a user-led stage?
Output
Deliver a complete initiative map:
- Stage-by-stage table: Stage | Leader | Autonomy Level | Handoff Type | Feedback Point
- Visual initiative timeline (text-based) showing control flow
- Handoff protocol specifications for each transition
- Risk assessment for each handoff point
- Recommendations for rebalancing initiative if 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 · 43 lines · 12 tokens per session scan A 02f65b19b842
map-initiative is a command published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 423 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
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specify
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converge
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implement
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