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.
npx skills add Owl-Listener/ai-design-skills --skill mixed-initiative-flowgit 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/skills/owl-listener/ai-design-skills/mixed-initiative-flow)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/mixed-initiative-flow"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/mixed-initiative-flow/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/skills/owl-listener/ai-design-skills/mixed-initiative-flow"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/mixed-initiative-flow.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 | $0.00023 | $0.00515 |
| Opus 5 | $0.00012 | $0.00258 |
| Sonnet 5 | $0.00005 | $0.00103 |
| Haiku 4.5 | $0.00002 | $0.00052 |
Grade A, and why
mixed-initiative-flow 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 12d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mixed-Initiative Flow
Mixed-initiative interaction is when both the human and the AI can take the lead. The designer decides who drives at each moment — and how control transfers between them.
Initiative Spectrum
Interactions sit on a spectrum:
- User-driven: The user gives instructions, the AI executes. The user controls pace, direction, and scope.
- AI-driven: The AI leads — asking questions, making suggestions, guiding the user through a process.
- Shared: Both parties contribute. The AI proposes, the user edits. The user starts, the AI finishes. Most AI products default to user-driven. The interesting design space is in shared and AI-driven modes.
Designing Initiative Handoffs
The moment control shifts from one party to the other is where most interactions fail. Design these transitions:
- Explicit handoff: "I've drafted three options. Which direction do you want to go?" — the AI clearly passes control.
- Implicit handoff: The AI stops generating and waits, signalling the user's turn through UI affordance.
- Negotiated handoff: "I could take this further or stop here for your input. What do you prefer?"
- Forced handoff: The AI encounters a decision it can't make and must hand back to the human.
When the AI Should Lead
The AI should take initiative when:
- The user is uncertain or exploring and needs guidance
- The task has a known best-practice sequence the AI can walk through
- The user has explicitly asked for help or coaching
- Proactive suggestions would save time without being intrusive
When the User Should Lead
The user should retain control when:
- The task involves subjective judgment or creative direction
- Stakes are high and errors are costly
- The user has strong domain expertise
- Privacy or consent decisions are involved
Anti-Patterns
- Initiative whiplash: Control bouncing back and forth too rapidly
- Passive AI: Never taking initiative even when it would help
- Overbearing AI: Taking over when the user wants control
- Unclear ownership: Neither party knows whose turn it is
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.
- 12d ago First seen · 40 lines · 23 tokens per session scan A ff3c445676c6
mixed-initiative-flow is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 515 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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