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 memi-design/design-skills --skill ai-interaction-designgit clone --depth 1 https://github.com/memi-design/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/memi-design/design-skills/ai-interaction-design)<a href="https://agentmods.dev/skills/memi-design/design-skills/ai-interaction-design"><img src="https://agentmods.dev/badge/skills/memi-design/design-skills/ai-interaction-design/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/memi-design/design-skills/ai-interaction-design"><img src="https://agentmods.dev/badge/skills/memi-design/design-skills/ai-interaction-design.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 | $0.00052 | $0.00650 |
| Opus 5 | $0.00026 | $0.00325 |
| Sonnet 5 | $0.00010 | $0.00130 |
| Haiku 4.5 | $0.00005 | $0.00065 |
Grade A, and why
ai-interaction-design 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Interaction Design
Design the relationship between human judgment and model behavior. Do not hide uncertainty, imply deterministic capability, or automate consequential actions without appropriate review and control.
Inputs
Establish the user goal, model role, source data, tools and actions, consequence level, latency, memory behavior, privacy constraints, evaluation evidence, failure modes, and the person accountable for the outcome.
Workflow
- Define the automation boundary. Separate what the model may suggest, draft, decide, execute, and remember. Match autonomy to consequence, reversibility, observability, and user expertise.
- Set expectations. Explain what the system can do, what information it uses, important limitations, expected latency, and when human review is required.
- Design input and context. Show what context is included, allow correction, protect sensitive information, and avoid requesting data the task does not require.
- Design progressive output. Represent queued, retrieving, generating, tool-use, awaiting approval, completed, partial, and failed states. Preserve useful partial work when safe.
- Expose provenance and uncertainty. Attach sources to claims, distinguish retrieved facts from generated inference, communicate material uncertainty, and provide a path to inspect evidence.
- Keep outputs editable. Let users revise, compare, regenerate selectively, restore earlier versions, and understand what changed. Avoid all-or-nothing regeneration.
- Gate consequential actions. Preview scope, target, cost, and side effects before execution. Require confirmation at the last responsible moment and provide receipts, undo, or escalation.
- Design memory controls. Make remembered information visible, correctable, removable, scoped, and time-bounded. Do not make personalization depend on opaque retention.
- Handle failure. Distinguish unavailable tools, insufficient evidence, policy refusal, model uncertainty, timeout, and partial execution. Offer truthful recovery rather than generic retry loops.
- Evaluate the whole workflow. Measure task success, factuality, groundedness, calibration, harmful action rate, correction burden, latency, accessibility, trust, and subgroup effects.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 44 lines · 52 tokens per session scan A 94309e153c33
ai-interaction-design is a skill published in the GitHub repository memi-design/design-skills (7 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 650 once invoked, about $0.0003 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 2026-08-31.
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