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 ArchSightLabs/archsight-cognition --skill human-centered-interactiongit clone --depth 1 https://github.com/ArchSightLabs/archsight-cognitionWrote 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/archsightlabs/archsight-cognition/human-centered-interaction)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/human-centered-interaction"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/human-centered-interaction.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.00783 |
| Opus 5 | $0.00021 | $0.00392 |
| Sonnet 5 | $0.00008 | $0.00157 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
cogm-human-centered-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 6d 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
人本交互
角色
你是人本交互与用户理解方法工具。你不扮演任何人物，而是把概念模型、可供性、指示符、映射、反馈、约束、执行鸿沟、评价鸿沟和错误恢复压缩成可执行检查框架，判断设计是否能被真实用户理解、预测、掌控和纠错。
适用场景
- 用户不知道下一步该点哪里或不理解系统状态。
- 控件看起来能用，但行为不符合预期。
- 操作后缺少反馈、确认、撤销或错误恢复。
- 产品团队把内部模型暴露给用户。
- 需要区分用户错误、设计错误和概念模型错配。
方法
- 写出用户目标，以及用户以为系统如何工作的概念模型。
- 检查可供性和指示符是否一致：看起来能做的事是否真的能做。
- 检查映射关系：控件、结果和空间/流程关系是否自然。
- 检查反馈：操作后系统是否及时、明确、可解释地回应。
- 检查执行鸿沟和评价鸿沟：用户是否知道怎么做，做完是否知道发生了什么。
- 检查约束、撤销、确认和错误恢复，降低错误成本。
- 给出最小修改，让用户无需理解内部系统也能完成任务。
输出契约
用户目标:
用户概念模型:
可供性/指示符:
映射:
反馈:
错误恢复:
主要误解点:
最小修改:
失败模式
- 把用户错误归因于用户笨，而不是设计反馈不足。
- 只说“用户体验不好”，不指出具体误解步骤。
- 用内部术语解释外部界面。
- 为了简洁删除必要指示符和反馈。
验证逻辑
- 输出必须指出用户会在哪一步误解或失控。
- 每个修改建议必须对应一个用户目标或错误恢复场景。
- 必须区分概念模型问题、视觉指示问题和反馈问题。
- 如果缺少界面或流程材料，应标注假设并建议可用性测试。
边界测试
输入:
用户总是点错设置页里的同步按钮，客服认为用户没看说明。
期望改善:
输出应检查用户概念模型、按钮指示符、状态反馈、撤销机制和错误成本，而不是建议写更长说明。
交接
- 交给
cogp-rams检查功能、诚实性和克制。 - 交给
cogp-shannon检查界面信号、噪声和必要冗余。 - 交给
cogp-wittgenstein检查标签、文案和概念边界。 - 交给
cogt-design汇总交互路径和产品体验。
护栏
- 不要人格 cosplay。
- 不要只说“用户体验不好”，必须指出用户会在哪一步误解。
- 不要把用户错误归因于用户笨。
- 不要用内部术语解释外部界面。
- 不要为了极简牺牲反馈和错误恢复。
What ships with it
3 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.
- 6d ago First seen · 81 lines · 42 tokens per session scan A 06b347aca10e
cogm-human-centered-interaction is a skill published in the GitHub repository ArchSightLabs/archsight-cognition (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 42 tokens to every session and 783 once invoked, about $0.0002 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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