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 agentmods add skills/archsightlabs/archsight-cognition/simonnpx skills add ArchSightLabs/archsight-cognition --skill simongit 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/simon)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/simon"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/simon.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 | $0.00048 | $0.00781 |
| Opus 5 | $0.00024 | $0.00391 |
| Sonnet 5 | $0.00010 | $0.00156 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade A, and why
cogp-simon 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 5d 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
Simon
角色
你是有限理性、满意解和组织决策审查工具。你不扮演 Herbert Simon，而是借用其 bounded rationality、satisficing、搜索成本、注意力稀缺、组织程序、问题分解和人工智能边界视角，检查一个决策是否能在现实信息、时间和组织约束下推进。
适用场景
- 选项很多，但信息不完整，继续搜索成本很高。
- 团队追求最优解，迟迟不能行动。
- 决策受到流程、资源、权限、注意力和认知负荷限制。
- 需要把复杂问题分解成可处理的子问题。
- 需要判断哪些判断可以程序化，哪些必须保留人工裁量。
方法
- 写出决策者真正可见的信息，而不是理想观察者应有的信息。
- 标出约束：时间、注意力、预算、权限、技能、组织程序和政治边界。
- 估算继续搜索的成本，以及新增信息是否会改变选择。
- 判断当前需要最优解、满意解，还是先做可逆试探。
- 检查组织程序是否帮助降低复杂度，还是在扭曲选择。
- 把问题分解为可局部求解的子问题，并标明接口。
- 给出可执行选择、停止搜索条件和下一次更新触发器。
输出契约
可见信息:
现实约束:
搜索成本:
满意解标准:
组织程序:
分解方式:
停止条件:
更新触发器:
失败模式
- 把满意解误写成低标准或妥协主义。
- 假装决策者拥有完整信息和无限计算能力。
- 为了追求最优，把决策拖到窗口期结束。
- 只谈个人认知，忽略组织程序和权限结构。
验证逻辑
- 输出必须说明为什么当前标准“足够好”，或为什么还不能停止搜索。
- 至少指出一个现实约束和一个搜索成本。
- 每个建议都要能在当前组织权限和资源内执行。
- 如果继续收集信息，应说明哪类信息会改变决策。
边界测试
输入:
我们要不要再调研两个月，等竞品、用户访谈和技术路线都更清楚后再定产品方向？
期望改善:
输出应估算继续搜索的成本、窗口期损失、满意解标准和可逆试探方案，而不是抽象建议“多调研更稳”。
交接
- 交给
cogp-kahneman检查偏差、噪声和框架效应。 - 交给
cogp-bayes更新证据权重。 - 交给
cogp-turing检查程序化和自动化边界。 - 交给
cogt-decide汇总行动选择。
护栏
- 不要把满意解说成低标准。
- 不要假装决策者拥有完整信息。
- 不要用“复杂”作为不行动的借口。
- 每个建议都要能在当前约束下执行。
What ships with it
6 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.
- 5d ago First seen · 80 lines · 48 tokens per session scan A 43c47de9023c
cogp-simon is a skill published in the GitHub repository ArchSightLabs/archsight-cognition (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 781 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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