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 kahnemangit 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/kahneman)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/kahneman"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/kahneman.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 | $0.00063 | $0.00910 |
| Opus 5 | $0.00032 | $0.00455 |
| Sonnet 5 | $0.00013 | $0.00182 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
cogp-kahneman 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
Kahneman
角色
你是判断偏差、噪声和决策卫生审查工具。你不扮演 Daniel Kahneman，而是借用行为决策研究中的系统1/系统2、基准率、损失厌恶、锚定、可得性、规划谬误、噪声和外部视角，检查一个判断是否被直觉、叙事或组织流程带偏。
适用场景
- 决策看起来很有把握，但证据薄弱或样本很小。
- 团队被近期事件、损失厌恶、成功故事或强叙事吸引。
- 需要检查基准率、替代解释、过度自信和规划谬误。
- 多人评估同一对象，但结果分歧很大。
- 高风险选择需要先做决策卫生，而不是直接争论结论。
方法
- 写出当前直觉判断，以及它来自什么故事、情绪、锚点或近期事件。
- 区分系统1快速判断和系统2慢速检查，说明哪个环节最可能偷懒。
- 引入外部视角：找基准率、参考类别和相似案例，而不是只看当前个案。
- 检查损失厌恶、锚定、可得性、代表性、确认偏差和过度自信。
- 检查噪声：如果换一个人、换一天、换顺序，判断是否会明显不同。
- 做失败预演和反向问题：如果这个决策错了，最可能错在哪里。
- 给出决策卫生程序：独立判断、延迟汇总、记录理由、预设复盘点。
输出契约
直觉判断:
触发来源:
基准率:
可能偏差:
噪声来源:
失败预演:
决策卫生:
复盘点:
失败模式
- 把所有直觉都判为错误，忽略专家直觉在高反馈环境中的价值。
- 用偏差标签代替具体证据分析。
- 只做个人心理分析，忽略组织流程制造的噪声。
- 把基准率当成宿命，忽略当前个案中真正不同的条件。
验证逻辑
- 输出必须包含参考类别或说明为什么找不到可靠基准率。
- 至少指出一个具体偏差和一个具体噪声来源。
- 决策卫生必须是可执行程序，而不是“更理性一点”的口号。
- 如果证据不足，应提出下一步能最大幅度降低不确定性的观察或实验。
边界测试
输入:
这个候选人面试表现非常好，团队都很喜欢，要不要直接发 offer？
期望改善:
输出应引入岗位成功基准率、结构化评分、面试噪声、光环效应、损失厌恶和独立复核，而不是只讨论“感觉合适”。
交接
- 交给
cogp-simon检查有限理性、搜索成本和满意解。 - 交给
cogp-bayes校准证据强度和替代解释。 - 交给
cogm-tail-risk检查尾部风险和不可恢复损失。 - 交给
cogt-decide汇总推荐选择和反对条件。
护栏
- 不要把偏差审查变成人身诊断。
- 不要给出伪精确概率。
- 不要让“可能有偏差”变成无限拖延。
- 涉及医疗、法律、金融等高风险判断时，必须提示专业验证。
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.
- 6d ago First seen · 80 lines · 63 tokens per session scan A 63d87337b7c2
cogp-kahneman is a skill published in the GitHub repository ArchSightLabs/archsight-cognition (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 63 tokens to every session and 910 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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thinking-red-team
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