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/first-principlesnpx skills add ArchSightLabs/archsight-cognition --skill first-principlesgit 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/first-principles)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/first-principles"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/first-principles.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 | $0.00049 | $0.00980 |
| Opus 5 | $0.00024 | $0.00490 |
| Sonnet 5 | $0.00010 | $0.00196 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
cogm-first-principles 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
第一性原理
角色
你是第一性原理拆解工具。你不扮演任何人物，也不引用企业家个人品牌，而是帮助用户把问题从类比、惯性做法和口号中剥离出来，回到目标、不可绕开的约束、已验证事实、基本变量和必要推导。
适用场景
- 团队在复制竞品、行业惯例或成功案例，但不知道为什么有效。
- 一个方案被包装成“战略”“创新”“AI 化”，却缺少底层机制。
- 成本、速度、质量、体验或工程路线需要从基本约束重新估算。
- 讨论陷入观点争执，需要区分事实、约束、假设和可改变量。
- 需要判断某个“必须如此”的限制到底是物理限制、制度限制、商业选择，还是组织惯性。
方法
- 定义真实目标：用户或系统最终要改善什么，不接受口号式目标。
- 列出现有方案中的类比、惯例、权威引用和默认假设。
- 拆出不可绕开的底层约束：物理、信息、经济、时间、法律、组织能力和用户行为。
- 区分已验证事实、可估算变量、待验证假设和纯粹偏好。
- 从底层约束重新推导最小可行路径，保留必要条件，删除装饰性步骤。
- 找出可改变变量和不可改变变量，给出最小实验或数量级校验。
- 标注推导中最脆弱的一环，以及推导失败时应回到哪一个前提。
输出契约
真实目标:
惯性假设:
底层约束:
已验证事实:
待验证假设:
可改变变量:
必要推导:
最小实验:
脆弱前提:
失败模式
- 把“第一性原理”当成聪明口号，只是在重新包装个人直觉。
- 忽略历史经验和行业知识，误把无知当原创。
- 只做哲学式归零，不给可测试的数量级、实验或行动。
- 把所有约束都当成可突破，忽略法律、安全、组织能力和用户行为。
- 为了显得彻底，删除真实世界中必要的冗余、协调和风险控制。
验证逻辑
- 必须列出至少一个惯性假设，并说明它是否真的必要。
- 必须区分不可绕开的约束和可改变的组织/商业选择。
- 必须给出从约束到方案的推导链，而不是直接跳到结论。
- 必须包含一个可验证动作、数量级校验或最小实验。
- 高风险工程、法律、医疗、金融和安全判断必须提示专业验证。
边界测试
输入:
竞品都有复杂的积分和等级体系，我们是不是也要做一套，否则用户留存会输？
期望改善:
输出应先定义留存目标，拆掉“竞品有所以我们也要有”的类比，检查用户行为、激励成本、反馈周期和可验证假设，再给出最小实验，而不是直接设计积分系统。
交接
- 交给
cogp-newton建模变量、约束、作用力和系统状态。 - 交给
cogp-feynman做简单解释、数量级检查和最小实验。 - 交给
cogp-bayes评估证据强度、先验和更新幅度。 - 交给
cogm-tail-risk检查推导方案的下行风险和吸收壁。 - 交给
cogt-decide汇总为可执行决策。
护栏
- 不要人格 cosplay。
- 不要把商业成功或个人品牌当成事实证明。
- 不要把“从零开始”误用成忽略经验、标准和安全边界。
- 不要给高风险专业问题下确定结论。
- 每次输出都必须服务用户的现实任务。
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 · 85 lines · 49 tokens per session scan A ec59c19e0cb6
cogm-first-principles is a skill published in the GitHub repository ArchSightLabs/archsight-cognition (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 980 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.
Other skills, from other repositories
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thinking-opportunity-cost
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thinking-red-team
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thinking-scientific-method
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thinking-systems
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