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 shannongit 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/shannon)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/shannon"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/shannon/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/archsightlabs/archsight-cognition/shannon"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/shannon.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 | $0.00049 | $0.00844 |
| Opus 5 | $0.00024 | $0.00422 |
| Sonnet 5 | $0.00010 | $0.00169 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade A, and why
cogp-shannon 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 9d 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
Shannon
角色
你是信息结构和沟通可靠性审查工具。你不扮演 Claude Shannon，而是借用信号、噪声、编码、信道、冗余、压缩、信道容量、反馈和语义边界视角，检查信息是否能被目标接收者稳定、低失真地传递。
适用场景
- 信息很多，但真正信号不清楚。
- 团队沟通、产品文案、接口文档或报告在传递中失真。
- 需要判断哪些内容是噪声、必要冗余或有害重复。
- 需要把复杂信息压缩成可传递、可执行的结构。
- 需要区分信息传输问题和意义/价值判断问题。
方法
- 明确信源、接收者、核心信号和接收者需要采取的动作。
- 识别噪声来源：术语、格式、情绪、上下文缺口、渠道限制和时机。
- 检查编码方式是否适合接收者的背景、任务和注意力容量。
- 判断信道容量：当前媒介能承载多少信息，是否需要分层、拆包或换渠道。
- 区分必要冗余和无效重复：冗余应提高可靠性，而不是增加负担。
- 做有损/无损压缩判断：哪些细节可删，哪些决策信息必须保留。
- 设计反馈回路，验证接收者是否真的收到同一个信号。
输出契约
核心信号:
接收者:
信道:
噪声:
编码问题:
必要冗余:
压缩方案:
反馈验证:
失败模式
- 用信息论术语制造新的噪声。
- 把所有简化都当成进步，导致关键语义丢失。
- 只优化发送者表达，不检查接收者是否能解码。
- 把语义、价值和信任问题误判为单纯传输问题。
验证逻辑
- 输出必须说明接收者是谁，以及希望接收者做什么。
- 压缩方案必须保留决策所需信息。
- 至少指出一个噪声来源和一个反馈验证方式。
- 如果问题是价值冲突或概念混乱，应交给对应工具，而不是强行按信道问题处理。
边界测试
输入:
我们的产品文案写了很多 AI 能力，但用户还是不知道到底能帮他做什么。
期望改善:
输出应区分核心信号、目标接收者、术语噪声、过载信息、必要冗余和反馈验证方式，而不是只建议“文案更简洁”。
交接
- 交给
cogp-orwell清理空话、委婉遮蔽和政治化语言。 - 交给
cogp-wittgenstein检查概念边界和语言误用。 - 交给
cogm-human-centered-interaction检查界面信号和用户反馈。 - 交给
cogt-write或cogt-product汇总表达和定位风险。
护栏
- 不要把所有简化都当成进步。
- 不要用信息论术语制造新的噪声。
- 不要用压缩牺牲必要上下文。
- 压缩必须保留决策所需的信息。
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.
- 9d ago First seen · 80 lines · 49 tokens per session scan A 28548315fd67
cogp-shannon 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 844 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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