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 TashanGKD/cognitive-os --skill cognitive-attendgit clone --depth 1 https://github.com/TashanGKD/cognitive-osWrote 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/tashangkd/cognitive-os/cognitive-attend)<a href="https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-attend"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-attend/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/tashangkd/cognitive-os/cognitive-attend"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-attend.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 | $0.00121 | $0.02460 |
| Opus 5 | $0.00060 | $0.01230 |
| Sonnet 5 | $0.00024 | $0.00492 |
| Haiku 4.5 | $0.00012 | $0.00246 |
Grade A, and why
cognitive-attend 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
cognitive-attend(认知信号检测 / 突显网络 SN 等效)
对应认知活动:突显网络(Salience Network)的信号检测——识别对话内容中「值得深入处理」的认知信号,主动路由给对应的 Skill。
人类大脑在处理日常输入时,SN 负责过滤:哪些信息应该引起注意?哪些需要从「背景」切换到「前景」处理?这个 Skill 模拟这个过程——让你不会遗漏隐藏在普通对话中的重要认知材料。
认知五维坐标(COG-TAX):
- 意识程度:前意识→显性(自动检测,结果呈现给用户选择)
- 脑网络:SN(检测不确定性/新颖性/重要性)
- 记忆系统:无直接写入(信号放大器,不产生独立记录)
- 执行功能:抑制(阻止忽略重要信号)
- 双系统:系统1(快速模式匹配)
理论依据:Corbetta & Shulman (2002) 双网络注意模型;Menon & Uddin (2010) SN中枢角色
知识导航表
| 层级 | 文档 | 用途 |
|---|---|---|
| D0 | 无需读取外部文档 | 信号检测基于对话内容,无文档依赖 |
使用方式
方式一:主动触发(推荐)
用户在任意对话后,说:
- 「检查一下这段话有没有认知信号」
- 「有什么值得记录的吗?」
- 「帮我识别这里的洞见」
AI 执行信号扫描,输出发现的信号。
方式二:作为对话预处理步骤(可选集成)
如果你的系统有对话管理框架(如 session-bootstrap、自定义对话循环等),可以将本 Skill 作为每轮对话的可选预处理步骤:
每轮对话 → [可选] 运行 cognitive-attend 扫描用户消息
→ 若有信号,提示用户选择是否深入处理
→ 用户选择后,触发对应 Skill
集成方法:在你的对话管理系统的"触发词扫描"步骤之后,添加:
IF 用户消息包含以下模式 → 运行 cognitive-attend:
「我发现/我意识到」+ 规律性表述
「这跟X一样/和之前的Y很像」
「但是这里不对/这和之前说的冲突」
「我又/我总是/我注意到自己」
「这说明一个规律/这在X里也成立」
激活后立即执行
Step 1 确认扫描对象
从对话上下文确认要扫描的内容:
- 若用户明确指定(「检查这段话」「刚才说的」)→ 扫描指定内容
- 若未指定 → 扫描用户最近一条或两条消息
Step 2 扫描五类显著性信号
对扫描对象全文做语义分析,逐类检测:
─ S1 洞见信号 ────────────────────────────
模式:「我发现/我意识到/我觉得」+ 原则性/规律性表述
特征:描述了可以跨情境推广的规律,而不是某次具体的事件
示例:「我发现每次压力大的时候,我会跳过验证步骤」
↓ 若检测到 → 触发建议
─ S2 跨域联想信号 ─────────────────────────
模式:「这跟X一样/和之前的Y很像/让我想到了/这个原理和」
特征:在不同领域之间发现了结构相似性
示例:「这个设计方式和我在认知科学里看到的很像」
↓ 若检测到 → 触发建议
─ S3 矛盾信号 ──────────────────────────────
模式:「但是这里不对/这和之前说的冲突/这两个是矛盾的」
特征:识别出两个论述之间的张力或不一致
示例:「但这和你之前说的「验证优先」好像是冲突的」
↓ 若检测到 → 触发建议
─ S4 反思信号 ──────────────────────────────
模式:「我又/我总是/我注意到自己/我有个习惯/我每次都会」
特征:识别自己行为/思维的反复模式
示例:「我又在没看完所有信息就下结论了」
↓ 若检测到 → 触发建议
─ S5 原则印证信号 ──────────────────────────
模式:「这说明一个规律/这在X里也成立/本质上/根本原因是」
特征:从具体案例中归纳出抽象规律,且该规律可能跨领域适用
示例:「这说明一个规律:约束越少,发散越好,但需要一个收敛触发点」
↓ 若检测到 → 触发建议
Step 3【α】 输出信号检测结果
若检测到至少一个信号:
---
🔍 **认知信号检测结果**
[对每个检测到的信号,输出一段:]
**[信号类型](S[N])**
引用片段:「[检测到的原文(10-30字)]」
信号说明:[一句话解释为什么这是一个[类型]信号]
建议:[对应操作]
---
若未检测到任何信号:
→ 简短告知:「扫描完成,当前内容中未检测到明显的认知信号。」
→ 不输出空列表,不过度解释
Step 4【γ】 等待用户选择是否处理
若有信号,在检测结果后输出选项:
「是否需要现在处理某个信号?
[处理信号1: 运行 X] [处理信号2: 运行 Y] [全部跳过,稍后再看]」
→ 若用户选择处理某信号 → 触发对应 Skill(cognitive-capture-fragment / cognitive-associate / cognitive-detect-contradiction / cognitive-self-reflect / cognitive-extract-principle)
→ 若用户选择跳过 → 结束,不记录任何内容
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.
- 10d ago First seen · 193 lines · 121 tokens per session scan A d50d2d6777d4
cognitive-attend is a skill published in the GitHub repository TashanGKD/cognitive-os (8 stars, last pushed 5mo ago), licensed MIT. It adds 121 tokens to every session and 2,460 once invoked, about $0.0006 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
cognitive-reorganize
A workflow for reorganising scattered documents into a complete personal knowledge structure.
cognitive-ask
A question-answering workflow that answers from a user's own knowledge documents, with sources, confidence levels, contradictions, and gaps made clear.
cognitive-extract-principle
A workflow for finding shared patterns across several stored thought fragments and turning them into candidate principles for review.
cognitive-integrate-fragments
A workflow for moving pending thought fragments into broader knowledge documents while preserving the existing structure.
cognitive-self-reflect
A guided self-reflection process that turns vague observations about your habits or feelings into structured records and compares them with earlier entries.
cognitive-capture-fragment
A workflow for capturing brief ideas and storing them as structured entries in a personal knowledge system.