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-work-alignment-checkgit 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-work-alignment-check)<a href="https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-work-alignment-check"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-work-alignment-check/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-work-alignment-check"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-work-alignment-check.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 | $0.00113 | $0.01616 |
| Opus 5 | $0.00056 | $0.00808 |
| Sonnet 5 | $0.00023 | $0.00323 |
| Haiku 4.5 | $0.00011 | $0.00162 |
Grade A, and why
cognitive-work-alignment-check 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.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
认知-工作对齐检查（cognitive-work-alignment-check）
设计意图：你的认知体系（L1.5原则 + L1文档）应该是所有判断和行动的根基。 如果某项工作无法追溯到任何 L1.5 原则或 L1 文档，它就是「漂浮工作」—— 做完了，但没有认知依据，也无法通过工作反哺认知体系。
认知科学依据：元认知监控（Flavell, 1979）—— 意识到「我为什么这么判断」比「我判断了什么」更重要。
知识导航表（执行前阅读）
| 层级 | 文档 | 用途 |
|---|---|---|
| D0 认知根 | cognitive/L0_brain_map.md |
了解当前认知体系的全貌（有哪些 L1 文档，覆盖哪些领域） |
| D0 原则 | cognitive/L1.5_principles/principles.md |
已确认的底层原则列表（对齐检查的基准） |
激活后立即执行
Step 0 读取认知基准
Read: cognitive/L0_brain_map.md
→ 提取：当前有哪些 L1 文档（按领域列出）
Read: cognitive/L1.5_principles/principles.md
→ 提取：已确认的原则列表（标记 ✅ 状态的）
Step 1【γ】 确认检查范围（用户选择）
询问用户：「你想检查哪个工作领域的对齐情况？」
选项：
A. 最近完成的具体任务（用户描述）
B. 某个持续进行的工作领域（用户描述）
C. 全部工作领域（若用户有工作域定义文档）
等待用户选择后继续。
Step 2【α】 逐项对齐检查
对每个工作产出/决策，执行：
2a. 「这个工作的目标是什么？」
→ 用一句话陈述
2b. 「这个目标/方法，可以追溯到哪条 L1.5 原则？」
→ 从 principles.md 中找到最相关的原则
→ 若找到：标记「✅ 对齐，来源：[原则编号] [原则名称]」
→ 若未找到：继续到 2c
2c. 「这个目标/方法，可以追溯到哪个 L1 文档中的概念？」
→ 从 cognitive/L1_knowledge/ 中找到最相关的文档章节
→ 若找到：标记「✅ 对齐，来源：[文档名] [章节]」
→ 若未找到：标记「⚠️ 漂浮工作，无认知根」
Step 3【α】 生成对齐报告
输出格式：
## 认知-工作对齐报告
### ✅ 已对齐的工作
| 工作产出 | 认知根 | 对应文档/原则 |
|---------|--------|------------|
| ... | L1.5 P[N] | [原则名] |
| ... | L1 文档 | [文档名][章节] |
### ⚠️ 漂浮工作（无认知根）
| 工作产出 | 当前状态 | 建议行动 |
|---------|---------|---------|
| ... | 无法追溯 | 选项A/B/C（见下文）|
### 🔍 认知缺口（工作揭示的 L1/L1.5 空白）
| 发现的空白 | 建议填补方式 |
|-----------|------------|
| ... | 运行 cognitive-capture-fragment 记录碎片 |
| ... | 运行 cognitive-extract-principle 提炼原则 |
Step 4【γ】 处理漂浮工作（用户决策）
对每项「漂浮工作」，请用户选择：
A. 「这个工作有认知根，只是我没有写进 L1/L1.5，现在补上」
→ 触发 cognitive-capture-fragment（记录对应认知碎片）
B. 「这个工作确实是漂浮的，未来应该先建立认知根再行动」
→ 记录到 cognitive/L3_logs/todo.md（待整合的认知缺口）
C. 「这个工作属于执行层（不需要认知根）」
→ 标记为「执行层工作，豁免对齐检查」
Step 5【α】 更新系统日志
追加到 cognitive/L3_logs/system_log.md：
格式：[LOG-YYYYMMDD-NN] cognitive-work-alignment-check |
对齐报告：✅X项 ⚠️Y项漂浮 🔍Z个认知缺口 | L0地图/L1.5原则库
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 · 143 lines · 113 tokens per session scan A c643e78f83e2
cognitive-work-alignment-check is a skill published in the GitHub repository TashanGKD/cognitive-os (8 stars, last pushed 5mo ago), licensed MIT. It adds 113 tokens to every session and 1,616 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.