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-review-brain-mapgit 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-review-brain-map)<a href="https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-review-brain-map"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-review-brain-map/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-review-brain-map"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-review-brain-map.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 | $0.00061 | $0.01123 |
| Opus 5 | $0.00030 | $0.00562 |
| Sonnet 5 | $0.00012 | $0.00225 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
cognitive-review-brain-map 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 12d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
大脑地图复盘 Skill（Review Brain Map）
读取认知结构各层状态，生成「今日认知快照」，帮助用户快速恢复上下文，决定下一步行动。
激活后立即执行
Step 1 读取全系统状态
Read: cognitive/L0_brain_map.md
Read: cognitive/L2_fragments/碎片整合索引.md
Read: cognitive/L3_logs/待完成总清单.md
Read: cognitive/L1.5_principles/principles.md（只读候选原则部分）
Step 2 统计关键指标
从碎片整合索引统计：
→ 待整合碎片总数（🔲状态）
→ 部分整合数（⚠️状态）
→ 待整合碎片的类型分布
从待完成总清单统计：
→ 🔴 高优先级待处理数量
→ TOP 3 最紧急项（标题+涉及文档）
从L1.5原则库统计：
→ 已确认原则数量
→ 候选原则数量（🟡状态）
从L0文档读取：
→ 各L1文档的最后更新时间（找出最久未更新的）
→ 当前Gap状态（已完成/待处理）
Step 3 生成状态快照，清晰展示
「━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🧠 认知状态快照（今日：YYYY-MM-DD）
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📝 待整合碎片：N 个（类型：产品理论M个、自我反思K个...）
📋 高优先级待办：M 项
💡 候选原则待确认：K 个
📅 最久未更新L1文档：[文档名]（N天前）
⚠️ 当前已知缺口：X 个（见L0）
TOP 待处理项：
1. [TODO-XXX] [高优先] [描述] → 涉及[文档名]
2. [TODO-XXX] [高优先] [描述] → 涉及[文档名]
3. [TODO-XXX] [中优先] [描述] → 涉及[文档名]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━」
Step 4 建议今日优先处理（根据积压情况自动推断）
基于数量和优先级，给出1-3条建议：
例：
「建议今日优先：
[1] 处理 N 个待整合碎片（运行「整合碎片」）
[2] 确认候选原则 P? 是否成立（运行「提炼原则」）
[3] 完善 [文档名]（运行「更新知识」）
[开始处理第1项] [开始处理第2项] [开始处理第3项] [我来决定]」
Step 5 追加 cognitive/L3_logs/system_log.md
[LOG-今日日期-NN] cognitive-review-brain-map | 生成认知快照 | 无文档变更
注意事项
- 快照是只读的，本 Skill 不修改任何 L0/L1/L2/L3 文档内容
- 如果 L0 不存在：「L0 大脑总地图不存在，建议先运行认知结构初始化」
- 快照后直接建议行动，不要只展示数据，要帮用户做决策
- 一次复盘后如果用户选择处理某项，直接过渡到对应 Skill，不要让用户重新说触发词
D5：任务完成后的 Loop 反馈
本次执行产出：S-object 认知快照（对话输出，系统日志追加一行） 产出位置：每次快照输出到对话中，无需写入文件（用户直接消费）
Loop 路由：
- 通路B（Loop 3 → Loop 2）： → 若快照中发现"某个 L1 文档内容已大幅落后于实际认知状态"：记录到 cognitive/L3_logs/todo.md（待处理洞见） → 若发现"某个 Loop 的某条通路长期未激活（如通路E 30天无记录）"：记录到 cognitive/L3_logs/todo.md（待处理缺口）
- 系统日志：
→ 每次快照完成后，追加一行到
cognitive/L3_logs/system_log.md→ 格式：[LOG-YYYYMMDD-NN] cognitive-review-brain-map | 生成认知快照 | 无文档变更
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.
- 12d ago First seen · 92 lines · 61 tokens per session scan A a63adedc838d
cognitive-review-brain-map is a skill published in the GitHub repository TashanGKD/cognitive-os (9 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 1,123 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.
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-integrate-fragments
A workflow for moving pending thought fragments into broader knowledge documents while preserving the existing structure.
cognitive-capture-fragment
A workflow for capturing brief ideas and storing them as structured entries in a personal knowledge system.
cognitive-extract-principle
A workflow for finding shared patterns across several stored thought fragments and turning them into candidate principles for review.
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.