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-consolidategit 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-consolidate)<a href="https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-consolidate"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-consolidate/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-consolidate"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-consolidate.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 | $0.00107 | $0.01993 |
| Opus 5 | $0.00053 | $0.00996 |
| Sonnet 5 | $0.00021 | $0.00399 |
| Haiku 4.5 | $0.00011 | $0.00199 |
Grade A, and why
cognitive-consolidate 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 11d 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.
cognitive-consolidate(记忆巩固 / 睡眠期巩固等效)
对应认知活动:睡眠期记忆巩固(海马体→皮层长期存储)——定期将积压的L2碎片批量整合进L1,完成情节记忆→语义记忆的转化
认知五维坐标(COG-TAX):
- 意识程度:隐性(用户不参与,后台自动)
- 脑网络:DMN(离线加工,记忆重激活)
- 记忆系统:情节记忆→语义记忆的转化(海马体→皮层)
- 执行功能:无(自动化批处理)
- 双系统:系统1(自动批处理,不需要有意识的决策)
理论依据:Stickgold (2005) 睡眠依赖性记忆巩固;Wilson & McNaughton (1994) 海马体重激活;McClelland et al. (1995) 互补学习系统理论
调用方式:后台脚本,每7天执行一次(headless模式) 人工触发(可选):「执行记忆巩固」「批量整合积压碎片」
知识导航表
| 层级 | 文档 | 用途 |
|---|---|---|
| D0 碎片索引 | cognitive/L2_fragments/fragment_index.md |
找到所有「待整合且已成熟」的碎片 |
| D0 L1 目标 | cognitive/L1_knowledge/[各维度文档] |
整合的目标文档 |
| D0 L0 地图 | cognitive/L0_brain_map.md |
了解文档间依赖关系 |
激活后立即执行(Headless 模式,所有决策使用默认策略)
Step 0 成熟度检查(安全退出点)
Read: cognitive/L2_fragments/fragment_index.md
→ 筛选条件:状态=🔲待整合 AND capture_time < 今天-7天
→ 若成熟碎片 < 3条 → 静默退出(输出「巩固条件未满足,退出」到系统日志后结束)
→ 若 ≥ 3条 → 继续 Step 1
Step 1 读取成熟碎片
对每个成熟碎片 ID:
Read: cognitive/L2_fragments/[对应碎片文件]
→ 提取:碎片标题、核心内容、归属维度、capture_time、归因类型
Step 2 按关联 L1 分组
根据碎片的「归属维度」字段,将碎片分组:
组1: 归属 [维度A] 的碎片 → 目标 L1 文档 = [维度A对应文档]
组2: 归属 [维度B] 的碎片 → 目标 L1 文档 = [维度B对应文档]
...
若碎片无明确归属维度 → 归入「待分类」组,追加到待完成清单(不自动处理)
Step 3 逐组执行整合(supplement路径优先,conflict路径不自动执行)
对每个分组:
Read: cognitive/L1_knowledge/[目标文档] 全文
对每个碎片,判断整合路径:
supplement(补充)路径 ← 默认执行:
条件:碎片内容与L1现有内容兼容,可直接添加
动作:在L1文档对应章节末尾追加碎片内容(StrReplace)
追加格式:「— (来自L2碎片 [碎片ID],[capture_date])」
conflict(冲突)路径 ← 不自动执行,转入人工决策:
条件:碎片内容与L1现有核心论点存在实质矛盾
动作:追加到待完成清单(Step 4),不修改L1
covered(已覆盖)路径 ← 静默归档:
条件:碎片内容已被L1现有内容完整覆盖
动作:更新碎片状态为「📦已归档(被L1覆盖)」
Step 4 conflict 类碎片追加到待完成清单
Write: cognitive/L3_logs/todo.md(追加)
格式:
□ [巩固-YYYYMMDD] 碎片[ID]「[标题]」与[L1文档名]存在冲突,需人工决策
碎片内容摘要:[前100字]
冲突点:[一句话描述]
Step 5 更新碎片整合索引
Write: cognitive/L2_fragments/fragment_index.md
→ 已supplement碎片:状态改为「✅已整合(巩固批次 YYYYMMDD)」
→ 已covered碎片:状态改为「📦已归档」
→ conflict碎片:状态改为「⏸️冲突待决策」
Step 6 写入巩固报告
Write: cognitive/L3_logs/consolidation_report_YYYYMMDD.md(新建)
内容:
---
## 记忆巩固报告(YYYY-MM-DD)
- 处理碎片总数:N条
- supplement(已整合):X条 → [列出碎片ID和目标文档]
- covered(已归档):Y条
- conflict(待人工决策):Z条 → [列出碎片ID]
- 静默退出原因:无(或:成熟碎片不足3条)
---
Step 7 追加系统日志
Write: cognitive/L3_logs/system_log.md(追加)
格式:[LOG-YYYYMMDD-NN] cognitive-consolidate | 巩固完成:整合X条,归档Y条,冲突Z条待决策 | fragment_index.md
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.
- 11d ago First seen · 143 lines · 107 tokens per session scan A 3a6e236d769e
cognitive-consolidate is a skill published in the GitHub repository TashanGKD/cognitive-os (9 stars, last pushed 5mo ago), licensed MIT. It adds 107 tokens to every session and 1,993 once invoked, about $0.0005 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.