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-calibrategit 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-calibrate)<a href="https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-calibrate"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-calibrate/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-calibrate"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-calibrate.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 | $0.02082 |
| Opus 5 | $0.00053 | $0.01041 |
| Sonnet 5 | $0.00021 | $0.00416 |
| Haiku 4.5 | $0.00011 | $0.00208 |
Grade A, and why
cognitive-calibrate 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
cognitive-calibrate(置信度校准 / 元认知置信度监控)
对应认知活动:元认知的置信度校准——追踪🟡AI生成内容是否事后得到验证,防止「自我确认偏误」导致未验证的推断长期被当作事实
认知五维坐标(COG-TAX):
- 意识程度:显性(需要用户参与确认)
- 脑网络:CEN(目标导向的检索与判断)+ SN(不确定性检测)
- 记忆系统:语义记忆的准确性评估
- 执行功能:监控(Monitoring),是元认知的核心成分
- 双系统:系统2(需要主动评估,不能自动化)
理论依据:Flavell (1979) 元认知监控;Dunning & Kruger (1999) 元认知准确性;Nelson & Narens (1990) 置信度监控框架
调用时机:
- 被 cognitive-consistency-check 内部调用(月度维护,Step 8)
- 用户主动触发(「验证历史内容」「校准知识置信度」)
Headless调用说明(被 cognitive-consistency-check Step 8 调用时):
- 调用方传入默认范围参数:scope="ALL_90_DAYS"(跳过Step 0的交互选择)
- 跳过 Step 0:不询问用户,直接使用 scope="ALL_90_DAYS" 执行 Step 1
- 若筛选结果 > 10条:Headless模式下只处理前10条(避免过长执行)
- 所有 Step 4 的结果写入 todo.md,等待下次 daily-briefing 展示给用户
- 不向对话输出 Step 4 的摘要(Headless模式静默执行)
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「归因=🟡AI生成/推断,验证状态=未验证」的条目 |
| D0 L1 文档 | cognitive/L1_knowledge/[各维度文档](含🟡标注的段落) |
找到需要验证的具体内容 |
激活后立即执行
Step 0 确认检查范围
Ask: 本次校准的范围?
选项 A:90天以上未验证的🟡内容(全量,可能较多)
选项 B:指定某个L1文档(精准,快速)
选项 C:从上次校准以来的所有🟡内容
Step 1 读取待验证条目
Read: cognitive/L2_fragments/fragment_index.md
→ 筛选:归因类型=🟡AI生成/推断 AND 验证状态=🔲未验证 AND capture_time < 今天-[范围天数]
→ 提取:碎片ID、标题、capture_time
Read: cognitive/L1_knowledge/[目标文档]
→ 扫描🟡归因标注的段落
→ 提取:段落位置、内容摘要(前150字)、标注时间
→ 若筛选结果为0条 → 告知用户「选定范围内无需校准的内容(🟡内容已全部处理,或该范围内无🟡归因内容)」
然后退出,不继续执行 Step 2 以后的步骤
Step 2 逐条向用户展示并询问
对每个待验证条目,展示:
---
📋 [内容编号/N]
**来源**:[L1文档名 §章节] / [L2碎片ID「标题」]
**当时的归因**:🟡 AI生成/推断([capture_date],距今[N]天)
**内容摘要**:[前150字]
**问题**:这段内容当时是AI的推断。现在:
A. ✅ 已有实际证据支持(标记为「已验证」)
B. ❌ 事后发现是错的(标记为「已否定」,需要修订L1/L2)
C. ⏸️ 还不确定,继续观察
D. ⏭️ 跳过这条(稍后处理)
---
Step 3 根据用户回答更新验证状态
A(已验证):
→ 更新 fragment_index.md:验证状态=✅已验证,verified_date=今天
→ 更新 L1 文档:将🟡标注改为✅(StrReplace)
B(已否定):
→ 更新 fragment_index.md:验证状态=❌已否定,verified_date=今天
→ 在 cognitive/L3_logs/todo.md 追加:
□ [校准-YYYYMMDD] 碎片[ID]/[L1段落]已被否定,需要修订对应内容
建议:运行 cognitive-update-knowledge 或 cognitive-detect-contradiction
→ 不自动修改 L1(修订需要用户确认)
C(继续观察):
→ 更新 fragment_index.md:验证状态=⏸️观察中,last_check=今天
→ 不做其他操作
D(跳过):
→ 不更新验证状态
→ 记录「已跳过」供下次校准时再展示
Step 4 生成校准摘要
输出:
---
## 置信度校准摘要([今日日期])
处理条目:N条
- ✅ 已验证:X条
- ❌ 已否定(需修订):Y条 → 已加入待完成清单
- ⏸️ 继续观察:Z条
- ⏭️ 跳过:W条(下次校准时再显示)
---
Step 5 追加系统日志
Write: cognitive/L3_logs/system_log.md(追加)
格式:[LOG-YYYYMMDD-NN] cognitive-calibrate | 校准完成:验证X条,否定Y条,观察Z条 | 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.
- 10d ago First seen · 155 lines · 107 tokens per session scan A 46ce270e384a
cognitive-calibrate 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 2,082 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.