cognitive-ask

cognitive-ask is a skill for Cursor from TashanGKD/cognitive-os. It costs 102 tokens per session (5,421 once invoked), scanned A, a copy of cognitive-ask, MIT.

A question-answering skill that answers from a user's own cognitive documents, including their knowledge maps, principles, and notes. It cites the source document and marks missing information or contradictions.

In plain words
What is it for?
It finds relevant documents, reconstructs the user's stated view on a topic, shows where that view appears, reports confidence, and identifies gaps or conflicts.
Why use it?
It helps locate a person's scattered previous thinking without adding outside opinions or pretending the documents contain answers they do not.

Skill for Cursor

Written for Cursor: installed under .cursor/.

Good fit It finds relevant documents, reconstructs the user's stated view on a topic, shows where that view appears, reports confidence, and identifies gaps or conflicts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tashangkd/cognitive-os/cognitive-ask
Install

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.

Any agent
npx skills add TashanGKD/cognitive-os --skill cognitive-ask
Clone the repo
git clone --depth 1 https://github.com/TashanGKD/cognitive-os

Made for: Cursor.

Wrote 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.

agentmods badge for cognitive-ask

README.md
[![agentmods](https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-ask/github.svg)](https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-ask)
Your own site
<a href="https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-ask"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-ask/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.

agentmods 80×15 button for cognitive-ask

Your own site · 80×15
<a href="https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-ask"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-ask.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,421 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00102 $0.05421
Opus 5 $0.00051 $0.02710
Sonnet 5 $0.00020 $0.01084
Haiku 4.5 $0.00010 $0.00542

Measured 8d ago against content hash 19f9e7a07b0a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

cognitive-ask 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 8d 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.

Origin

This is a copy

91% identical to cognitive-ask — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.cursor/skills/cognitive-ask/SKILL.md · 341 lines

How it starts

The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.

认知问答 Skill(Cognitive Ask)

严格基于用户自己的认知文档(L0/L1/L1.5/L2)回答问题。 铁律:不引入外部观点,不补充用户没有说过的内容,只用用户自己的文字和思考。


知识导航表(执行前必须理解的概念根)

层级 文档 需要理解的概念
D0 认知根(必读) cognitive/L0_brain_map.md 整个K-object体系的入口索引:各L1文档的主题/位置/版本;扫描找相关K-objects
D3 规范参考 .cursor/rules/knowledge-integrity-rules.mdc(R2 NO_FABRICATION + R3 READ_FIRST) R2:不引入用户文档中没有的观点;R3:先读相关文档再回答,不凭印象
D4 运行时数据 L0_大脑总地图.md + 根据问题确定的相关L1/L1.5/L2文档 先扫描L0找相关文档,再读相关文档后回答

核心概念速查: ① 只用用户自己的K-objects回答 = 禁止引入外部观点,禁止补充用户没有写过的内容 ② 引用必须说明来源:「在[文档名]§[章节]中提到…」——让用户能验证 ③ 知识空白 = 相关K-objects中找不到内容时,明确说「您的文档中未提及」,不自行推断


这个 Skill 解决的问题

用户在多处文档、多次对话中积累了大量思考,但:

  • 某个话题在多个文档里都有提及,散落各处
  • 不记得自己对某个问题的完整立场是什么
  • 想知道自己的某个想法在整个思维体系里处于哪个位置
  • 不确定自己是否已经想清楚了某个问题

激活后立即执行

Step 0  工作区路径探测(v2 Agent 兼容层)

        认知库有两种可能的路径布局:
        尝试读取:`docs/L0/大脑总地图.md`
        IF 成功 → **v2 模式**:L0=docs/L0/ | L1=docs/L1/ | L1.5=docs/L1_5/ | L2=docs/L2/
        ELSE 尝试:`cognitive/L0_brain_map.md`
        IF 成功 → **Cursor 模式**:使用 cognitive/ 前缀(原路径不变)
        IF 都失败 → 告知用户认知库路径未初始化,停止

        确定路径模式后,所有后续步骤中路径自动替换。
        下文中 [CL0] / [CL1] / [CL1.5] / [CL2] 分别代表对应层的实际路径前缀。

Step 1  理解问题
        → 用户的问题是什么?核心概念是什么?
        → 如果问题不清晰,追问一个最关键的问题(一次只问一个)
        → 明确问题后继续

Step 2  搜索文档(分层检索,从顶层到底层)

        [Layer 1] L1.5 底层原则层(最先检索)
        Read: [CL1.5]/底层原则库.md
        → 这个问题是否直接触及某个已确认原则(P1/P2等)?
        → 如果是,记录:「原则层有直接答案」

        [Layer 2] L1 系统性文档(必须先读 L0,再系统覆盖相关聚类)
        
        Step 2a:Read [CL0]/大脑总地图.md
        → 从 L0 提取所有聚类列表和已登记的 L1 文档名

        Step 2a.5:全量文档索引(防止 L0 过时导致遗漏)

        ⚠️ L0 可能不包含最近新建的文档——必须执行本步骤补全。

        执行 list_files([CL1]) 或等效的文件系统扫描
        → 获得 L1 目录下所有 .md 文件的完整列表
        → 与 Step 2a 从 L0 中提取的列表做 diff
        → 若发现「L0 未登记的文档」:加入待检索列表,标注「L0 未登记,来自文件系统扫描」
        → 记录:「L0 登记 N 篇,文件系统实际 M 篇,差异 K 篇」

        → 判断「问题是全局性问题还是聚焦性问题」:
          【全局性问题判断标准】满足以下任一条件,即为全局性:
          · 问题中包含「整体」「核心」「本质」「全部」「所有」「系统」「体系」
          · 问题是「X是什么」「X有哪些观点」「X的关键是什么」形式
          · 问题话题是一个跨文档的高层概念(无法被单个文档完整回答)
          → 全局性问题:**必须覆盖所有聚类(含 L0 未登记的),无例外**
          
          【聚焦性问题】:用户明确指定了单一文档/聚类(如「[B]文档里说了什么」)
          → 聚焦性问题:可以限定在指定范围内
        
        Step 2b:分两步读取每个 L1 文档(先结构,后内容)

        对每个「待检索聚类」中的文档,执行以下两步:

        **Step 2b-①(结构扫描,必须先做)**
        Read 该文档的前 80 行(涵盖摘要 + 全部章节标题)
        → 提取所有 ## / ### 章节标题,构建「章节目录」
        → 根据用户问题的关键词,标记「高相关章节」(显式理由)
        → ⚠️ 不允许「凭直觉」跳过某个聚类——必须给出明确的跳过原因

        **Step 2b-②(定向读取,基于 ① 的结论)**
        只读被标记的「高相关章节」(不是整篇)
        IF 文档整体相关性高(全局性问题 + 核心文档)→ 读全文
        → 记录:找到了哪些相关内容,在哪里,来自哪个聚类

        Step 2c:知识图谱邻域扫描 + BM25 兜底(双轨并行)

        **轨道 A:知识图谱邻域扫描**(优先,精度高)
        认知科学依据:扩散激活(Collins & Loftus, 1975)——激活一个概念节点后,
                      激活沿关系边向邻近节点传播(priming effect)

        Read: [CL0]/知识图谱_正式文档.md(或 cognitive/knowledge_graph.md)
        → 找到 Step 2b 中已确定的「主要相关L1文档」在图谱中的节点
        → 获取该节点的全部关系边(出边+入边)
        → 按关系强度排序:depends(1.0) > extends(0.9) > cross_ref(0.7) > references(0.5)
        → 选取权重 ≥ 0.7 的邻居节点(Top-5,排除已在 Step 2b 读过的)

        IF 邻居节点与当前问题高度相关(主题相似):
        → Read 该邻居文档的相关章节(每个最多读 300 字)
        → 记录:「从图谱邻域发现补充内容,来自 [文档名]([关系类型] 关系)」

        **轨道 B:BM25 关键词兜底**(知识图谱缺失时,或作为补充验证)
        触发条件:知识图谱文件不存在 OR 图谱中已读文档的关系边 < 3 条(稀疏图谱)

        提取用户问题的关键词(中文字 + 英文词)
        对 Step 2a.5 中 L0 未登记但存在于文件系统的文档:
        → 读取各文档前 200 字(标题+摘要),计算关键词命中数
        → 命中 ≥ 2 个关键词的文档:加入待检索列表,执行 Step 2b 两步读取
        → 记录:「BM25 兜底发现补充文档 [文档名],命中词:[词列表]」

        ⚠️ 约束:
        - Step 2c 是「补充」步骤,不是「替代」Step 2b 的步骤
        - 两轨道合计最多新增 3-5 个文档,避免检索范围无限扩展
        - 图谱文件不可读时轨道A静默跳过,但轨道B必须执行

        [Layer 3] L2 碎片层(以下两种情况必须执行,不允许跳过)
        
        强制触发条件(满足任一即必须读 L2):
        ① L2 碎片整合索引中,有「🔲 待整合」碎片的关联L1文档与本问题相关
        ② 问题是全局性问题(已在 Step 2a 判断为全局性时,必须读 L2 补充 L1 之外的洞见)
        
        ⚠️ 不允许以「L1 已有足够内容」为理由跳过 L2 检索——L2 碎片常包含尚未整合进 L1 的新洞见
        
        Read: cognitive/L2_fragments/fragment_index.md
        → 搜索所有「🔲 待整合」碎片,找出与问题相关的条目
        → Read 相关碎片的内容
        → 标注:L2碎片的确认程度(confirmed / tentative / uncertain)
        → 在回答中注明:「以下来自 L2 碎片,尚未整合进 L1」

Step 3  综合分析
        整理找到的所有相关内容,判断:

        ① 「清晰明确」:某文档/碎片有直接、完整的答案
        ② 「需要综合」:答案散落在多处,需要拼合
        ③ 「有矛盾」:不同文档/碎片对这个问题给出了有张力的答案
        ④ 「有空白」:文档只涉及了部分,有明显没有覆盖到的角度
        ⑤ 「完全空白」:认知库中没有覆盖这个问题

Step 4  生成回答(严格格式)

        ━━ 基于您的认知文档 ━━

        【核心答案】
        [直接回答问题,只用用户文档中的原话或紧密推论]

        【来源(必须列出每一条依据)】
        · [文档名/碎片ID] > [章节/位置]:「原文摘录」
        · [文档名/碎片ID] > [章节/位置]:「原文摘录」

        【置信度】
        [🟢 高]:文档中有明确表述,直接引用
        [🟡 中]:需要综合多处推论,可能有偏差
        [🔴 低]:仅有碎片级别的提及,尚未系统化

        【矛盾(如有)】
        发现以下不一致:
        · [文档A]说:「...」
        · [碎片F-XXX]说:「...」
        → 这两处存在[直接冲突/隐式张力],尚未消解。

        **若为🔴 致命冲突(两个结论直接互斥,任一成立另一必然错误)**:
          ⚠️ 致命矛盾——此矛盾未消解将影响本答案的可信度,强烈建议立即运行矛盾检测
          [立即运行矛盾检测] [了解但暂不检测]

        **若为🟡 隐式张力或🟢 轻微不一致**:
          是否要现在运行矛盾检测?[是] [暂时不用]

        【知识空白(如有)】
        您的文档对以下角度尚未覆盖:
        · [空白点1]:[一句话说明缺了什么]
        · [空白点2]
        是否要记录为待思考的碎片?[记录为碎片] [不用]
        ━━━━━━━━━━━━━━━━━━━━━━━━

Step 5  处理后续
        → 用户说「是」(运行矛盾检测)→ 触发 cognitive-detect-contradiction Skill
        → 用户说「记录为碎片」→ 触发 cognitive-capture-fragment Skill,预填充「待思考:[空白点]」
        → 用户有追问 → 继续在文档范围内回答(不引入外部内容)
        → 用户说「这块没想清楚」→ 询问「要运行 self-reflect(自我反思)来帮你想清楚吗?」

Read the full file on GitHub · 341 lines

Changes

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.

  1. 8d ago First seen · 341 lines · 102 tokens per session scan A 19f9e7a07b0a

Subscribe to this mod's changes

cognitive-ask is a skill published in the GitHub repository TashanGKD/cognitive-os (8 stars, last pushed 5mo ago), licensed MIT. It adds 102 tokens to every session and 5,421 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to cognitive-ask, differing in 10 lines, and is treated as a copy.