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/tashan-cursor-skills --skill cognitive-extract-principlegit clone --depth 1 https://github.com/TashanGKD/tashan-cursor-skillsWrote 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/tashan-cursor-skills/cognitive-extract-principle)<a href="https://agentmods.dev/skills/tashangkd/tashan-cursor-skills/cognitive-extract-principle"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/cognitive-extract-principle/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/tashan-cursor-skills/cognitive-extract-principle"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/cognitive-extract-principle.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 | $0.00078 | $0.03263 |
| Opus 5 | $0.00039 | $0.01631 |
| Sonnet 5 | $0.00016 | $0.00653 |
| Haiku 4.5 | $0.00008 | $0.00326 |
Grade A, and why
cognitive-extract-principle 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.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
L1.5原则提炼 Skill(Extract Principle)
实现「DMN模拟」:从多个L2碎片中发现跨领域共同模式,提炼为L1.5候选原则,经用户确认后正式沉淀。
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) | _内部总控/认知结构/L1.5_底层原则层/底层原则库.md |
K0原则的定义、现有原则列表(P1~Pn)、原则的稳定性要求(几乎不变) |
| D3 规范参考 | _内部总控/认知结构/维护协议_自洽规范.md |
原则确认流程:候选→用户确认→正式写入的三步要求 |
| D4 运行时数据 | _内部总控/认知结构/L2_碎片化思考/碎片整合索引.md |
所有🔲待整合碎片(提炼原则的原材料) |
核心概念速查: ① K0原则 = 最高稳定性的K-object,跨所有场景成立,几乎不变——修改须谨慎 ② 提炼模式:≥3个碎片中出现相同底层模式,才候选升格为原则(避免过度泛化) ③ 用户确认是K0 ceremony的必要步骤:不允许AI自行写入原则库,必须等用户明确确认
激活后立即执行
Step 1 读取碎片数据
Read: _内部总控/认知结构/L2_碎片化思考/碎片整合索引.md(全部条目)
Read: 全部L2碎片文件内容(按类型分批读取)
Read: _内部总控/认知结构/L1.5_底层原则层/底层原则库.md(已有原则,避免重复提炼)
Step 2 分析跨碎片共同模式
问题一:哪些碎片背后有相同的驱动逻辑?
→ 比较碎片的核心观点,找出「即使话题不同,但背后遵循同一个规律」的碎片群
→ 最少需要3个独立领域的碎片支持,才算可能成立的原则
问题二:这个模式是否已被现有L1.5原则覆盖?
→ 与P1(验证优先于感受)、P2(从小点切入升维)对比
→ 如果是已有原则的印证 → 记录为「印证了P?」,不新建原则
→ 如果是新模式 → 继续
Step 2.5 [候选原则冲突检测](T4B 新增:在提出候选前检测与现有候选原则的冲突)
快速扫描 底层原则库.md §二(候选原则列表 P4?~P17? 等):
→ 新发现的模式,与哪个现有候选原则在「某类场景下建议方向相反」?
→ 若发现冲突:
「新发现的模式与候选原则 P??「[表述]」在以下场景存在优先级张力:
[冲突场景描述]
建议:在候选提出时明确说明两者的优先级关系。」
→ 将冲突关系纳入 Step 3 的候选展示中(供用户参考)
→ 若无冲突:静默通过,直接进入 Step 3
Step 3 提出候选原则
对每个发现的新模式,生成:
「━━ 候选原则 ━━
候选表述:「[跨领域通用的原则,一句话]」
印证碎片(N 个,跨 M 个独立领域):
┌─────────────────────────────────┐
│ F-XXX(领域:产品理论) │
│ 「碎片摘要...」 │
│ F-YYY(领域:自我反思) │
│ 「碎片摘要...」 │
│ F-ZZZ(领域:组织设计) │
│ 「碎片摘要...」 │
└─────────────────────────────────┘
关卡C:跨3个以上独立领域?[✅是 / ❌否]
置信度:[高 | 中 | 低](基于印证数量和清晰程度)
[✅ 确认为新原则,写入L1.5] [🔧 修改表述后确认] [❌ 不确认,继续观察]」
Step 3.5 【F-022 全节点挑战者反思】候选原则提出后、用户确认写入前执行
以「原则体系批判者」视角执行3条挑战(在候选展示中附上挑战结论,供用户参考):
1. 是否真的新:这条候选原则与 P1「验证优先于感受」和 P2「从小点切入升维」的本质区别
是什么?能不能用一句话证明它不是P1或P2的特例?
2. 反例构造:能否构造一个明确的反例——在某个场景下,遵循这条原则反而会
导致更差的结果?如果很容易构造反例,候选原则的适用范围需要限定。
3. 操作化检验:这条原则能否转化为一个「可以判断是否遵循了该原则」的具体行为描述?
如果无法操作化,它可能还是一个模糊的价值观而非可执行的心智模型。
挑战结论附在候选原则展示中:「挑战视角:[3条挑战的简短结论]」
用户在看到候选原则时,同时看到挑战视角,再决定确认/修改/否定。
Step 4 执行确认的新原则写入
用户确认后:
a. Write:追加到 L1.5/底层原则库.md(新原则章节,含状态=✅已确认)
b. 更新碎片整合索引.md:为相关碎片的「L1.5原则」字段填写新原则ID
c. 执行L1约束检查(见 Step 5)
d. 追加 L3/系统日志.md
Step 4e 后台触发 cognitive-cascade-notifier(CS-011 修复,不阻断主流程)
⚠️ 本步骤在 Step 4 完成后立即执行,不等待后台结果,主流程继续 Step 5
输入:{
change_type: "new_principle",
change_summary: [新原则表述(与 Step 3 候选表述相同,一句话)],
principle_id: [新原则编号,如 "P15",必填,从 Step 4a 写入时确认]
}
后台 cognitive-cascade-notifier 将分析五域影响并写入待完成总清单。
主流程继续 Step 5 不受影响。
Step 5 [L1约束检查] 新原则对现有L1文档的影响(CS-003 + E1C 修复)
Read: 全部L1文档标题和摘要
→ 找出哪些L1文档内容可能需要在新原则的约束下重新审视
→ 生成检查清单,并立即提示用户处理意愿:
「新原则「...」对以下L1文档有约束意义,建议同步更新:
□ [文档A] - 第X章(涉及判断标准的设计)
□ [文档B] - 第Y节(涉及执行流程)
建议:触发 cognitive-update-knowledge 逐一更新上述文档,
确保 L1 文档引用新原则编号,体系保持一致。
是否现在逐一检查?[逐一审查(调用cognitive-update-knowledge)] [留待下次处理]」
【强制执行 E1C】无论用户是否选择立即审查,都必须执行以下两步:
a. 【强制】Write: 追加到 L3/待完成总清单.md:
```
□ [新原则P?]约束检查:验证以下L1文档是否与新原则对齐:[文档列表]
来源:cognitive-extract-principle 自动生成 | 日期:YYYY-MM-DD
```
b. 告知用户:「✅ 已将 N 个 L1 文档检查任务写入待完成清单,随时可处理。」
Step 6 如果没有发现新原则
「在当前碎片库中,未发现新的跨领域共同模式。
现有原则(P1/P2)已能覆盖大部分碎片的底层逻辑。
建议:继续积累碎片后再次运行。当前已有 N 个「待观察」碎片。」
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 207 lines · 78 tokens per session scan A 200f2d0b017e
cognitive-extract-principle is a skill published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 78 tokens to every session and 3,263 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
cognitive-consistency-check
A full validation of a structured knowledge system against ten consistency rules, including existing references, declared relationships, document status, and non-contradictory content.
cognitive-extract-principle
A Chinese-language skill for finding recurring patterns across separate notes and turning them into candidate underlying principles.
cognitive-update-knowledge
A controlled workflow for updating a core knowledge document, including impact analysis, backups, consistency checks, and related record updates.
cognitive-self-reflect
A guided self-reflection tool that turns personal observations into written patterns and compares them with earlier reflections over time.
cognitive-attend
A signal detector for finding notable ideas in conversation. It looks for insights, connections between topics, contradictions, reflections, and evidence supporting a principle.
cognitive-background-synthesizer
A background process that looks for connections between recent notes and completed tasks, then writes possible patterns into a system log.