cognitive-self-reflect

cognitive-self-reflect is a skill for Cursor from TashanGKD/cognitive-os. It costs 71 tokens per session (3,531 once invoked), scanned A, original, MIT.

A guided self-reflection tool that turns personal observations into written patterns and compares them with earlier reflections over time.

In plain words
What is it for?
Use it when reflecting on a habit, recurring problem, feeling, or behaviour and wanting to record examples, patterns, and possible next steps.
Why use it?
It helps make vague feelings or recurring habits more specific and easier to recognise. It can also identify repeated thinking without follow-up action.

Skill for Cursor

Written for Cursor: installed under .cursor/.

Good fit Use it when reflecting on a habit, recurring problem, feeling, or behaviour and wanting to record examples, patterns, and possible next steps.

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

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

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Your own site · 80×15
<a href="https://agentmods.dev/skills/tashangkd/cognitive-os/cognitive-self-reflect"><img src="https://agentmods.dev/badge/skills/tashangkd/cognitive-os/cognitive-self-reflect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,531 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 original No closer match found 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 $0.00071 $0.03531
Opus 5 $0.00036 $0.01766
Sonnet 5 $0.00014 $0.00706
Haiku 4.5 $0.00007 $0.00353

Measured 10d ago against content hash 108fbbbfe6df, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

cognitive-self-reflect 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.

.cursor/skills/cognitive-self-reflect/SKILL.md · 243 lines

How it starts

The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.

自我反思 Skill(Self Reflect)

引导者模式:帮用户把模糊的自我观察变成清晰的认知模式记录,并与历史反思做跨时间比对,识别重复出现的模式。


激活后立即执行

Step 1  读取历史反思记录
        Read: cognitive/L2_fragments/自我反思记录/自我反思记录.md
        → 记住已有的反思条目数量和关键模式(用于Step 3的历史比对)
        → 内部初始化:追问计数 = 0,五维度覆盖状态全部置为 ❌

Step 2  引导者模式(默认进入,不判断"够不够"就跳过)

        ⚠️ 严格规定:不允许在 Step 2 判断用户"已经描述清楚"而跳到 Step 3。
        所有反思都必须经历完整的追问深度,即使用户主动提供了信息。

        每轮只问一个最关键的问题(一次只问一个),等用户回答后追问计数+1:

        [现象维度优先问]
        - 「这个[习惯/感觉]是在什么具体情境下出现的?」
        - 「能举一个最近的具体例子,从头到尾描述一下吗?」
        - 「这个模式有多频繁?最近1个月里大概出现了几次?」

        追问计数 ≥ 3 且现象维度已覆盖 → 进入 Step 3

Step 3  历史模式比对 + 反刍检测(苏格拉底模式)
        认知科学依据:Treynor et al., 2003;反刍(mPFC过激活+FPCN调控缺失)
        vs 反思(DMN+FPCN协作),区别在于:是否产生新洞见 + 是否有行动导向

        [A] 检测「同主题出现次数」
        → 统计历史反思记录中,同一主题/错误模式在过去 30 天内的出现次数 N

        [B] 反刍风险判断(同时满足以下两条才标记)
        条件1:N ≥ 3(同主题30天内≥3次)
        条件2:前 N-1 次记录中,没有「应对方向」字段的实际执行记录
               (判断依据:条目内是否有「执行了[具体行动]」类的后续注记)

        IF 反刍风险触发(两条件同时满足):
        → 在继续追问之前,输出:
          「⚠️ 反刍检测:过去30天内,关于「[主题]」你已记录了N次。
           认知科学研究发现,反复思考同一问题但不行动,会强化而非消解这个模式。
           
           建议在继续记录之前先选择一个方向:
           [继续深度反思(本次有新发现)]
           [转化为待完成事项(行动优先)]
           [提炼为L1.5候选原则(这已经是一个规律)]」
          → 等用户选择后继续

        IF 用户选「继续深度反思」→ 正常进入 Step 4,但 Step 7 必须输出行动提醒
        IF 用户选「转化为待完成事项」→ 直接写入 L3/待完成总清单.md,跳出反思流程
        IF 用户选「提炼为L1.5候选原则」→ 触发 cognitive-extract-principle

        [C] 无反刍风险的历史比对(原逻辑)
        → 如果发现相似历史模式(但不满足反刍条件):
          「我注意到你在[日期]也记录过类似的模式——「[历史条目标题]」。
           这次看起来是同一个根因,还是有所不同?」
          → 等用户回答,追问计数+1
        → 如果是全新模式:继续 Step 4,不提及历史

Step 4  五维度强制深挖(苏格拉底模式,不允许按需跳过)

        ⚠️ 深度追问要求:必须覆盖以下5个维度,全部达到「用户已清晰表达」
        状态,且总追问轮数(Step 2+3+4 合计)≥ 10 轮,才能进入 Step 4.5。

        五个必须覆盖的维度:
        □ 维度1【现象】:具体的行为表现是什么?(含触发场景、频率、典型案例)
        □ 维度2【触发条件】:在什么具体条件/信号下出现?至少识别出3个触发信号
        □ 维度3【内在感受】:出现这个模式时,自己当时的情绪/认知状态是什么?
        □ 维度4【本质根因】:这个模式的底层驱动是什么?(必须由用户自己说出,AI不代答)
        □ 维度5【应对方向】:下次出现时怎么识别?识别后做什么不同的选择?

        每个维度的问题池(每轮只问一个,等用户回答后更新维度状态和计数):

        [维度2 - 触发条件]
        - 「这个模式在什么情况下不会出现?」(反向定位触发边界)
        - 「触发它的,是外部事件还是内部状态?」
        - 「你能列出3个典型的触发信号吗?」

        [维度3 - 内在感受]
        - 「当时你的情绪状态是什么?」
        - 「在做出这个行为之前,你有没有注意到自己的想法或感受?」
        - 「这个模式出现时,你的身体有什么感觉吗?」

        [维度4 - 本质根因]
        - 「这个模式背后,你在保护什么,或者在逃避什么?」
        - 「如果这个模式不存在,你认为会发生什么?」
        - 「这个模式在什么时候开始出现的?当时的背景是?」
        - 「这个模式的本质是什么?是用某种感受替代了某种判断吗?」

        [维度5 - 应对方向]
        - 「下次当你注意到这个触发信号时,你希望自己做什么不同的事?」
        - 「有没有一个具体的"如果X,就Y"规则可以帮你应对?」
        - 「谁或什么可以帮助你在当下识别出这个模式?」

        → 每轮等用户自己说出答案,AI 不代替用户总结
        → 用户回答后更新该维度状态(❌→✅)并追问计数+1

Step 4.5  追问门槛检查(进入写入前的强制检查点)

        检查以下两个条件是否都满足:

        ☐ 五维度完整性:
          □ 维度1【现象】:[✅/❌]
          □ 维度2【触发条件】:[✅/❌]
          □ 维度3【内在感受】:[✅/❌]
          □ 维度4【本质根因】:[✅/❌]
          □ 维度5【应对方向】:[✅/❌]

        ☐ 追问深度:总追问轮数 ≥ 10 轮?(当前:N 轮)

        → 两个条件都满足 → 进入 Step 5
        → 任一条件未满足:
          - 有未覆盖维度 → 继续追问该维度,追问计数+1
          - 追问轮数不足 → 继续追问最薄弱的维度

        ⚠️ 禁止在两个条件都满足之前进入 Step 5。

Step 5  生成结构化条目,向用户确认
        「准备记录反思条目:
          ━━ 反思 [R-ID]:[标题] ━━
          现象:[用户描述的具体表现]
          本质:[用户分析的根本原因]
          触发条件:[列出已识别的触发场景]
          应对方向:[用户提到的应对方法]
          归因:🔵 用户原始思考 + 🟢 AI引导整理
          ───────────────────────────
          [✅ 确认记录] [✏️ 修改] [不记录了]」

Step 6  执行写入(用户确认后)
        a. Write: 追加到自我反思记录.md(使用标准格式)
        b. 检查与L1.5原则的关联:
           → 是否是P1(验证优先于感受)的行为层面印证?
           → 是否是P2(从小点切入升维)的反面?
           → 如有关联,在条目末尾标注「关联L1.5:P?」
        c. 追加 cognitive/L3_logs/system_log.md

Step 7  如果与历史模式相同,提示规律
        「这已经是第 N 次记录类似的模式了(历史记录:[日期1], [日期2]...)。
          这可能是一个深层的认知习惯。是否要运行「提炼原则」来看看它能否升华为L1.5原则?
          [运行提炼原则] [暂时不用]」

Read the full file on GitHub · 243 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. 10d ago First seen · 243 lines · 71 tokens per session scan A 108fbbbfe6df

Subscribe to this mod's changes

cognitive-self-reflect is a skill published in the GitHub repository TashanGKD/cognitive-os (9 stars, last pushed 5mo ago), licensed MIT. It adds 71 tokens to every session and 3,531 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 2026-08-31.