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-input-classifiergit 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-input-classifier)<a href="https://agentmods.dev/skills/tashangkd/tashan-cursor-skills/cognitive-input-classifier"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/cognitive-input-classifier/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-input-classifier"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/cognitive-input-classifier.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 | $0.00113 | $0.01878 |
| Opus 5 | $0.00056 | $0.00939 |
| Sonnet 5 | $0.00023 | $0.00376 |
| Haiku 4.5 | $0.00011 | $0.00188 |
Grade A, and why
cognitive-input-classifier 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cognitive-input-classifier — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
认知输入分类器(cognitive-input-classifier)
强绑定 Rule:R2 NO_FABRICATION(判断理由必须基于输入文本中实际存在的信号词) R13 RULE_BEATS_STYLE(准确判断优先于表述流畅)
核心原则
判断理由必须基于文本证据:从用户输入中找到具体信号词,引用它来支撑结论。不允许「感觉上像认知更新」这类无证据判断。
混合时强制拆分:同时发现两类信号词时,必须走「AB混合」分支,不允许取占比大的一类压过另一类。
无法判断时只有两个出口:追问一次,或默认路径B并告知可纠正。不允许输出「这是一个既有认知又有任务性质的输入」类模糊结论。
激活后立即执行
Step 1 分析输入的意图信号
从用户输入中(不是从上下文,而是从当前这段文字本身)找信号词:
认知信号词(指向路径A):
「我发现」「我觉得」「我想到」「记录一下」「碎片」「洞见」
「理解X是什么」「X的规律是」「这让我想到」「我注意到」
「想法」「感悟」「我有个看法」
「先记录」「先按规范记录」「记录一下这个洞见/方向/原则/发现」
⚠️ CO-BUILD-LOG 消歧:「先按规范记录」+「洞见/方向/规律/原则/新发现」
→ 路径A(cognitive-capture-fragment,写 L2 碎片),不是 CO-BUILD-LOG
CO-BUILD-LOG 仅用于「记录当前正在执行的建设任务的过程决策」,
而非「记录从过程中产生的知识性洞见结果」。
任务信号词(指向路径B):
「帮我做」「写X」「执行」「实现」「修复」「部署」「设计」
「新建」「创建」「分析[X并输出]」「整合[X到某处]」
祈使句动词开头且带具体可交付产物的描述
⚠️ 优先基于当前输入文字中出现的信号词做判断。
若信号词指向结论,不用上下文语义来覆盖文字证据。
Step 2 判断并输出路由结论(格式固定)
情形1:只发现认知信号词,无任务信号词
→ 输出:
「📗 路径A:认知更新。
建议触发:cognitive-capture-fragment
判断依据:输入中的「[引用实际词句]」表明这是一个认知洞见/思考记录。」
情形2:只发现任务信号词,无认知信号词
→ 输出:
「📋 路径B:任务执行。
建议触发:[基于输入的动词和领域推断对应Skill;不确定时写「[请指定Skill]」]
判断依据:输入中的「[引用实际词句]」表明这是一个需要执行的任务。」
情形3:同时发现两类信号词(AB混合)
→ 输出:
「🔀 路径AB混合。
认知部分:「[引用内容]」→ 路径A → cognitive-capture-fragment
任务部分:「[引用内容]」→ 路径B → [对应Skill]
请问先处理哪个?」
情形4:无法找到明确信号词
→ 追问(最多1次):
「❓ 无法从文字中找到明确分类信号。请问这个输入的核心目的是:
(A) 理解/记录某件事(认知更新)
(B) 做成某件事(任务执行)?」
→ 若用户回答 → 按回答输出对应情形1或2的结论
→ 若用户仍不确定 → 默认路径B:「默认路径B(任务执行),如有误请直接纠正」
Step 3 输出结论后强制停止,等待用户指令
⚠️ 关键约束:输出路由结论后,本 Skill 执行完成。
不继续执行任何其他步骤,不触发任何下游 Skill。
即使上下文中有明显的「下一步」,也不主动执行。
等待用户的明确指令:「确认」「走A」「走B」「用路径X触发」等。
若用户不认可结论:
→ 无条件接受纠正,输出:「收到,更正为路径[X],你可以直接触发 [对应Skill]」
→ 不辩解,不再次分析
What ships with it
1 file 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.
- 6d ago First seen · 136 lines · 113 tokens per session scan A 13643608f62b
cognitive-input-classifier is a skill published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 113 tokens to every session and 1,878 once invoked, about $0.0006 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.
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