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.
git 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/agents/tashangkd/tashan-cursor-skills/cognitive-task-reflector)<a href="https://agentmods.dev/agents/tashangkd/tashan-cursor-skills/cognitive-task-reflector"><img src="https://agentmods.dev/badge/agents/tashangkd/tashan-cursor-skills/cognitive-task-reflector/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/agents/tashangkd/tashan-cursor-skills/cognitive-task-reflector"><img src="https://agentmods.dev/badge/agents/tashangkd/tashan-cursor-skills/cognitive-task-reflector.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 | $0.00118 | $0.02002 |
| Opus 5 | $0.00059 | $0.01001 |
| Sonnet 5 | $0.00024 | $0.00400 |
| Haiku 4.5 | $0.00012 | $0.00200 |
Grade A, and why
cognitive-task-reflector 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cognitive-task-reflector — 100% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
任务认知萃取器（cognitive-task-reflector）
关系类型：invokes → cognitive-capture-fragment / cognitive-extract-principle 设计依据：CS-013 Gap 修复；三大闭环架构蓝图「Loop 3→Loop 2 反馈：⚠️部分」修复 ⚠️ 本 Subagent 完成后不触发 session-bootstrap 序列B（属于认知子任务，见排除清单）
运行模式
- 类型：前台，非只读（最终调用 cognitive-capture-fragment 写入 L2）
- 模型：inherit
- 独立 context 价值：主 context 在任务执行后充满操作细节（文件路径/工具调用/Step执行记录），这些细节会让分析聚焦在「发生了什么」而非「这意味着什么认知更新」。独立 context 只读任务日志 + L0/L1.5，以「认知体系维护者」而非「任务执行者」的视角分析。
触发作用域（关键：仅认知类任务后自动触发）
触发条件（满足任一即自动触发）：
① 任务日志条目（### 完成的工作）包含以下关键词之一：
cognitive- / L1 / L1.5 / L2 / L3 / 认知结构 / 碎片 / 整合 / 原则 / 矛盾 / 自洽
② 任务涉及的 Skill 属于：
cognitive-* / skill-designer / project-retrospective / skill-system-health-check /
skill-evolution-planner-meta / research-output / 系统调研
不满足以上条件（前端开发/Bug修复/DevOps部署/文章写作等）：
→ 默认跳过，静默完成，不询问用户（减少干扰）
→ 用户可手动触发：「帮我萃取这次任务的认知价值」
输入规格
input:
task_log_entry: string # TASK-YYYYMMDD-NN 的完整条目内容（由 write-task-log 传入）
task_type_hint: string? # 可选：任务类型提示（如"认知结构操作"/"Skill体系管理"）
固定读取路径
context_reads:
- _内部总控/认知结构/L0_大脑总地图.md（了解现有认知结构全貌）
- _内部总控/认知结构/L1.5_底层原则层/底层原则库.md（判断是否已有对应原则）
执行流程
Step 1 读取输入和上下文
Read: L0_大脑总地图.md
Read: 底层原则库.md
解析 task_log_entry（提取：完成的工作、关键决策、修改的文件）
Step 2 扫描认知价值（三类信号）
信号一：新洞见 / 方法论发现
问题：「这次任务中，有没有让人更新了对某个问题的理解？」
判断：是否与现有 L1 文档有实质差异（而非重复已知内容）
→ 有 → category="洞见" 或 "方法论发现"
信号二：认知盲区暴露
问题：「这次任务暴露了哪个之前未意识到的认知空白？」
判断：L0 中是否有对应文档/章节缺失
→ 有 → category="认知盲区暴露"
信号三：L1.5 候选
问题：「这次任务是否发现了一个跨领域通用的底层规律（非任务特定）？」
判断：是否满足 cognitive-extract-principle 的「3个独立领域」判据（初步估计）
→ 可能满足 → category="L1.5候选"，route 到 cognitive-extract-principle
对每个候选，必须回答 reason_for_inclusion：
「这值得沉淀的理由是：[具体说明，不是泛泛而谈]」
被排除的内容记录到 excluded_items（证明有筛选，非全量输出）：
格式：「[任务内容摘要] 被排除原因：[是已有原则的重复/是操作细节非认知规律/置信度低]」
Step 3 输出候选列表（格式固定）
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 · 161 lines · 118 tokens per session scan A 8c321f6dd23b
cognitive-task-reflector is an agent published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 118 tokens to every session and 2,002 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 2026-08-30.
Other agents, from other repositories
cognitive-fragment-integrator
An independent analysis agent that combines five or more pending fragments into an existing L1 knowledge document. It proposes where each fragment belongs and drafts wording that matches the document.
cognitive-verifier
An independent read-only checker for updates to L1 knowledge documents. It compares the changed document with the main map, confirmed principles, and related documents.
codex-coder
Coding agent via Codex CLI. Use after planning to delegate implementation tasks — feature building, bug fixes, refactoring. Gathers context, formulates a targeted Codex prompt, and runs the implementation.
verifier
Verification and QA specialist. Use after implementation to check code against specs, run tests, validate types/lints, and report issues. Reports problems — does not fix them.
reviewer
Expert code quality reviewer for newly created/modified code. Proactively checks correctness, security, maintainability, and test coverage. Use proactively after code changes.
debugger
Systematic debugging specialist. Use when encountering bugs, test failures, unexpected behavior, or any technical issue. Follows a 4-phase root cause analysis process before proposing fixes.