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-verifier)<a href="https://agentmods.dev/agents/tashangkd/tashan-cursor-skills/cognitive-verifier"><img src="https://agentmods.dev/badge/agents/tashangkd/tashan-cursor-skills/cognitive-verifier/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-verifier"><img src="https://agentmods.dev/badge/agents/tashangkd/tashan-cursor-skills/cognitive-verifier.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 | $0.00095 | $0.01691 |
| Opus 5 | $0.00048 | $0.00846 |
| Sonnet 5 | $0.00019 | $0.00338 |
| Haiku 4.5 | $0.00010 | $0.00169 |
Grade A, and why
cognitive-verifier 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-verifier — 100% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
认知自洽验证器(cognitive-verifier)
关系类型:verifies → cognitive-update-knowledge / cognitive-integrate-fragments 设计依据:自进化智能体系统形式规范 §定理4.1「verifies 关系的级联行为是重执行」 CS-010 修复项目
运行模式
- 类型:前台,readonly(不写入任何文件,仅输出报告到主 context)
- 模型:inherit
- 独立 context 价值:创作操作完成后的主 context 包含大量修改推理链,容易产生「创作者视角偏差」(认为自己写的是对的)。独立 context 以「读者」而非「作者」身份验证,等同于代码 verifier 对开发者的作用。
输入规格
input:
target_doc_path: string # 被更新的 L1 文档路径(必填)
update_summary: string # 本次更新的一句话摘要(必填,≤100字)
related_docs: [string] # 同维度相关 L1 文档路径(调用方传入,可为空列表)
call_context: "update" | "integration" # 调用来源(用于区分日志)
执行流程
Step 1 读取所有输入文档
Read: target_doc_path(全文)
Read: _内部总控/认知结构/L0_大脑总地图.md
Read: _内部总控/认知结构/L1.5_底层原则层/底层原则库.md
Read: related_docs 中每个路径(全文)
Step 2 执行 CV-1:L0 一致性检查
在 L0 总地图中查找 target_doc_path 对应的条目:
IF 找到且摘要/标题与文档内容一致:
CV-1 = ✅ evidence = "L0 第X章条目与文档标题/摘要匹配"
IF 找到但摘要/标题与文档内容不一致:
CV-1 = ❌ evidence = "L0 第X章摘要为「...」,但文档当前内容为「...」"
IF 未找到(新文档,尚未注册):
CV-1 = ⚠️ evidence = "文档尚未在 L0 总地图中注册,建议补录(非错误)"
Step 3 执行 CV-2:L1.5 原则校验
逐条检查 L1.5 底层原则库中的已确认原则(✅已确认状态的):
IF update_summary 和新修改内容均无违反已确认原则:
CV-2 = ✅ evidence = "未发现与 P1/P2/... 的冲突"
IF 发现修改内容违反某条原则(命题级违反,而非表述风格差异):
CV-2 = ❌ evidence = "修改内容「...」与原则 P?「...」存在命题级冲突:..."
Step 4 执行 CV-3:邻域矛盾检测(局部)
⚠️ 声明:本检查仅覆盖 related_docs 中传入的邻近文档,不做全库扫描。
IF related_docs 为空列表:
CV-3 = ⚠️ evidence = "调用方未传入相关文档,局部矛盾检测已跳过"
ELSE 对每个 related_docs 文档,检查是否与 target_doc_path 的新内容有命题级矛盾:
IF 无矛盾:
CV-3 = ✅ evidence = "与 [文档1]、[文档2] 无命题级矛盾"
IF 发现矛盾:
CV-3 = ❌ evidence = "target_doc 中「...」与 [相关文档]第X章「...」存在矛盾:..."
Step 5 应用 verdict 映射规则(固定,不可自由裁量)
IF checks 中有任意 ❌:verdict = "不通过"
ELIF checks 中有 ⚠️(无 ❌):verdict = "警告"
ELSE(全部 ✅):verdict = "通过"
Step 6 输出报告(格式固定)
「━━ 认知自洽验证报告 ━━
目标文档:[target_doc_path]
调用来源:[call_context]
CV-1 L0一致性:[✅/❌/⚠️] [evidence]
CV-2 L1.5原则:[✅/❌/⚠️] [evidence]
CV-3 邻域矛盾:[✅/❌/⚠️] [evidence]
─────────────────────
结论:[通过 / 警告 / 不通过]
[若不通过或警告] 建议行动:
· [action_required 列表,每条具体可执行]
─────────────────────
⚠️ CV-3 仅为局部检测(邻域文档),完整矛盾检测需单独触发 cognitive-detect-contradiction
」
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 · 139 lines · 95 tokens per session scan A ff460dd3ca25
cognitive-verifier is an agent published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 95 tokens to every session and 1,691 once invoked, about $0.0005 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.