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 5.5 | $0.00038 | $0.00676 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (22 stars, last pushed 6mo ago), licensed MIT. It adds 95 tokens to every session and 1,691 once invoked, about $0.0004 per session on Opus 5.5. 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.
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