Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/luna-jmy/thinkdokit-skillsnpx agentmods add skills/luna-jmy/thinkdokit-skills/llmwiki-healthWrote 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/luna-jmy/thinkdokit-skills/llmwiki-health)<a href="https://agentmods.dev/skills/luna-jmy/thinkdokit-skills/llmwiki-health"><img src="https://agentmods.dev/badge/skills/luna-jmy/thinkdokit-skills/llmwiki-health/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/luna-jmy/thinkdokit-skills/llmwiki-health"><img src="https://agentmods.dev/badge/skills/luna-jmy/thinkdokit-skills/llmwiki-health.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 | $0.00071 | $0.01223 |
| Opus 5 | $0.00036 | $0.00611 |
| Sonnet 5 | $0.00014 | $0.00245 |
| Haiku 4.5 | $0.00007 | $0.00122 |
Grade A, and why
llmwiki-health 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Wiki 健康检查
对 wiki/ 做一次系统巡检，找出结构问题、内容老化和维护风险。
目标
- 找出损坏链接和孤立页面
- 检查 frontmatter、命名规范、目录归类
- 发现来源漂移和陈旧页面
- 判断索引和搜索是否同步
- 输出清晰的修复优先级
检查范围
wiki/下所有 Markdown 页面wiki/index.mdwiki/log.mdwiki/.manifest.json- 原始来源目录
- 可用时的
qmd索引状态
执行步骤
1. 建立页面清单
- 枚举
wiki/下所有.md文件 - 读取 frontmatter
- 提取正文中的
[[wikilinks]] - 构建页面图谱和入链统计
2. 做结构检查
重点检查：
- 损坏的
[[wikilinks]] - 入链为零的孤立页
- 缺少
title、type、created、updated的页面 - 正文里残留
#llmwiki的页面，以及 frontmattertags中缺少llmwiki的应复习页面 type和目录不一致的页面- 不是 kebab-case 的文件名
- 内容为空、占位或极短的页面
3. 做内容检查
重点检查：
- 长时间未更新的核心页面
- 已有更新来源，但页面未同步的主题
- 在多个来源频繁出现、却没有独立页面的实体或概念
- 明显冲突的表述、数字、日期或结论
4. 检查 manifest 与来源漂移
读取 wiki/.manifest.json 后：
- 重新计算已跟踪来源的哈希
- 找出已修改但未重摄取的来源
- 找出已删除来源
- 找出未跟踪来源
如果项目使用的哈希方式与环境有关，应沿用项目现有实现，不要引入新格式。
5. 检查索引和搜索状态
- 比较
wiki/index.md与实际页面列表 - 找出未列入索引的页面
- 找出索引里已失效的条目
- 如果
qmd可用，检查集合和索引状态是否同步
6. 输出按优先级排序的报告
建议分为三档：
- Critical
- 损坏链接
- 明显矛盾
- manifest/source 严重漂移
- index 与实际结构失配
- Warning
- 孤立页
- 过时页
- 内容过少页
- frontmatter 问题
- Info
- 缺失页面机会
- 可新增综合页
- 命名风格待统一
建议输出指标
- 总页面数
- 各类型页面数
- 总链接数
- 损坏链接数
- 孤立页数
- 过时页数
- 未跟踪来源数
- 已修改未重摄取来源数
健康评分
如果需要给分，可从 100 分开始按问题扣分，但要把规则写清楚，不要给出无依据的主观分数。
最终目标
这不是单纯报错，而是帮助用户决定下一步：
- 先跑
llmwiki-ingest - 还是先跑
llmwiki-optimize - 还是先补某几个关键页面
常用脚本
frontmatter llmwiki 标签检查与修复
健康检查发现缺失标签时，可先检查再修复：
python3 ../llmwiki-optimize/scripts/ensure_llmwiki_tag.py /path/to/wiki --check
python3 ../llmwiki-optimize/scripts/ensure_llmwiki_tag.py /path/to/wiki
建议：
- 在报告里先给出缺失 frontmatter 标签和正文残留的数量
- 需要批量修复时再执行不带
--check的版本
检查并收紧 frontmatter 复习池标签
如果健康检查发现复习列表被资料页污染，可按来源同步 frontmatter llmwiki：
python3 ../llmwiki-optimize/scripts/sync_llmwiki_by_source.py /path/to/wiki --check
python3 ../llmwiki-optimize/scripts/sync_llmwiki_by_source.py /path/to/wiki
默认仅保留 Books/ 来源页面 frontmatter tags 里的 llmwiki，并清理正文残留标签。
搜索依赖
本技能及 llmwiki 系列技能默认使用 qmd 进行词法和向量检索。如果用户未安装 qmd，应自动回退到 rag-skill 进行基于文件扫描和精确文本检索的搜索，并在输出中明确告知用户当前使用的是 rag-skill 回退模式。
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.
- 12d ago First seen · 147 lines · 71 tokens per session scan A 9c91573e00c2
llmwiki-health is a skill published in the GitHub repository luna-jmy/thinkdokit-skills (5 stars, last pushed 5mo ago), licensed MIT. It adds 71 tokens to every session and 1,223 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.
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