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 GYF0311/lorekit --skill wiki-outputgit clone --depth 1 https://github.com/GYF0311/lorekitWrote 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/gyf0311/lorekit/wiki-output)<a href="https://agentmods.dev/skills/gyf0311/lorekit/wiki-output"><img src="https://agentmods.dev/badge/skills/gyf0311/lorekit/wiki-output/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/gyf0311/lorekit/wiki-output"><img src="https://agentmods.dev/badge/skills/gyf0311/lorekit/wiki-output.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 | $0.01674 |
| Opus 5.5 | $0.00038 | $0.00670 |
| Sonnet 5.5 | $0.00019 | $0.00335 |
| Haiku 4.5 | $0.00010 | $0.00167 |
Grade A, and why
wiki-output 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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wiki-output
把 corpus/知识库/ 里的已有沉淀组合成对外产物（幻灯片 / 文章 / 图表 / 汇报稿），落在 corpus/输出/{子目录}/ 下作为终态产物保留。
When to trigger
- 用户说"做个 Marp 幻灯片讲 XXX"、"给我做个分享 PPT"、"演讲稿"
- 用户说"写一篇对外文章"、"我要投公众号"、"给我一份投稿草稿"
- 用户说"出一张 matplotlib 图"、"趋势图"、"对比图"
- 用户说"汇报一下 XXX"、"写一份给 XXX 看的汇报材料"
不要触发：
- 用户只是内部 query / 综合 →
wiki-query（产物落输出/问答/） - 用户要存外部资料 →
wiki-ingest - 用户要存对话洞察 →
wiki-fileback
分工边界（重要）
| 本 skill 负责 | 不负责 |
|---|---|
读 知识库/ 综合，组织成对外叙事 |
爬外部新资料（去 wiki-ingest） |
| 按输出类型选格式（.md / .py / marp） | 回流进知识库（除非先生明确） |
落盘到 corpus/输出/{子目录}/ |
更新 concept 页 compiled truth |
frontmatter 加 graph-excluded: true |
修改 corpus/index.md 的知识库登记 |
产出形式 × 落盘路径
| 输出类型 | 文件形式 | 落盘路径 | frontmatter 关键字段 |
|---|---|---|---|
| Marp 幻灯片 | .md（含 marp: true） |
corpus/输出/幻灯片/YYYY-MM-DD-<slug>.md |
marp: true, graph-excluded: true |
| matplotlib 图表 | .py（可直接运行） |
corpus/输出/图表/YYYY-MM-DD-<slug>.py |
无 frontmatter；头部注释写数据来源 wikilink |
| 对外文章 | .md（公众号友好） |
corpus/输出/文章/YYYY-MM-DD-<slug>.md |
graph-excluded: true, target_audience: <对象> |
| 汇报材料 | .md（结构化汇报稿） |
corpus/输出/文章/YYYY-MM-DD-<slug>.md |
同上 |
铁律：所有产物 frontmatter 必含 graph-excluded: true——输出属于终态产物，不进图谱、不进入长期知识索引。
6 步流程
1. 明确输出类型与受众
先问清楚（或从上下文推断）：
- 什么类型？（Marp 幻灯片 / 对外文章 / matplotlib 图表 / 汇报材料）
- 给谁看？（领导 / 同行 / 公众号读者 / 投稿目标刊物）
- 大概多长？（几页 / 几分钟 / 几千字）
2. 读 知识库/ 相关沉淀
- 先 Read
corpus/index.md定位相关分区 - Read
{dir}/_INDEX.md定位具体页 - Read 对应 concept / synthesis / 摘要页
- 只用
知识库/的沉淀作为素材，不要临时 WebFetch 新资料（那是 wiki-ingest 的职责） - 如果素材不够，主动告诉先生："corpus 里相关沉淀只有 N 页，输出可能单薄。要不要先 ingest 一些新来源？"
3. 按输出类型组织叙事
- Marp 幻灯片：title / agenda / 分节 slides / take-aways / Q&A / sources 页。每 slide 一个核心论点 + 1-2 个 wikilink 引用
- 对外文章：标题 → 导言 → 核心论点分节（每节带 source 引用脚注）→ 结论 → 参考资料
- matplotlib 图表：头部注释写数据来源 wikilink + 生成时间；代码块可直接跑；若数据需要手工填，注释明确标"数据源 TODO"
- 汇报材料：背景 → 关键事实 → 分析 → 建议 → 附录（sources）
4. 落盘到 corpus/输出/{子目录}/
- 路径按上方表格
- frontmatter 必含
graph-excluded: true - 同步更新
corpus/log.md：## [YYYY-MM-DD HH:mm] output | <类型> | <一句话简述>
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 · 112 lines · 95 tokens per session scan A 2036f8e9ebfb
wiki-output is a skill published in the GitHub repository GYF0311/lorekit (5 stars, last pushed 3mo ago), licensed MIT. It adds 95 tokens to every session and 1,674 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 2026-08-31.
Other skills, from other repositories
wiki-ingest
Ingest exactly one research paper or source document into an open-llm-wiki vault. Use when the user explicitly asks to add, ingest, process, or publish a paper into the wiki. The workflow parses the source, drafts a source page, runs independent QA, publishes only after the quality gate passes, extracts and normalizes…
openkb-deck-neon
A skill for creating a single-file HTML slide deck from content stored in OpenKB, a knowledge-base chat system. It defines a dark, high-tech visual style using neon colors, glow effects, and glass-like panels.
openkb-deck-editorial
A tool for creating a single-file HTML slide presentation from compiled knowledge-base content. Its visual style uses a warm cream background, serif typography, and brick-red accents.
mindbase
Maintain a persistent AI-tended research wiki. Activates when the user wants to ingest a source (PDF, URL, article, paste), capture a thought, build the wiki context, query what's already known, lint for orphans/contradictions, or maintain page conventions. Phrases include "add this to my wiki", "today I decided…"…
create-an-asset
Build a customer deck, one-pager or leave-behind from your account data, call notes and approved materials, with no made-up numbers. Use when the user asks for a sales asset, one-pager, deck, leave-behind, "create an asset for [prospect]", or "make something for [account]".
wiki-knowledge-agent
Turn chat-pasted text/links into a verified, translated, searchable wiki knowledge base. When a user pastes a link or text (in ANY channel — Discord, Slack, CLI, etc.), fetch+verify it, classify it (useful vs ad), translate+summarize it into the target language, save it to the wiki, and optionally alert on…