Chubby Skills is a collection of 14 agent skills and command-line tools that collects videos, podcasts, articles, and local documents into a searchable personal knowledge base of Markdown files with source information. Content creators and people maintaining personal research libraries use it to save material, find supporting evidence, and export source-based research packs. Its catalogue add-ons are the project's own skills, which let coding agents collect, search, read, and package knowledge-base content, with an optional MCP server for agent access.
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/chubbyguan/chubbyskillsnpx agentmods add skills/chubbyguan/chubbyskills/knowledge-base-managementWrote 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/chubbyguan/chubbyskills/knowledge-base-management)<a href="https://agentmods.dev/skills/chubbyguan/chubbyskills/knowledge-base-management"><img src="https://agentmods.dev/badge/skills/chubbyguan/chubbyskills/knowledge-base-management/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/chubbyguan/chubbyskills/knowledge-base-management"><img src="https://agentmods.dev/badge/skills/chubbyguan/chubbyskills/knowledge-base-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low MCP Rug Pull · line 203 pip install without ==version installs the latest release, which could include malicious changes.Fix: Pin the version: pip install package==1.2.3
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 | $0.00069 | $0.06153 |
| Opus 5.5 | $0.00028 | $0.02461 |
| Sonnet 5.5 | $0.00014 | $0.01231 |
| Haiku 4.5 | $0.00007 | $0.00615 |
Grade A, and why
knowledge-base-management scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 578 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base Management — 知识库全生命周期管理
核心架构
三层架构
素材库/ → Layer 1: 不可变原始素材（AI 只读）
wiki/ → Layer 2: AI 编译的结构化知识（AI 维护）
产出/ → Layer 3: 按需生成的视图（不持久化）
核心原则：素材库是事实源（不可变），Wiki 是 AI 编译的投影（必须溯源），产出是按需生成的视图（不持久化）。
Vault 配置
vault 路径通过环境变量配置，不写死在脚本里：
export VAULT_DIR="$HOME/Documents/your-vault" # 你的 Obsidian vault 根目录
完整仓库可以统一配置采集、搜索和资料包：
python3 tools/chubby.py init --vault "$VAULT_DIR"
这里的路径是知识库根目录。采集进入根目录的 00_Inbox，默认索引为 .chubby/index.sqlite。独立安装本 skill 后，tools/vault_index.py 和 tools/evidence_brief.py 直接使用 VAULT_DIR 或 --vault，不读取仓库中的 chubby.yaml。
健康检查可自行加入调度器；安装本 skill 不会创建 cron。
1. 素材入库 (Ingest)
新素材处理流水线
用户丢素材 →
① 判断类型 → 存入素材库/对应子目录
② 提取关键信息 → 决定是否建 wiki 页面
③ 如果值得：建选题（打 SHARP 分）或更新项目页
④ 更新 wiki/index.md 索引
素材分级 (ABC Grading)
| 等级 | 标准 | 处理 |
|---|---|---|
| A | 核心方法论/框架/系统 | 优先编译为选题 |
| B | 有洞察但不够系统 | 备选，作为 A 的补充 |
| C | 水货/重复/与定位无关 | 归档，不主动编译 |
公众号文章处理
来源一：可选的 wechat-article-exporter 外部同步
如果已自行部署该工具和调度器，可把公众号文章同步到 素材库/公众号文章/<公众号名>/。本 skill 不内置该服务或定时任务。
来源二：手机保存的文章导入 通过手机保存的文章先落到一个待处理目录（按日期），再由一个归类脚本搬入素材库并按领域筛选优质文章做 A+B 深度处理。
归类脚本（如
sync_notes_to_kb.py）与各人的目录结构强相关，本仓库不内置，按下面的「工作流」自建即可。
工作流：
新文章入库 →
① 脚本自动归类到 素材库/公众号文章/<公众号名>/
② URL 去重（跳过已存在的文章）
③ 领域关键词筛选（AI/半导体/消费/电商/品牌/投资/Agent/Skill）
跳过纯营销、与用户领域无关的文章
④ 命中关键词的精选文章 → A+B 双轨处理（每日上限 5 篇）
A层：观点提取+分类+3条Takeaway → wiki/📡 外部输入/公众号/<公众号名>/<主题>/
B层：问题链+伴读引导 → 同上
⑤ 更新 wiki/index.md（外部输入 — 最新 A+B 处理 表格）
关键原则：
- 聚焦高价值主题（匹配用户兴趣），不追求全量覆盖
- 每个 wiki 页面必须用
sourcefrontmatter 列出引用素材路径 - 深度处理上限 5 篇/天，分批消化
- 文章路径格式：
[[../../../../素材库/公众号文章/<公众号>/<文件名>]]
2. 健康检查 (Audit)
自动检查项
手动运行脚本或配置自己的调度器后，检查：
- 断链（broken wikilinks）
- 缺 source 字段
- frontmatter 缺失
- 待沉淀概念
- 低链接密度
脚本：scripts/vault_health_check.py（本仓库提供，零依赖，纯标准库）
# 指定 vault 路径
python3 scripts/vault_health_check.py "$HOME/Documents/your-vault"
# 或读 VAULT_DIR 环境变量
export VAULT_DIR="$HOME/Documents/your-vault"
python3 scripts/vault_health_check.py --json
# 把缺 source 字段也视为问题（强溯源场景）
python3 scripts/vault_health_check.py --require-source
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +67 lines · +20 tokens per session 634a1b89d124
- 1mo ago Changed · +5 lines 643730c54375
- 1mo ago First seen · 506 lines · 49 tokens per session scan A 4fcbc3bfc34b
knowledge-base-management is a skill published in the GitHub repository chubbyguan/chubbyskills (1,276 stars, last pushed 9d ago), licensed MIT. It adds 69 tokens to every session and 6,153 once invoked, about $0.0003 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.
Other skills, from other repositories
wiki-dashboard
Create dynamic, queryable Obsidian dashboard views using Bases or Dataview. Use for auto-updating tables, indexes, or structured views of vault content; not for ordinary question answering.
wiki-switch
List, create, or switch named Obsidian wiki vault profiles under the global config directory. Use when changing the active vault, checking which vault is active, or managing multi-vault profiles.
daily-update
Run or configure the daily wiki maintenance cycle: check source freshness, refresh the index and hot.md, and manage its scheduled 9 AM launchd/systemd/cron reminder. Use for daily or morning wiki refresh or scheduler setup and verification.
how-we-define-chaos
How Context scores a workspace's chaos, and how to organize notes so it goes down. Use when the user asks you to tidy, organize, clean up or restructure their notes or folders, asks about their chaos score, or when a write or move answer reports the score got messier.
any-mcp
Use when an agent needs to read, search, create, edit, organize, upload, or chat with Anytype through the bounded any-mcp server. Covers safe tool selection, exact identifier chaining, optional toolsets, common PKM workflows, and the narrow anyr fallbacks for chat subscriptions and rich chat blocks.
21-day-self-interview
A 21-night guided self-reflection routine in which an agent asks three questions each night, remembers the answers, and reflects on them at key points.