knowledge-base

knowledge-base is a skill for Claude Code from wonderwhy-er/DesktopCommanderMCP. It costs 112 tokens per session (2,165 once invoked), scanned A, original, MIT.

A method for creating a searchable collection of Markdown notes for AI agents. Markdown is plain text with simple formatting, and the collection uses an index to point agents to relevant notes.

In plain words
What is it for?
It helps start, organize, update, link, search, and clean up an agent-readable knowledge base.
Why use it?
It prevents notes from becoming a disorganized pile that agents cannot reliably find or reuse.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is - [Rate limiting](../api/rate-limiting.md) — see-also: tokens carry the rate-limit key..

Part of the desktop-commander plugin — 6 skills shipped together

Good fit It helps start, organize, update, link, search, and clean up an agent-readable knowledge base.

Compare 6 skills from other repositories ↓
About the project

Wonderwhy-er/DesktopCommanderMCP is an MCP server that lets AI clients search and edit files, run terminal commands, and manage computer processes. It is used to give coding agents practical control over a local development environment through chat. The catalogue add-ons extend or configure this server and its agent workflows.

wonderwhy-er/DesktopCommanderMCP · 10,103 stars · on GitHub · desktopcommander.app

Install

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.

Clone the repo
git clone --depth 1 https://github.com/wonderwhy-er/DesktopCommanderMCP
agentmods
npx agentmods add skills/wonderwhy-er/desktopcommandermcp/knowledge-base

Made for: Claude Code.

Or install desktop-commander, the plugin that ships this one along with the rest of its 6 skills.

Wrote 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.

agentmods badge for knowledge-base

README.md
[![agentmods](https://agentmods.dev/badge/skills/wonderwhy-er/desktopcommandermcp/knowledge-base/github.svg)](https://agentmods.dev/skills/wonderwhy-er/desktopcommandermcp/knowledge-base)
Your own site
<a href="https://agentmods.dev/skills/wonderwhy-er/desktopcommandermcp/knowledge-base"><img src="https://agentmods.dev/badge/skills/wonderwhy-er/desktopcommandermcp/knowledge-base/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.

agentmods 80×15 button for knowledge-base

Your own site · 80×15
<a href="https://agentmods.dev/skills/wonderwhy-er/desktopcommandermcp/knowledge-base"><img src="https://agentmods.dev/badge/skills/wonderwhy-er/desktopcommandermcp/knowledge-base.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 112 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,165 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00112 $0.02165
Opus 5.5 $0.00045 $0.00866
Sonnet 5.5 $0.00022 $0.00433
Haiku 4.5 $0.00011 $0.00216

Measured 1mo ago against content hash 34ca1c1fd9e6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

knowledge-base 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

plugins/claude/skills/knowledge-base/SKILL.md · 199 lines

How it starts

The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Knowledge Base (Markdown, agent-readable)

A knowledge base (KB) here is a folder of Markdown files designed so any AI agent can navigate it without a vector database: the agent reads one index file, picks the relevant notes by their descriptions, and opens only those. Keep the whole KB readable and the index lean — the index is what gets loaded into context, so it must be high-signal.

Apply the rules below when creating a new KB, adding/editing notes, or doing a maintenance pass. When the user's request is ambiguous (new KB vs. add note vs. cleanup), ask which one before acting.

Core principles (the "why")

  1. Index-first navigation. The index is a map, not a container. An agent reads INDEX.md, selects notes by their one-line descriptions, then opens only those files. No note is "in" the KB unless it's registered in the index.
  2. Atomicity. One topic per note. If a title needs an "and", it's probably two notes. Atomic notes are easier to find, link, and reuse.
  3. Stable IDs. Every note has a permanent ID that never changes and is never reused, so links survive renames and moves.
  4. Linked with reason. When two notes connect, state why (prerequisite, supports, contrasts, see-also). Connections carry as much value as the notes.
  5. Lean instructions. Keep INDEX.md instructions short and high-signal — it competes for the agent's context budget. Bodies load on demand.
  6. Structured for retrieval. Clear H1 title and H2/H3 sections so an agent can grab the relevant slice of a note, not the whole thing.

Folder structure (nested by topic)

knowledge-base/
  INDEX.md                 # entry point: instructions + full note registry
  topics/
    <topic>/
      _topic.md            # topic map: what this topic covers + its notes
      <slug>.md            # one atomic note
  assets/                  # images / attachments referenced by notes
  • Topic folders are short, lowercase nouns (auth, billing, deploys).
  • _topic.md is the topic-level map of content (MOC): a short intro plus links to every note in that topic. It is a convenience view; INDEX.md remains the source of truth.

Read the full file on GitHub · 199 lines

Changes

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.

  1. 1mo ago First seen · 199 lines · 112 tokens per session scan A 34ca1c1fd9e6

Subscribe to this mod's changes

knowledge-base is a skill published in the GitHub repository wonderwhy-er/DesktopCommanderMCP (10,103 stars, last pushed today), licensed MIT. It adds 112 tokens to every session and 2,165 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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