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.
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/wonderwhy-er/DesktopCommanderMCPnpx agentmods add skills/wonderwhy-er/desktopcommandermcp/knowledge-baseWrote 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/wonderwhy-er/desktopcommandermcp/knowledge-base)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00112 | $0.02165 |
| Opus 5.5 | $0.00045 | $0.00866 |
| Sonnet 5.5 | $0.00022 | $0.00433 |
| Haiku 4.5 | $0.00011 | $0.00216 |
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- knowledge-base — 100% identical, 0 lines differ
- knowledge-base — 100% identical, 0 lines differ
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")
- 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. - 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.
- Stable IDs. Every note has a permanent ID that never changes and is never reused, so links survive renames and moves.
- Linked with reason. When two notes connect, state why (prerequisite, supports, contrasts, see-also). Connections carry as much value as the notes.
- Lean instructions. Keep
INDEX.mdinstructions short and high-signal — it competes for the agent's context budget. Bodies load on demand. - 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.mdis the topic-level map of content (MOC): a short intro plus links to every note in that topic. It is a convenience view;INDEX.mdremains the source of truth.
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 · 199 lines · 112 tokens per session scan A 34ca1c1fd9e6
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.
Other skills, from other repositories
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.
aatmf-t04-memory-manipulation
AATMF T4 — Multi-Turn & Memory Manipulation. Persistent memory injection, conversation-state poisoning, cross-session contamination, ghost-context leak.
kb-research
Structured approach to finding and synthesizing information from the user's knowledge base.
peer-review
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating…