a skill by aka-kika, brought here by SD
using the skill librarian
paste this link into your ai. it will know what to do.
https://innernet.live/skills/aka-kika-using-the-skill-librarianUse when starting any non-trivial task, when wondering "is there a skill for this", or after finishing work that used a librarian recommendation. Applies in every agent connected to the skill-librarian MCP server.
Using the Skill Librarian
Overview
The skill-librarian MCP server searches your full skill collection and recommends the best fits for a stated intent. The librarian is the access path: only a small curated set of skills stays installed per agent; everything else is one librarian_find call away. Never copy skill files into agent config directories — read recommendations in place.
This is the one skill worth installing everywhere. It replaces the rest.
Quick Reference
| Tool | When to call |
|---|---|
librarian_find(intent, k) | Before any non-trivial task. Plain-language intent ("package a python mcp server for distribution"), not keywords. |
librarian_brainstorm | Open-ended ideation — "what could I build/do here" — instead of find. |
librarian_report(skill, worked, note) | Always after acting on a recommendation — used or rejected, one line why. Success rates drive curation; this is not optional bookkeeping. |
librarian_reindex | After adding or editing skills in the collection. |
librarian_stats | Collection health and usage stats. |
Workflow
1. librarian_find with your intent. Do this even if locally-installed skills look sufficient — the librarian searches the whole collection; your installed list is a tiny fraction of it. 2. Weigh each recommendation's fit, why, and why_not. Rejecting all of them is a valid outcome. 3. Load the chosen skill from the collection — recommendations return names, not paths, and skills may be nested inside bundles: find <your-skills-dir> -maxdepth 4 -type d -name "<skill-name>" → read its SKILL.md. 4. Follow the skill. 5. librarian_report with worked=true/false and a one-line note (why it worked, why it failed, or why you rejected it).
If the MCP isn't connected
The server is a local stdio Python process. Command and env (translate to your agent's config format):
{
"command": "/path/to/the_librarian/.venv/bin/python",
"args": ["/path/to/the_librarian/server.py"],
"env": {
"LIBRARIAN_SKILLS_DIR": "/path/to/your/skills-collection",
"OLLAMA_HOST": "http://localhost:11434",
"LIBRARIAN_EMBED_MODEL": "nomic-embed-text",
"LIBRARIAN_RERANK_BIN": "/path/to/the_librarian/bin/afm-rerank"
}
}LIBRARIAN_RERANK_BIN is optional (macOS + Apple Intelligence only); omit it to use pure embedding order. See the repo README for full setup.
Common Mistakes
- Skipping find because an installed skill looks close enough — the collection
version may be better; check first.
- Keyword-style intents ("mcp python") — write what you're trying to accomplish;
the embedding search works on intent.
- Forgetting
librarian_report— unreported uses starve the curation loop. - Copying a recommended skill into an agent's skills dir — read it in place; if it
earns permanent installation, the human decides.
keep it where your ai can reach it.
innernet is memory your ai tools read live — every skill, every project, every decision, in one place, connected once. save this skill to yours, or publish one of your own as a link like this.