Kb Setup logo

Kb Setup

Organization
BlackBeltTechnology
kb-setup

Set up, initialize, or configure the local markdown knowledge base for a project. Use when the user says "set up the knowledge base", "init the KB", "configure the markdown KB", "index my docs", "add a doc source to the KB", or wants the project's markdown searchable by `kb search`. Wraps `kb init` end-to-end through `kb index` and a smoke `kb search`.

Overview

PublisherBlackBeltTechnology
Repositorypi-agent-dashboard
Skill namekb-setup
Stars
330
Forks
51
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BlackBeltTechnology on GitHub. Read the source before you install it.

Installation

Install the Kb Setup AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BlackBeltTechnology/pi-agent-dashboard/tree/develop/packages/kb/skill/kb-setup
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BlackBeltTechnology/pi-agent-dashboard.git /tmp/pi-agent-dashboard
mkdir -p .claude/skills
cp -r /tmp/pi-agent-dashboard/packages/kb/skill/kb-setup .claude/skills/kb-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kb Setup in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kb Setup on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kb Setup is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

kb-setup — bring up the project knowledge base

@blackbelt-technology/pi-dashboard-kb builds a zero-token, local SQLite/FTS5 index over this project's markdown so kb-search can retrieve before answering. This SKILL does the one-time bring-up. Retrieval is pull (the agent calls kb); nothing is auto-injected.

When to Use

  • User asks to set up / init / configure the knowledge base.
  • No .pi/dashboard/knowledge_base.json exists yet and the project has docs.
  • User wants to add a doc source (local dir, npm package docs, git handbook).

Procedure

  1. Detect existing config. Check .pi/dashboard/knowledge_base.json (project) and ~/.pi/dashboard/knowledge_base.json (global). Run kb config to see the resolved layering. If a project config already exists and is healthy, skip to step 5 (smoke search) unless the user wants changes.
  2. Choose scope + sources. Ask (if unclear) whether to write project or global config. Pick sources[]:
    • local doc dir: { "kind": "filesystem", "ref": "docs", "priority": 10 } (higher priority = preferred when the same content appears in two roots)
    • AGENTS.md / CLAUDE.md: covered by indexAgentsFiles: true (default on)
    • source-tree *.md: covered by includeSourceMarkdown: true (default on)
    • remote sources (npm/git/https) require trust-on-first-use — defer unless the user explicitly asks; remote resolvers land in a later phase.
  3. Scaffold. kb init [--global] [--source <ref>]... [--dry-run]. Review the --dry-run output with the user, then run for real. kb init refuses to clobber an existing config without --force, and gitignores the dbPath.
  4. Index. kb index → reports files scanned / changed / chunks. First run is the cold index; later runs are incremental (mtime → sha256).
  5. Smoke search. kb search "<a real term from the docs>" --limit 5 and confirm ranked {path, headingPath, score, snippet} come back. If empty, check kb config shows the right sources and that the dir has .md files.
  6. Point the user at the kb-search SKILL for everyday retrieval.

Pitfalls

  • Do NOT overwrite an existing config without confirming — use --force only after the user agrees.
  • dbPath must be gitignored; kb init does this for project configs. For a custom dbPath outside the project, add the gitignore entry yourself.
  • No sources[] → kb index exits 2. Always seed at least one filesystem source.

Verification

  • kb config prints origin, dbAbsPath, and the resolved sources list.
  • kb index reports N files … M chunks and store.counts() JSON.
  • kb search "<term>" returns ranked hits with snippets.

Frequently asked questions

What does the Kb Setup AI skill do?

Set up, initialize, or configure the local markdown knowledge base for a project. Use when the user says "set up the knowledge base", "init the KB", "configure the markdown KB", "index my docs", "add a doc source to the KB", or wants the project's markdown searchable by `kb search`. Wraps `kb init` end-to-end through `kb index` and a smoke `kb search`.

Why use Kb Setup on TypingMind?

Because you install it once and use it with any model. Kb Setup is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kb Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BlackBeltTechnology/pi-agent-dashboard/tree/develop/packages/kb/skill/kb-setup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kb Setup?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kb Setup?

As many as you like. As long as a model supports skills, you can use Kb Setup with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kb Setup AI skill free?

Yes. It is published on GitHub by BlackBeltTechnology under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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