How do I install the Kb Search skill?

Run npx skillmds@latest add blackbelttechnology/kb-search in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Kb Search safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Kb Search?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Kb Search free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Kb Search?

BlackBeltTechnology (@blackbelttechnology) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Kb Search by blackbelttechnology — Agent Skill

Name: Kb Search
Author: BlackBeltTechnology

Kb Search

Search the local markdown knowledge base for facts, prior decisions, definitions, people, error codes, config flags, and how-we-fixed-it notes. ALWAYS search here BEFORE answering a project-specific question from memory, guessing, or asking the user. Use whenever you hit an unknown term, an unfamiliar entity, an error string, or a "how do we do X / why did we choose Y" question.

File contents

kb-search — retrieve before you answer

A fast, local, zero-token FTS5 knowledge base over this project's markdown (@blackbelt-technology/pi-dashboard-kb). Retrieval is pull: you call it; nothing is auto-injected. Sub-second, deterministic, costs no model tokens — so call it freely on any uncertainty.

When to Use

Hit an unknown name / term / error string / config flag / function.
Need a past decision, convention, or "how we did X".
About to answer a factual question about this project from memory.
About to ask the user something the docs may already answer.

Procedure

Extract the key entities from the problem (names, error strings, slugs, config keys, function names).
Run: kb search "<entities>" --limit 8 --json
Read only the top 1–2 hits' full content when needed: kb get <path> --section "<heading_path>"
Still unresolved? Walk the graph from a hit: kb neighbors "<heading_path>" --depth 2 and kb backlinks "<path>".
Paraphrase miss? Lexical search is weak when your words differ from the docs' words. Reformulate once using the domain's actual terms (synonyms, the real flag/class names) and re-search. Then escalate to the user only if the KB returns nothing relevant.
Synthesize from the retrieved sections. Cite the path you used.

Pitfalls

Do NOT answer project-specific questions from memory without searching first.
Do NOT read whole files — search returns ranked sections with snippets; open full content only for the top hits.
Empty result is not a stop sign — reformulate with domain terms once, then ask.
Filter when you only want rules: kb search "<q>" --doc-type agents.

Verification

kb search returns ranked {path, headingPath, score, snippet} (lower score = more relevant).
Freshness is automatic: kb search runs an incremental reindex first unless --no-reindex.
Requires @blackbelt-technology/pi-dashboard-kb installed (kb on PATH) and a configured source (.pi/dashboard/knowledge_base.json or --source <dir>).

---name: kb-searchdescription: Search the local markdown knowledge base for facts, prior decisions, definitions, people, error codes, config flags, and how-we-fixed-it notes. ALWAYS search here BEFORE answering a project-specific question from memory, guessing, or asking the user. Use whenever you hit an unknown term, an unfamiliar entity, an error string, or a "how do we do X / why did we choose Y" question.---# kb-search — retrieve before you answerA fast, local, zero-token FTS5 knowledge base over this project's markdown(`@blackbelt-technology/pi-dashboard-kb`). Retrieval is **pull**: you call it; nothingis auto-injected. Sub-second, deterministic, costs no model tokens — so call itfreely on any uncertainty.## When to Use- Hit an unknown name / term / error string / config flag / function.- Need a past decision, convention, or "how we did X".- About to answer a factual question about this project from memory.- About to ask the user something the docs may already answer.## Procedure1. Extract the key entities from the problem (names, error strings, slugs, config keys, function names).2. Run: `kb search "<entities>" --limit 8 --json`3. Read only the top 1–2 hits' full content when needed: `kb get <path> --section "<heading_path>"`4. Still unresolved? Walk the graph from a hit: `kb neighbors "<heading_path>" --depth 2` and `kb backlinks "<path>"`.5. Paraphrase miss? Lexical search is weak when your words differ from the docs' words. **Reformulate once** using the domain's actual terms (synonyms, the real flag/class names) and re-search. Then escalate to the user only if the KB returns nothing relevant.6. Synthesize from the retrieved sections. Cite the `path` you used.## Pitfalls- Do NOT answer project-specific questions from memory without searching first.- Do NOT read whole files — search returns ranked sections with snippets; open full content only for the top hits.- Empty result is not a stop sign — reformulate with domain terms once, then ask.- Filter when you only want rules: `kb search "<q>" --doc-type agents`.## Verification- `kb search` returns ranked `{path, headingPath, score, snippet}` (lower score = more relevant).- Freshness is automatic: `kb search` runs an incremental reindex first unless `--no-reindex`.- Requires `@blackbelt-technology/pi-dashboard-kb` installed (`kb` on PATH) and a configured source (`.pi/dashboard/knowledge_base.json` or `--source <dir>`).

Frequently asked questions