kb-search

name	kb-search
description	Search the empirical Knowledge Base for the best-fit architecture and technology learnings given a product's conditions. Use during context-building in the realize lenses (/arch, /run, /ux, /agentic) when you need to know what worked before for a product in this situation — its stage, scale, persistence, and monetization. Reads the architecture/ and technology/ shelves (the condition-matched, "what works for us" engine), distinct from search-kb which routes work to a domain. Returns the learnings that fit, with recommendation, rationale, and provenance.
version	0.1.0
user-invocable	false

kb-search

The KB is a bank of empirical learnings — for a product in a given situation, what capabilities and what architecture worked best, and when to evolve. This skill finds the learning that fits. See core/components/memory/knowledge/_DESIGN.md for the full design.

V1 note: the script only serves structure. You do the matching — read the Conditions and judge fit. The script does not rank for you; that is deliberate (the V1 engine; a server will rank later).

Inputs

A condition profile for the product you are building context for, read from its ProductOS — as many of these as you know:

• stage: prototype | internal | public | monetized
• users: one | small-team | public
• persistence: none | some | full
• monetization: none | lead-capture | paywall
• plus any free-text intent ("quick demo for one teammate")

How to use

The script lives at scripts/kb_search.py (Python 3, no dependencies). Pass --kb-root if the KB is not at the default deploy path.

1. Get the map. Run index — it returns every learning's id, title, conditions facets, evolve_when links, and provenance as JSON.

   python3 scripts/kb_search.py index
   

2. Match by reasoning. Read the conditions of each entry and pick the one(s) whose situation matches the product's profile. This is judgment, not keyword matching — "quick demo for one teammate" matches a stage: prototype, users: one learning even with no shared words. Prefer higher-provenance learnings (proven across more products) when several fit.

3. Fetch the learning. Run get <id> for the chosen learning to read its full Recommendation and Rationale.

   python3 scripts/kb_search.py get architecture/single-user-throwaway
   

4. Follow evolution. Use the learning's evolve_when to see what to climb to as the product's conditions change.

grep <terms> exists as a coarse pre-filter to narrow candidates when the manifest is large — it is NOT the matcher; step 2 always is.

Output

Return the selected learning(s) with: the recommendation (capabilities + architecture), the rationale, the provenance, and the evolve-when pointers — so the calling play (e.g. /realize) can apply them.

Forward compatibility

When the KB moves to a server, this skill's script swaps its internals from the local files to a kb CLI; the inputs, the steps, and the output shape do not change.