Search

Search the AI Wiki

Given keywords (or a question), find every relevant page and source and return a ranked, linked result set. The discipline this skill enforces: find candidates cheaply by querying the Obsidian Bases and the search/tag indexes, then read only the top matches — rather than pulling index.md (21 KB) or many pages into context. That keeps answers fast and the context window lean.

Setup

Obsidian commands go through the bundled wrapper. From the skill directory:

chmod +x scripts/obs.sh   # first run only
scripts/obs.sh base:query path="wiki/Glossary.base" view="A–Z" format=md | head -3   # smoke test

If a CLI call comes back empty, don't assume the wrapper is broken. The wrapper is fine — it only redirects the noisy startup banner (stderr) and prints real results on stdout. Empty output almost always means either (a) search flakiness (it intermittently returns nothing on the first call — just run the same command again), or (b) Obsidian isn't running yet. base:query and tag are reliable; use them to confirm the CLI works before falling back to anything else. Do not bypass obs.sh by calling the raw binary — the wrapper exists so you target the right vault (a global OBSIDIAN_VAULT env var points at a different vault on this machine, so the raw binary would query the wrong one).

Full command set and base view names are in references/obsidian-cli.md.

Strategy: three lookup channels, then read the winners

Run the channels that fit the query (cheap, often in parallel), union the hits, rank, then read only the top few pages in full.

1. Term lookup — Glossary.base

The Glossary is the catalog of entities + concepts + topics. If a keyword maps to a cluster, pull that view; otherwise pull A–Z and scan. format=md is compact and readable.

scripts/obs.sh base:query path="wiki/Glossary.base" view="Vectorization & RAG" format=md
scripts/obs.sh base:query path="wiki/Glossary.base" view="A–Z" format=json

Cluster view names are listed in references/obsidian-cli.md (e.g. LLM Core, Context Engineering & Agents, Training & Fine-tuning, Tools & Frameworks, People…).

2. Tag lookup — exact topic membership

When a keyword is (or resembles) a tag, this is the highest-precision channel:

scripts/obs.sh tags counts sort=count format=json   # find the closest existing tag
scripts/obs.sh tag name="rag" verbose               # all pages carrying #rag

3. Full-text — search across all pages and sources

scripts/obs.sh search query="reward model" format=json   # -> array of file paths

For multiple keywords, run one search per keyword and rank files by how many keywords hit them. To get the full catalog of pages (with titles/types/#sources) for ranking context, query the Index base:

scripts/obs.sh base:query path="wiki/Index.base" view="All Pages" format=json

4. Read only the top matches

scripts/obs.sh read path="wiki/concepts/rag.md" on the handful of pages that clearly matter. Don't read everything you found — read what you'll actually cite.

Ranking

Order results by usefulness, not just match count:

1. Direct term pages — an entity/concept/topic whose title is the keyword.
2. Topic overviews — good entry points that link onward.
3. Source pages — primary material behind the claims.
4. Analyses — existing syntheses/comparisons that may already answer the question.

A page hit by several channels (Glossary + tag + full-text) ranks above a single-channel hit. Prefer recently-updated pages and those with more sources when otherwise tied (both visible in the base output).

Output format

Group by category, each entry a wikilink + one-line description + path:

**Concepts & Entities**
- [[rag]] — Retrieval-Augmented Generation: core pattern + tradeoffs (wiki/concepts/rag.md)
- [[pgvector]] — Postgres extension for vector search (wiki/entities/pgvector.md)

**Topics**
- [[embeddings]] — overview tying vectorization, RAG, and vector DBs together

**Sources**
- [[pinecone-concepts]] — Pinecone's managed vector DB model

**Analyses**
- [[what-is-rag-and-related-retrieval-architectures]] — existing synthesis

If the query was a question (not just keywords), synthesize a short answer first, citing pages with [[wikilinks]], then list the results that back it. If nothing relevant exists, say so plainly and suggest the closest pages or that the topic may need ingesting.

Offer to save (Query workflow, step 4)

If you produced a substantial, reusable synthesis, offer to save it as wiki/analyses/<slug>.md. Only if the user accepts: write the page (analysis template in the ingest skill's references/page-templates.md), add a one-line entry to wiki/index.md, and append a query entry to wiki/log.md. A plain lookup that just lists pages does not need to be saved.