wiki-retrieve
Build and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta, remote egress requires explicit consent, and unavailable reranking falls back deterministically.
npx skills add AgriciDaniel/claude-obsidian --skill wiki-retrieve --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Retrieve relevant passages This extension derives search data from `wiki/` into `.vault-meta/`. It never changes canonical notes. Always pass the selected vault explicitly. Resolve the installed product root from this skill's own location, not from the vault or current working directory: ```bash PRODUCT_ROOT=/absolute/path/to/installed/claude-obsidian PREFIX="$PRODUCT_ROOT/scripts/contextual-prefix.py" BM25="$PRODUCT_ROOT/scripts/bm25-index.py" RETRIEVE="$PRODUCT_ROOT/scripts/retrieve.py" RERANK="$PRODUCT_ROOT/scripts/rerank.py" test -f "$PREFIX" && test -f "$BM25" && test -f "$RETRIEVE" && test -f "$RERANK" ``` ## Pipeline 1. `contextual-prefix.py` splits pages on paragraph boundaries and stores the raw chunk plus a short page-level prefix. 2. `bm25-index.py` builds a local, standard-library BM25 index over the contextualized text. 3. `retrieve.py` selects BM25 candidates, optionally reranks them, rejects invalid records, deduplicates by page, and returns paths and snippets. 4. The caller reads the returned pages and performs synthesis; retrieval output is not itself evidence. ## Provision locally Preview first, then build synthetic prefixes without network egress: ```bash pytho
- Pipeline
- Provision locally
- Contextual-prefix privacy
- Query
- Integrity rules
- Checkpoint
test -f "$PREFIX" && test -f "$BM25" && test -f "$RETRIEVE" && test -f "$RERANK" python3 "$PREFIX" --vault "$VAULT" --all --no-llm --peek python3 "$PREFIX" --vault "$VAULT" --all --no-llm python3 "$BM25" --vault "$VAULT" build python3 "$RETRIEVE" --vault "$VAULT" "wiki" --top 1 --no-rerank --explain python3 "$RETRIEVE" --vault "$VAULT" "$QUERY" --top 5 --no-rerank --explain python3 "$BM25" --vault "$VAULT" stats python3 "$BM25" --vault "$VAULT" query "$QUERY" --top 10 python3 "$RERANK" --vault "$VAULT" "$QUERY" --peek python3 "$RERANK" --vault "$VAULT" "$QUERY" --model nomic-embed-text --peek
What does the wiki-retrieve skill do?
Build and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta, remote egress requires explicit consent, and unavailable reranking falls back deterministically.
How do I install it?
Run `npx skills add AgriciDaniel/claude-obsidian --skill wiki-retrieve --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From AgriciDaniel/claude-obsidian, a repository with 10,371 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.