Wiki retrieve

Skill AgriciDaniel/claude-obsidian/skills/wiki-retrieve

Self-organizing AI second brain for Obsidian + Claude Code. Drop any source and Claude reads, links, and files it into one connected knowledge graph of plain Markdown you own. AI note-taking, personal knowledge management (PKM), and an open-source Notion alternative. Based on Karpathy's LLM Wiki pattern.

Install

npx -y skills add AgriciDaniel/claude-obsidian --skill wiki-retrieve

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

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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:

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

contextual-prefix.py splits pages on paragraph boundaries and stores the raw chunk plus a short page-level prefix.
bm25-index.py builds a local, standard-library BM25 index over the contextualized text.
retrieve.py selects BM25 candidates, optionally reranks them, rejects invalid records, deduplicates by page, and returns paths and snippets.
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:

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

Chunk and index files are disposable runtime state. Incremental prefixing skips records whose chunk and page hashes still match. A complete scan removes surplus records for deleted pages, and the prefixer invalidates the BM25 index before changing its chunk set so a mixed stale index is not served. Prefix and BM25 build operations share the vault-wide mutation lock with every other writer; a busy vault fails closed instead of publishing a partial index.

Contextual-prefix privacy

Synthetic prefixes use only local frontmatter and page text. The Anthropic API and claude subprocess tiers can send page bodies off-machine and therefore require the user's explicit consent plus --allow-egress. Never infer consent from an API key or installed binary. Preview the scope first and state which provider will receive what data.

Remote Ollama endpoints also require explicit approval and --allow-remote-ollama; the default reranker accepts localhost only.

Query

For a strictly read-only lookup, use the prebuilt BM25 index:

python3 "$RETRIEVE" --vault "$VAULT" "$QUERY" --top 5 --no-rerank --explain

For an explicitly requested rerank, omit --no-rerank. The default is Ollama's multilingual nomic-embed-text-v2-moe model (approximately 958 MB); the product never pulls it automatically. To use an already-installed, smaller, English-oriented v1.5 model, pass --model nomic-embed-text explicitly. Nomic models use search_query: for the query and search_document: for candidate text. Nomic v2 has a 512-token input context and Ollama truncates longer embedding inputs by default; BM25 still scores the complete chunk. Embeddings are cached by exact model, input scheme, and hash of the exact prefixed input. A missing local Ollama service, missing selected model, unusable vector, or any candidate embedding failure falls back for the complete result set to the original BM25 order; it never mixes cosine and BM25 score scales.

Query input is bounded at 8,000 normalized characters and result counts must be between 1 and 1,000. Oversized queries and invalid limits fail with an actionable usage error instead of looking like an empty successful search. An untagged model request matches only the installed untagged name or its :latest alias; select any other tag explicitly.

Use direct diagnostics when needed:

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

Integrity rules

Accept only relative chunk and page paths whose resolved targets remain under $VAULT/.vault-meta/chunks/ and $VAULT/wiki/ respectively.
Reject hashless legacy chunk records and require chunk-body, page, and index hashes to match before a cached record can be built or served.
Reject absolute paths, symlink escapes, missing pages, mismatched chunk IDs, changed page hashes, and stale index/chunk hash pairs.
Rerank the full candidate set, then deduplicate by page, then apply --top.
An empty index is an honest no-result state. A missing or corrupt index makes retrieve.py exit 10 with a stable rebuild command; callers fall back to the standard vault query/text-search path and do not fabricate matches.
Do not cite benchmark percentages unless a reproducible vault-specific benchmark produced them.

Checkpoint

Observe cache readiness and privacy boundaries, think about whether lexical or semantic ranking is needed, verify returned paths and source freshness, and grow by measuring retrieval misses against a maintained local query set.