pkm-explore

name	pkm-explore
description	Use when researching what the vault knows about a topic — performs graph + semantic exploration with gap analysis to map existing knowledge and find missing connections. Primarily used via the vault-explorer agent. Not for routine session-start context loading (hooks handle that).

PKM Explore — Knowledge Discovery and Gap Analysis

Understand what the vault already knows about a topic before creating new notes or doing research. Combines graph traversal with semantic search to find both explicit connections and hidden relationships.

When to use: Researching vault knowledge on a topic, finding related notes, exploring a topic area before writing.

Step 1: Identify Seed Note

Find the most relevant existing note for the topic:

vault_semantic_search({ query: "<topic>", limit: 5 })

A note on score interpretation. vault_semantic_search uses text-embedding-3-large (3072-dim) cosine similarity, which compresses hard. In a typical vault, even genuinely on-topic notes score around 0.45–0.65, and tangential notes that share surface tokens (e.g., "vibration" matching a "vibe coding" note) often score 0.40–0.45 too. Absolute scores cannot reliably separate "topically relevant seed" from "tangential surface match" — you must read and judge.

Routing:

• If a specific note path was provided, use that directly as the seed.
• Otherwise: read the top result with vault_read and judge whether it is topically relevant to the query (not just sharing surface tokens). If yes, use it as the seed and advance to Step 2.
• If the top result is tangential or off-topic, corroborate with one vault_search keyword query using a distinctive term from the topic. If vault_search returns zero matches AND the semantic top hits are all tangential on read, the vault has minimal coverage — report that and suggest creating a seed note using pkm-write. Do not advance into Steps 2–5 and do not synthesize from tangential matches.

If vault_semantic_search is unavailable (no OPENAI_API_KEY), use vault_search with key terms and vault_query by tags/type to locate a seed note.

Step 2: Graph Context

Explore the structural neighborhood around the seed note:

vault_neighborhood({ path: "<seed-note-path>", depth: 2, direction: "both" })

This reveals the intentional knowledge structure — what someone explicitly linked. If the seed note has no links (new or isolated note), this is expected — rely on Step 3 results for connection discovery. If vault_neighborhood returns an error (e.g., 'File not found'), the seed note may have been deleted or the path misresolved — try the next-best result from Step 1, or verify with vault_search.

Step 3: Semantic Expansion

Search for conceptually related notes that may not be in the graph:

vault_semantic_search({ query: "<topic>", limit: 10 })

If vault_semantic_search is unavailable (no OPENAI_API_KEY), use vault_search with multiple keyword variations and vault_query with relevant tags.

Step 4: Gap Analysis

Compare the graph results (step 2) with semantic results (step 3):

Category	Meaning	Action
In graph AND semantic	Well-connected, established knowledge	No action needed
In graph but NOT semantic	Structurally linked but topically distant	May indicate weak/outdated link
In semantic but NOT graph	Missing links — conceptually related but unconnected	Highest-value findings — suggest linking

Note: If Step 3 used vault_search/vault_query (no OPENAI_API_KEY), the gap analysis is less precise — text search misses conceptually related notes that use different terminology. Flag this reduced fidelity when presenting findings.

Step 5: Synthesize

Return findings organized as:

1. What exists: Key notes on this topic with brief summaries (type, status, tags)
2. How it's connected: Graph structure — which notes link to which, at what depth
3. Missing links: Conceptually related notes not yet in the graph (candidates for linking — note reduced precision if semantic search was unavailable)
4. Knowledge gaps: Topics referenced but not covered by any note — candidates for new notes via pkm-write