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Multi-pass retrieval across the Distillery knowledge base that synthesizes findings into a structured narrative with inline citations, contradiction flags, and knowledge gap analysis.
How this skill is triggered — by the user, by Claude, or both
Slash command
/distillery:pourThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
<!-- Trigger phrases: pour, synthesize, what's the full picture on, deep dive into, /pour <topic> -->
Pour performs multi-pass retrieval across the Distillery knowledge base and synthesizes findings into a structured narrative with inline citations, contradiction flags, and knowledge gap analysis.
Recent changes: New --graph flag (off by default) opts into graph-expanded retrieval — after each semantic search, BFS pulls in 1-hop structural neighbours via entry_relations, and synthesis distinguishes search-matched entries from structurally-related ones.
/pour <topic or question> optionally with --project <name> and/or --graphSee CONVENTIONS.md — skip if already confirmed this conversation.
If no arguments provided, ask:
What topic would you like to synthesize? (e.g., "authentication architecture", "decisions made about caching")
Extract from arguments:
distillery_search call below is invoked with expand_graph=true, expand_hops=1 so the seed semantic-search results are augmented with 1-hop structural neighbours from entry_relations. Score discount is 0.5 per hop. Each result carries provenance: "search" | "graph", depth, and parent_id. The envelope adds graph_expansion: {seed_count, expanded_count}. Never expand without explicit user opt-in via this flag.When --graph is set, every distillery_search call in this step takes the additional arguments expand_graph=true, expand_hops=1. When --graph is absent, all calls run unchanged (no graph arguments).
Pass 1a -- Curated Content Search:
Search for high-value curated entries first to ensure they aren't drowned out by feed volume:
distillery_search(query="<topic>", limit=10, entry_type=["session", "bookmark", "minutes", "reference", "idea", "digest"], project="<project if specified>")
With --graph: distillery_search(query="<topic>", limit=10, entry_type=[...], project="<project if specified>", expand_graph=true, expand_hops=1)
Pass 1b -- Broad Search:
distillery_search(query="<topic>", limit=20, project="<project if specified>")
With --graph: distillery_search(query="<topic>", limit=20, project="<project if specified>", expand_graph=true, expand_hops=1)
Deduplicate Pass 1a and 1b results by entry ID, keeping the higher similarity score. Record all entries and scores. When --graph is set, also record each entry's provenance (search or graph), depth, and parent_id; if the same entry appears via both provenances across passes, prefer provenance="search" (the stronger signal).
Pass 2 -- Follow-up Searches (up to 3) + Tag Expansion (up to 3):
Analyze Pass 1 for related concepts, people, sub-topics, or terms not directly covered by the original query. For each significant one:
distillery_search(query="<related concept>", limit=10, project="<project if specified>") (add expand_graph=true, expand_hops=1 when --graph is set)
Tag-based expansion: Extract tags from Pass 1 results and identify their namespace prefixes (e.g., tags like domain/authentication, domain/oauth → namespace prefix domain). Call distillery_list(group_by="tags", project="<project if specified>", limit=200) to get tag frequencies across the knowledge base. From the returned tag groups, filter to those matching the namespace prefixes and rank by count. Convert the top-ranked tag segments to search queries by taking the leaf segment and replacing hyphens with spaces (e.g., domain/oauth → "oauth", domain/session-management → "session management"). Run up to 3 distillery_search calls from these ranked tag-derived queries, skipping any that duplicate an existing Pass 2 concept query. (Add expand_graph=true, expand_hops=1 when --graph is set.)
Report: Tag expansion: discovered <N> related topics from tag vocabulary. (Omit this line entirely if no tags are found in Pass 1 results — Pass 2 proceeds with concept-based queries only.)
Pass 3 -- Gap-filling (up to 2):
If earlier passes reveal references to specific projects, decisions, or events not yet returned:
distillery_search(query="<targeted gap query>", limit=10, project="<project if specified>") (add expand_graph=true, expand_hops=1 when --graph is set)
Deduplication: By entry ID across all passes. Each entry counted once; track which pass discovered it. When --graph is set, also track per entry the final provenance (search vs graph) for use in Step 5 synthesis.
Report: Retrieved X unique entries across Y search passes. When --graph is set, append (graph expansion: <seed_count> seed → <expanded_count> after 1-hop neighbours) using totals summed from the graph_expansion envelope across all calls.
Fewer than 2 entries: Fall back to recall-style display showing full provenance (ID, type badge, author, date, similarity, content) per entry, then:
Only <N> entry found for this topic. Showing results directly instead of synthesizing.
Tip: Use /distill to capture more knowledge about this topic.
Stop here -- do not synthesize with fewer than 2 entries.
Single author: Append a note that all entries are from one author and the synthesis may reflect a single perspective.
Produce these sections, omitting any with no relevant content:
a. Summary (always include) -- 2-3 paragraph narrative weaving findings together with inline [Entry <short-id>] citations (short-id = first 8 chars of UUID).
Example: The team adopted DuckDB after evaluating SQLite and PostgreSQL [Entry 550e8400]. This was driven by the need for embedded analytical queries [Entry 7c9e6679].
Provenance distinction (when --graph is set): Entries with provenance="search" matched the query semantically; entries with provenance="graph" did not match the query directly but were pulled in as 1-hop structural neighbours of a search-matched entry. In the prose, label graph-only entries as "structurally related" (e.g., [Entry abc12345, structurally related]) and call out why they appear — e.g., "linked to [Entry 550e8400] via entry_relations". Treat search-matched entries as primary signal; graph-only entries provide supporting context (downstream impacts, prior decisions a search-matched entry references, etc.). The synthesis should make this distinction visible to the user so they understand why each entry appears.
b. Timeline (omit if all entries same day) -- Chronological evolution with dates, descriptions, and citations.
c. Key Decisions (omit if none) -- Bullet list: what was decided, by whom, when, rationale, citation.
d. Contradictions (omit if none) -- Conflicting information between entries, presenting both sides with dates. Note which may supersede.
e. Knowledge Gaps (omit if none) -- Thin areas and suggestions for what to capture next via /distill.
Table of all cited entries:
| # | Short ID | Type | Author | Date | Preview | Similarity |
|---|---|---|---|---|---|---|
| 1 | 550e8400 | [session] | Alice Smith | 2026-03-15 | We decided to use DuckDB for... | 92% |
When --graph is set, add a Provenance column showing search or graph (and for graph entries, append the parent_id short-id, e.g., graph (via 550e8400)), so the audit trail makes the retrieval path explicit.
Ask: Would you like to go deeper on any sub-topic, or is this sufficient?
If the user identifies a sub-topic: run a focused search (limit=10) — passing expand_graph=true, expand_hops=1 if --graph is set — deduplicate against cited entries, produce a ## Refinement: <Sub-topic> addendum with synthesis and an Additional Sources table (with Provenance column when --graph is set), then ask again. Maximum 5 refinement rounds.
When satisfied: Synthesis complete. X entries cited across Y sections.
Heading # Pour: <Topic>, then sections Summary, Timeline, Key Decisions, Contradictions, Knowledge Gaps, Sources as described above. Refinement addendums appended if requested. Fewer than 2 entries triggers the fallback display instead.
[Entry <short-id>] citation format (short-id = first 8 chars of UUID)--project filter to every distillery_search call when provided--graph defaults OFF — never pass expand_graph=true without explicit user opt-in via the flag; without --graph, all distillery_search calls and synthesis behavior remain unchanged (no provenance column, no "structurally related" labels, no graph_expansion totals)--graph is set, apply expand_graph=true, expand_hops=1 to every distillery_search call (Pass 1a, 1b, Pass 2 concept and tag-derived, Pass 3, and any Step 7 refinement search)--graph is set, label graph-only entries (provenance="graph") as "structurally related" in the prose and explain why they appear (parent entry, relation path)npx claudepluginhub norrietaylor/distillery --plugin distilleryCompiles deep context on a topic by combining semantic search with relationship traversal across 4 retrieval phases. Useful for research spanning multiple entries and connections.
Synthesizes multi-source search results (chat, email, docs, trackers) into coherent, deduplicated answers with citations, prioritizing authoritative recent info and confidence scoring.
Queries and manages a project knowledge graph across tasks, SOPs, memories, and concepts. Captures patterns, pitfalls, decisions, and learnings for persistent retrieval.