pmm:query

pmm:query

Explicit memory recall. Context-first — answers from already-loaded files before any agent dispatch. Supports free-text questions, attribution filters, date ranges, file scoping, deep traversal, and prose or dump output.

Query: $ARGUMENTS

If $ARGUMENTS is empty, ask the user what to search for before proceeding.

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Routing Table

Map question type to target file(s):

Question type	Target file(s)
Decisions / decided / ratified	decisions.md
Preferences / style / how user works	preferences.md
Tone / voice / reasoning / lens	voices.md
Recent work / latest / just shipped	last.md, progress.md
Relationships / how things connect	graph.md, vectors.md
History / arc / timeline	timeline.md
Rules / directives / standing instructions	standinginstructions.md
People / tools / systems	assets.md
Facts / long-term / background	memory.md
Processes / workflows	processes.md
Mistakes / lessons / errors	lessons.md
Categories / naming / taxonomy	taxonomies.md
Ambiguous / broad	all active files

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Execution

Step 1 — Parse Query

Extract from $ARGUMENTS:

• Keyword / question — everything that is not a filter or modifier
• Attribution filter — by namespace:name (e.g. by user:raffi, by agent:leith)
• Date filter — since YYYY-MM-DD or before YYYY-MM-DD
• File scope — in <filename> (e.g. in decisions, in lessons) — search only that file
• Deep flag — presence of the word deep → set deep=true, remove from keyword
• Dump flag — presence of the word dump → set dump=true, remove from keyword

Step 2 — Context-First Routing

Read memory/config.md for Session Start mode.

If Mode: lazy — memory files are already in context (injected by the SessionStart hook). Execute Steps 3–8 directly in the main context window without dispatching any agent. Tier 1 files are in-context. For Tier 2 files (graph.md, vectors.md, taxonomies.md, assets.md), use the Read tool to load the relevant file before searching — do not load all four, only what the routing table requires.

If Mode: eager — fall through to Agent Dispatch at the end of this document.

Step 3 — Search

For each target file (respecting routing table and any in <file> scope):

1. Check or Read the file (Tier 1 = already in context; Tier 2 = Read on demand)
2. Match entries containing the keyword (case-insensitive)
3. Apply attribution filter: only include entries where the line or nearby heading contains [namespace:name] matching the filter
4. Apply date filter: only include entries whose date prefix (YYYY-MM-DD) satisfies ≥ since or ≤ before
5. Collect all matches with their source file

Read memory/config.md to confirm which files are active. Skip deactivated files.

Step 4 — Deep Traversal

Skip if deep=false.

Expand the result set using similarity, graph, and taxonomy data. Run regardless of whether Step 3 found results.

4a — Vector cluster expansion (vectors.md):

1. Read (or use in-context) vectors.md
2. Find clusters whose name or member list contains the keyword
3. Collect all member concepts from matched clusters
4. Find similarity lines ([[A]] ↔ [[B]] | score: ...) where A or B matches — collect the paired concept if score ≥ 0.6
5. Search all active files for each expanded concept. Tag new matches [via vectors]

4b — Graph edge traversal (graph.md):

1. Read (or use in-context) graph.md
2. Find edges where the keyword appears in either node ([[keyword]])
3. Collect neighbour nodes (one hop only)
4. Search all active files for each neighbour. Tag new matches [via graph]

4c — Taxonomy broadening (taxonomies.md):

1. Read (or use in-context) taxonomies.md
2. Find categories or classifications containing the keyword
3. Collect sibling terms in those categories
4. Search all active files for each sibling. Tag new matches [via taxonomy]

Deduplicate — results already found in Step 3 should not be listed again.

Step 5 — Full File Escalation (T3)

If Steps 3 and 4 returned no results:

1. Read memory/config.md for the Active Files list
2. For each active file loaded with tail:N, header, or skip at session start: re-read the file in full using the Read tool
3. Search the full content for the query keyword
4. If matches found, collect them and proceed to Step 7 (format output)
5. If still no matches, proceed to Step 6 (git fallback)

Step 6 — Beyond-Window Gate

Only if Steps 3, 4, and 5 together returned no results.

Check memory/config.md for ## Recall Beyond Window → Mode:

If Mode: prompt — present AskUserQuestion with three options:

• Yes, search git history — dispatch a minimal git-history agent (no file reads; memory is in context):

  Run: git log --all --grep="<keyword>" --oneline
  For each matching commit: git show <hash> -- memory/
  Return the relevant lines and which commit they came from.
  

  Use the Readonly Agent Model from config (default: haiku). Incorporate results into Step 8 output tagged (from git history, commit <hash>).

• Yes, and don't ask me again — same dispatch, then update memory/config.md: replace - Mode: prompt under ## Recall Beyond Window with - Mode: auto

• No — return: No record found in the current memory window.

If Mode: auto — silently dispatch the minimal git-history agent (no file reads; same prompt as above).

Step 7 — Cross-Reference Enrichment

Skip if deep=true (graph.md was already traversed in Step 4b).

Otherwise: if results mention a named entity (person, tool, system, concept) that also appears in graph.md or assets.md:

• Read those files if not already in context
• Append a Related context: note (1–2 lines) if it genuinely enriches the result
• Do not bloat — only include if it adds something the main results don't already say

Step 8 — Format Output

Branch A — Prose mode (default, dump=false):

Synthesize a narrative answer from all collected results.

Rules:

• Write in concise, direct prose — answer the question, don't describe what files say
• Weave evidence from multiple source files into a coherent response
• Cite sources inline as parentheticals: (decisions.md), (timeline.md, 2026-03-17)
• Preserve attribution tags inline where relevant: [user:raffi]
• For deep-mode results, note provenance naturally: "A related concept, X (via graph), also shows..."
• For git history results: "(from git history, commit abc1234)"
• End with a single Sources: footer line listing all files that contributed
• If no results after all fallbacks: No record found in the memory files.
• No header block, no match counts — start directly with the narrative

<synthesized narrative answering the user's question, citing sources inline>

Sources: decisions.md, timeline.md, graph.md

Branch B — Dump mode (dump=true):

PMM Query Results
=================
Query: <original query>
Filters: <attribution filter> | <date filter> | <file scope> (or "none" if no filters)
Mode: <deep+dump | dump>
Found: N result(s) in M file(s)

--- <filename>.md ---
<verbatim matching entry, including attribution tag if present>

--- <filename>.md [via vectors] ---
<entry found through vector cluster/similarity expansion>

--- <filename>.md [via graph] ---
<entry found through graph edge traversal>

--- <filename>.md [via taxonomy] ---
<entry found through taxonomy broadening>

[Related context]
<brief enrichment from graph.md/assets.md, if applicable>

• Group results by source file
• Show the full entry (heading + body), not just the matching line
• Preserve attribution tags verbatim
• Tag deep traversal results with provenance
• Git history fallback: --- git history (commit abc1234) ---
• If no results: No record found in the memory files.
• No preamble — start directly with PMM Query Results

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Agent Dispatch (eager mode)

Used only when Mode: eager.

Dispatch a general-purpose agent using the Readonly Agent Model from memory/config.md (default: haiku). Replace <project-root> with the actual project root path and <user-query> with $ARGUMENTS.

Query PMM memory files. This is a READ-ONLY task — do not edit any files.

Project root: <project-root> User query: <user-query>

Step 1 — Parse Query

Extract from the user query:

• Keyword / question — core search term
• Attribution filter — by namespace:name
• Date filter — since YYYY-MM-DD or before YYYY-MM-DD
• File scope — in <filename>
• Deep flag — word deep present
• Dump flag — word dump present

Step 2 — Route to Relevant Files

If file scope is set, search only that file. Otherwise use the routing table:

• Decisions / ratified → decisions.md
• Preferences / style → preferences.md
• Tone / voice / lens → voices.md
• Recent work / latest → last.md, progress.md
• Relationships → graph.md, vectors.md
• History / timeline → timeline.md
• Rules / standing → standinginstructions.md
• People / tools → assets.md
• Facts / background → memory.md
• Processes / workflows → processes.md
• Mistakes / lessons → lessons.md
• Categories / naming → taxonomies.md
• Ambiguous / broad → all active files

Read <project-root>/memory/config.md to confirm active files. Skip deactivated files.

Step 3 — Search

For each target file:

1. Read the file
2. Match entries containing the keyword (case-insensitive)
3. Apply attribution filter if set
4. Apply date filter if set
5. Collect matches with source file noted

Step 4 — Deep Traversal (deep=true only)

4a — vectors.md: Find clusters/similarities containing keyword (score ≥ 0.6). Tag results [via vectors].

4b — graph.md: Find edges containing keyword, collect one-hop neighbours. Tag results [via graph].

4c — taxonomies.md: Find categories containing keyword, collect siblings. Tag results [via taxonomy].

Deduplicate against Step 3 results.

Step 5 — Full File Escalation (T3)

If Steps 3 and 4 returned no results:

1. Read memory/config.md for the Active Files list
2. For each active file loaded with tail:N, header, or skip at session start: re-read the file in full using the Read tool
3. Search the full content for the query keyword
4. If matches found, collect them and proceed to Step 8 (format output)
5. If still no matches, proceed to Step 6 (git fallback)

Step 6 — Fallback Chain

If Steps 3+4+5 return no results:

1. Check timeline.md and last.md
2. Run: git log --all --grep="<keyword>" --oneline
3. For matching commits: git show <hash> -- memory/ — extract relevant lines
4. If still nothing: return No record found in the memory files.

Never hallucinate past context.

Step 7 — Cross-Reference Enrichment

Skip if deep=true. Otherwise: if results mention a named entity in graph.md or assets.md, append a brief Related context: note (1–2 lines max) if it adds meaningful information.

Step 8 — Format Output

Prose mode (dump=false): synthesized narrative with inline citations and Sources: footer.

Dump mode (dump=true): structured PMM Query Results block with verbatim entries grouped by file, provenance tags on deep-traversal results.

Return the formatted output as a string. Do not write to any file.

Output the agent's return value verbatim.

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Notes

• Context-first path is the default when session_start: lazy (hook-loaded sessions). This eliminates agent dispatch for in-window queries.
• Agent dispatch is the fallback for eager mode or unwired sessions — file reads happen inside the agent.
• Phase 4 Recall in the main session handles implicit recall mid-conversation. This skill is the explicit, filterable version.
• Model selection follows Readonly Agent Model in memory/config.md (default: haiku). No reasoning required for read-only traversal.
• Attribution tags ([user:name], [agent:name], [system:process]) identify who originated each piece of information. Always preserve and surface them.
• For the full memory file reference, see references/core.md.