corpus

/reflect:corpus — Knowledge-corpus Q&A (build → prime → query → reprime)

reflect-kb is search-shaped: every /reflect:recall is a fresh hybrid query. A corpus is the complementary pattern — a long-lived, filtered slice of the user's own learnings that you hold open and answer a question-set against.

You (the agent) ARE the Q&A session. There is no SDK subprocess: the build step snapshots the filtered learnings to a JSON file and prints a primed context document; you read that document and answer questions over it.

When to use

• The user wants to "ask the auth subsystem", "ask the migration log", "query everything tagged X" as a sustained back-and-forth — not a single search.
• A filtered slice (tag / category / project / date-window) of the KB is the unit of conversation.

For a one-shot retrieval, use /reflect:recall instead.

Filter spec

Space-separated key:value tokens. Bare tokens are treated as tags. All predicates AND together; tag: may repeat (or use tag:a,b) to OR tags.

token	meaning
tag:auth / auth	learning carries this tag (case-insensitive)
category:security	frontmatter category equals this
project:api	frontmatter project_id/project equals this
since:2026-01-01	learning created/archived date ≥ this (inclusive)
until:2026-06-30	learning created/archived date ≤ this (inclusive)

Build / prime

Run the corpus builder via the recall script (no embedding model loads — the filter is pure frontmatter logic):

plugins/reflect/skills/recall/scripts/recall.py \
  --corpus auth-subsystem \
  --corpus-filter "tag:auth category:security project:api" \
  --format markdown

This snapshots every matching learning to $REFLECT_STATE_DIR/corpora/auth-subsystem.json (default ~/.reflect/corpora/) — persisting the filter, a last-built timestamp, and the KB mtime — and prints the primed context document.

Then: read the printed document and answer the user's questions using ONLY the learnings in it. Treat it as the full context for this corpus session.

Reprime on drift

The KB drifts as the user works (new learnings ingested, old ones archived). Re-run the saved filter — no need to restate it:

plugins/reflect/skills/recall/scripts/recall.py \
  --corpus auth-subsystem --corpus-rebuild --format json

--corpus-rebuild re-applies the persisted filter against the current KB: newly-matching learnings are pulled in, entries that no longer match (or were deleted) are dropped. The JSON form reports "stale": true/false — stale means the KB has been written since the snapshot was built, i.e. it's time to reprime before answering further.

Lifecycle (claude-mem KnowledgeAgent / CorpusBuilder, adapted)

1. build — build_corpus(filter) runs the filter, writes the snapshot.
2. prime — read the printed primed document; that is your Q&A context.
3. query — answer the user's questions over the primed document.
4. reprime — on KB drift (stale), --corpus-rebuild and re-read.

The deterministic build/filter/snapshot/reprime engine lives in reflect-kb/src/reflect_kb/recall/corpus.py; the LLM-driven Q&A is yours.