| name | palace-index-curator |
| description | Curate the web-capture index. Use when the capture backlog grows, captures sit unprocessed at seedling/pending, or to surface stored research during work. |
| alwaysApply | false |
| category | governance |
| tags | ["knowledge-management","capture-index","curation","promotion","analytics"] |
| dependencies | ["memory_palace.corpus.index_analytics","memory_palace.corpus.index_promoter"] |
Palace Index Curator
Overview
The web-research hooks auto-capture every WebFetch and WebSearch into
hooks/memory-palace-index.yaml, storing each as a markdown file and an
index entry. Captures land at the defaults routing_type: pending,
maturity: seedling, importance_score: 50, and nothing advances them.
Left alone, the index becomes a write-only graveyard: the majority of
entries are never incorporated, analyzed, or surfaced.
This skill drains that backlog and keeps it drained. It wires the
capture index to the corpus tooling the plugin already ships
(decay_model, keyword_index, marginal_value) through three
commands: a read-only report, a dry-run-first promotion engine, and a
SessionStart surfacing hook.
When to Use
- The capture backlog has grown and most entries are still
pending.
- You want a corpus health report (inert ratio, orphans, topic clusters).
- You want stored research surfaced automatically during sessions.
When NOT to Use
- Ingesting a single new resource: use
knowledge-intake.
- Searching stored knowledge ad hoc: use
knowledge-locator.
- Tending a digital garden file: use
digital-garden-cultivator.
Workflow
1. Analyze (read-only)
uv run python scripts/memory_palace_cli.py index report
Reports total entries, the inert ratio, orphaned captures (entries whose
backing file is gone), the largest topic clusters by domain, and the
top promotion candidates. Writes nothing.
2. Incorporate (dry-run, then apply)
uv run python scripts/memory_palace_cli.py index promote
uv run python scripts/memory_palace_cli.py index promote --apply
Each pending entry is classified into one action:
- promote: recent, authoritative, or clustered. Gets a real
importance score, a routing type, and maturity
seedling -> growing.
- archive: orphaned or older than the archive horizon and never
revisited. Marked
archived rather than promoted, following the
principle that unused captures should drain, not accumulate.
- hold: everything else stays
pending with no change.
Applying is idempotent: promoted and archived entries are no longer
pending, so a second run proposes nothing new. The dry-run diff is
always shown before --apply writes.
3. Surface (learn)
A SessionStart hook (hooks/index_surfacer.py) names the highest-value
promoted captures at the start of a session. It is disabled by default.
Enable it in memory-palace-config.yaml:
feature_flags:
context_injection: true
The hook only speaks when promoted entries clear the importance floor,
and it exits silently on any error so it can never block a session.
Design Notes
- Promotion uses only structural signals (recency, domain authority,
cluster size). The decision logic is deterministic; no model call
gates a transition.
- The decay half-lives (14/30/90 days) are tunable priors, not retention
constants. Wixted & Ebbesen (1997) and Murre & Dros (2015) show
forgetting follows a power law; FSRS (Ye, Su & Cao, 2022) validates
exponential decay only with a learned per-item half-life. Calibrate
against reopen logs if usage data accrues.
- Retrieval stays keyword-first (
cache_lookup / keyword_index);
embeddings are not required at the current corpus scale. BM25 is the
workhorse up to ~5000 documents; embeddings add value only for
vocabulary-mismatch discovery.
- Near-duplicate detection layers SHA-256 exact match (present via
content_hash) then MinHash with k-shingling for near-duplicates
(Broder, 1997). SimHash is preferable only at tens of thousands of
documents.
- Importance formula:
relevance = w1 * centrality + w2 * decay(t) + w3 * usage. The plugin ships all three terms (graph_analyzer
PageRank, decay_model, usage_tracker).
Exit Criteria