Skip to main content
AI/MLathola

palace-index-curator

Curate the web-capture index. Use when the capture backlog grows, captures sit unprocessed at seedling/pending, or to surface stored research during work.

Stars
294
Source
athola/claude-night-market
Updated
2026-05-30
Slug
athola--claude-night-market--palace-index-curator
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/athola/claude-night-market/HEAD/plugins/memory-palace/skills/palace-index-curator/SKILL.md -o .claude/skills/palace-index-curator.md

Drops the SKILL.md into .claude/skills/palace-index-curator.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

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)

# Dry run: prints promote/archive proposals, writes nothing.
uv run python scripts/memory_palace_cli.py index promote

# Apply: backs up the index under data/backups/, then persists.
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

  • index report runs and prints the inert ratio and orphan count for the live index.
  • index promote (no flag) prints proposals and writes nothing (the index file is byte-identical afterward).
  • index promote --apply creates a timestamped backup under data/backups/ before persisting, and a re-run proposes nothing.
  • With context_injection: true, a SessionStart event surfaces the top promoted captures; with the flag off, it stays silent.
  • Failure modes (missing index, corrupt YAML, missing backing files) are handled without raising: report degrades, promote holds, hook exits silently.