Agent skill

Palace Index Curator

by athola in athola/claude-night-market

Curate the web-capture index. An agent skill from athola/claude-night-market.

MITAuto-check passed

Install Palace Index Curator

skills CLI
$ npx skills add athola/claude-night-market --skill palace-index-curator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install athola/claude-night-market palace-index-curator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/athola/claude-night-market.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/memory-palace/skills/palace-index-curator .claude/skills/palace-index-curator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
palace-index-curator
GitHub stars
341
Token cost
~2.4k tokens
SKILL.md length
1,167 words
Files
2
Skills in repo
152
Repo updated
First seen
Licence
MIT

At a glance

Curate the web-capture index. An agent skill from athola/claude-night-market.

  • Works in 4 steps: Analyze (read-only) → Incorporate (dry-run, then apply) → Committed state must be drained → …
  • The capture backlog grows
  • SKILL.md covers Overview, When To Use, When NOT to Use and Workflow, plus 5 more sections
  • Calls uv and git

What it does

Palace Index Curator is an agent skill from athola/claude-night-market. Curate the web-capture index. Use when the capture backlog grows, captures sit unprocessed at seedling/pending, or to surface stored research during work.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `modules/archive-pattern.md`).

The repository describes itself as: 23 Claude Code plugins: TDD enforcement hooks, git/PR workflows, spec-driven development, code review, project lifecycle, fix-from-error, maintenance automation, context… The licence is MIT.

When your agent uses it

  • The capture backlog grows
  • Captures sit unprocessed at seedling/pending
  • Surface stored research during work

Example prompts

  • “/palace-index-curator”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Analyze (read-only)
  2. Incorporate (dry-run, then apply)
  3. Committed state must be drained
  4. Surface (learn)

What it can do on your machine

Read from SKILL.md and the folder at commit 9f3eb00. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv and git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Palace Index Curator loads about 2.4k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 1,167 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from athola/claude-night-market at commit 9f3eb00, republished under its MIT licence (© athola). 1,167 words, ~2,381 tokens.

Download SKILL.mdSave it as .claude/skills/palace-index-curator/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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

  • A commit was blocked because the index still carries pending entries the drain held back.
  • 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)
bash
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)
bash
# 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.

Running these by hand is the exception. --apply runs on every commit from scripts/precommit_palace_maintenance.sh, so the backlog drains continuously rather than in occasional sweeps. Reach for the commands above when a commit is blocked, or when you want the dry-run diff before the hook decides for you.

3. Committed state must be drained

The commit that carries the index must carry it with zero pending entries. scripts/check_capture_index_drained.py runs at the end of the maintenance hook and fails the commit otherwise, and tests/test_capture_index_artifact.py re-checks the same invariant in CI so a bypassed hook does not land a backlog.

Two things make that gate reachable rather than a standing block:

  • The capture write stages the index (hooks/shared/deduplication._stage_index). Without it, pre-commit reverts the unstaged write before any hook runs, so the drain reads a tree the fresh capture is missing from and converges on a fixed point that excludes exactly the entries it exists to process. That is how 47 captures accumulated behind a drain that reported nothing to do.
  • The drain resolves promote and archive by itself. Only hold survives it, so a blocked commit means a specific capture needs a person to score or archive it. The gate names the keys.
4. 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:

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.

The corpus keyword index is a separate artifact

The three steps above all operate on the capture index at hooks/memory-palace-index.yaml. Retrieval reads a different file: data/indexes/keyword-index.yaml, built from the staging captures and consumed by cache_lookup. Curating one does nothing to the other.

That keyword index is derived data and is not tracked in git, so a fresh checkout has none at all. Rebuild it with:

bash
# Report what would be indexed, writing nothing.
uv run python scripts/build_indexes.py --dry-run

# Write data/indexes/keyword-index.yaml.
uv run python scripts/build_indexes.py

The builder refuses to write an empty index over a populated one. An empty corpus is reported with "wrote": false and any existing index is left untouched. Writing entries: {} over real data is how the corpus went dark in 1.5.0, and it stayed dark because the regeneration script named in that stub file had never been written.

Show full SKILL.md (501 more words)Show less

A capture whose frontmatter will not parse contributes nothing to the keyword index. Body extraction is gated on that parse, so the whole document drops out rather than just its topic, and the drain above reports nothing wrong because the index entry itself looks ordinary.

The usual cause is a page title or search query holding a double quote, which closed the YAML scalar early when the capture was written. The capture hooks escape their scalars now, so this reaches captures written before that fix and no others.

bash
# Report which captures cannot be parsed, writing nothing.
uv run python scripts/repair_capture_frontmatter.py

# Re-quote them, backing the originals up under data/backups/.
uv run python scripts/repair_capture_frontmatter.py --apply

The repair rewrites the broken scalar and nothing else, and refuses any file it cannot re-parse afterward: turning an invisible capture into a subtly wrong one is worse than leaving it alone. Rebuild the keyword index once it has run, since retrieval reads the separate artifact described above.

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), and 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).

Archiving Completed Work

Triage decides whether a single capture drains or accumulates. When a whole body of work finishes, freeze it behind an index instead of deleting it or leaving it in the active listing.

See modules/archive-pattern.md for the structure, the closing-note requirement, and the two discoverability layers.

Exit Criteria

  • build_indexes.py --dry-run reports a non-zero entry count and leaves data/indexes/keyword-index.yaml byte-identical.
  • build_indexes.py against an empty corpus reports "wrote": false and leaves an existing populated index untouched.
  • 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, and 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.
  • check_capture_index_drained.py exits 0 against the committed index and exits 1 naming the keys when one is left pending.
  • repair_capture_frontmatter.py reports zero repairable captures against the committed corpus, and refuses a file it cannot re-parse after repair.
  • A capture written by update_index appears in git diff --cached without anyone staging it by hand.

© athola, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in plugins/memory-palace/skills/palace-index-curator of athola/claude-night-market.

  • SKILL.md
  • modules/archive-pattern.md

Open the folder on GitHubat commit 9f3eb00

Compare with similar skills

Palace Index Curator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Palace Index Curator compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Palace Index Curator this skillathola/claude-night-market341—~2.4kAutomated safety check: PassMIT
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Indexabilitythedaviddias/Front-End-Checklist74k—~814Automated safety check: PassMIT
Curating Memory Palaceletta-ai/letta-code3.6k—~3.2kAutomated safety check: PassApache-2.0
Capturealirezarezvani/claude-skills28k1 repos~2.8kAutomated safety check: PassMIT
Indexability Conflictsthedaviddias/Front-End-Checklist74k—~876Automated safety check: PassMIT

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Questions about Palace Index Curator

What does Palace Index Curator do?

Curate the web-capture index. An agent skill from athola/claude-night-market. Palace Index Curator is an agent skill from athola/claude-night-market. Curate the web-capture index.

When should I use Palace Index Curator?

Palace Index Curator fits situations like: the capture backlog grows; captures sit unprocessed at seedling/pending; surface stored research during work.

How do I install Palace Index Curator in Claude Code?

Run `npx skills add athola/claude-night-market --skill palace-index-curator -a claude-code`. Or copy the skill folder (plugins/memory-palace/skills/palace-index-curator in athola/claude-night-market) into .claude/skills/palace-index-curator in your project. Claude Code loads it when a task matches its description.

How do I install Palace Index Curator in Codex?

Run `npx skills add athola/claude-night-market --skill palace-index-curator -a codex`. Or copy the skill folder (plugins/memory-palace/skills/palace-index-curator in athola/claude-night-market) into .agents/skills/palace-index-curator in your project. Codex loads it when a task matches its description.

Can I use Palace Index Curator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add athola/claude-night-market --skill palace-index-curator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/palace-index-curator, .gemini/skills/palace-index-curator, .github/skills/palace-index-curator and .opencode/skills/palace-index-curator in your project.

What does Palace Index Curator need to run?

Going by SKILL.md and its folder, Palace Index Curator needs the command-line tools its instructions call (uv and git). Our summary lists: Python 3.

Does Palace Index Curator access the network?

SKILL.md contains no URLs. Its commands use uv and git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Palace Index Curator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Palace Index Curator use?

Palace Index Curator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Palace Index Curator use?

About 2.4k tokens (SKILL.md is roughly 9.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Palace Index Curator?

Skills that share tags, products or a category with Palace Index Curator: Backlog Capture (mvschwarz/openrig, 6.8k stars), Indexability (thedaviddias/Front-End-Checklist, 74k stars), Curating Memory Palace (letta-ai/letta-code, 3.6k stars) and Capture (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Palace Index Curator?

athola (a GitHub user) maintains it in athola/claude-night-market, which has 341 GitHub stars. The repository holds 152 skills in this directory. The repository was last updated on October 9, 2026.

Source: athola/claude-night-market on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.