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Search corpus

Skill abetatos/seed-grimoire/.claude/skills/search-corpus

Targeted grep across a book's canon, plan, summaries, and chapter prose. Tier priority canon > plan > summary > chapter. Use this when the writer needs to recall a specific named character, place, magic term, or earlier line without loading every file into context. Invoke as "search for X in book N" or "where did we mention X?"From its SKILL.md

Install
npx -y skills add abetatos/seed-grimoire --skill search-corpus

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.9 KB, 655 tokens by cl100k_base, as published. Nobody here has run it

search-corpus

You are running the search-corpus skill. The writer needs to find something specific without pulling whole files into context.

When to invoke

  • "Where did we name the river?"
  • "What was the magic cost we established for X?"
  • "Did this character appear before?"
  • "Find every mention of <term>."

Hard rules

  • Prefer canon hits to chapter hits. Canon is the source of truth; chapter prose may be inconsistent (and you'll catch it that way).
  • Don't paste large chunks back. Quote the hit, identify the file, and let the user read more if they want. The point is to not bloat context.
  • If canon and chapter prose disagree, flag it. Don't silently resolve.

Steps

0. Dispatch the search to a subagent (main thread only)

If you can spawn subagents (you have the Agent tool), run the search in the Explore subagent (Agent tool, subagent_type: Explore, model: haiku — it's grep + excerpts, no literary judgment needed) so the raw hits and ±2-line context never bloat the main conversation — only the answer comes back. Give it: the query, the series/book, the **tier priority (canon > plan > summary

chapter)**, and the steps below (run search.py, read the output, report). Tell it to return only the resolved answer + the file:line of the load-bearing hits, and to flag any canon-vs-chapter contradiction rather than resolve it. When it returns, relay its answer. Skip the rest of these steps yourself — they are what the subagent runs.

(If you have no Agent tool, just run steps 1-3 here.)

1. Run the search

python3 .claude/skills/search-corpus/scripts/search.py \
    --series-slug <slug> --book-number <N> --query "<regex or literal>"

Options:

  • --max-hits 20 to widen.
  • --tiers canon,plan to restrict (defaults to all four).

2. Read the output

Each hit shows file:line, tier tag, and ±2 lines of context. The script returns up to 12 hits by default in tier priority order.

3. Report to user

  • If 0 hits: tell the user. Suggest the term may be new; if they're about to write it, suggest promoting to canon.
  • If 1-3 clean canon hits: quote them with file:line links and answer the question.
  • If many hits across tiers: summarize the canon view first, then note where chapter prose discusses it.
  • If canon and chapter contradict: flag it explicitly. Do not pick a winner.

What this skill does NOT do

  • Does not modify any files.
  • Does not invent canon entries.
  • Does not summarize the whole book; it answers one specific question.

What ships with it: 1 file

1.6 KB alongside SKILL.md, 1 of them executable

scripts/

Keep looking

Skills are one crate of 326,506. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.