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AbilityaiAbilityai/cornelius/.claude/skills/quick-search/SKILL.md

quick-search

Skill109 starsChanged 38 days agoMITView on GitHub

Fast knowledge base retrieval - single FAISS search + graph connections, no subagents or LLM orchestration

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    NO subagent spawning: - do everything inline

    line 61
  2. Distinctive:

    NO spreading activation: - use static mode (faster for simple lookups)

    line 64
  3. Distinctive:

    Parallel execution: - run search and connections in the same message

    line 65
  4. Distinctive:

    Maximum 3 tool calls: - search + connections + read top file

    line 66
  5. Very specific:

    If the user wants deeper analysis, tell them to use /recall or /find-connections

    line 67

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Quick Search

Fastest possible knowledge base retrieval. No subagents, no multi-layer orchestration, no changelogs.

Purpose

Return relevant notes and their graph neighborhood in minimal tool calls. Designed for speed over thoroughness.

Query

$ARGUMENTS

Process

Execute these two commands in parallel (single message, two Bash calls):

# 1. Semantic search (6-14s - the unavoidable cost)
BRAIN_READ_SCOPE=core,Books,document-insights resources/local-brain-search/run_search.sh "$ARGUMENTS" --limit 5 --json

# 2. Graph connections for likely top hit (0.3s - nearly free)
BRAIN_READ_SCOPE=core,Books,document-insights resources/local-brain-search/run_connections.sh "$ARGUMENTS" --json
# read role: lookup (contract: scope-mount) - group the output: your thinking (core) · what you have read (Books / Document Insights); --scope core pins to your own notes

Then read the top result file using Read tool.

That's it. Three tool calls. Present results and stop.

Output Format

Keep it brief:

## [Query]

**Top matches:**
1. [[Note Title]] (0.XX) - [one-line summary from content]
2. [[Note Title]] (0.XX) - [one-line summary]
3. [[Note Title]] (0.XX) - [one-line summary]

**Graph neighborhood** (for top hit):
- Outgoing: [[Note]], [[Note]], ...
- Incoming: [[Note]], [[Note]], ...

**Top result content:**
[First ~30 lines of the highest-scoring note]

Rules

  • NO subagent spawning - do everything inline
  • NO changelog creation - this is a read-only lookup
  • NO multi-layer expansion - one search, one connection call, done
  • NO spreading activation - use static mode (faster for simple lookups)
  • Parallel execution - run search and connections in the same message
  • Maximum 3 tool calls - search + connections + read top file
  • If the user wants deeper analysis, tell them to use /recall or /find-connections

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Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.