agentleFS
tboome33tboome33/obsidian-mcp-router/skills/read-search/SKILL.md

read-search

Skill1 starsChanged 7 months agoApache-2.0View on GitHub

Plain-text (substring) search inside a vault, with surrounding context.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Call the obsidian-router search MCP tool with arguments parsed from $ARGUMENTS.

    line 16
  2. Very specific:

    if user includes --all or "across all vaults", set vault to *

    line 31
  3. Very specific:

    If many matches (>20), summarize counts and show the first 20.

    line 42

≈ “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

read-search

Call the obsidian-router search MCP tool with arguments parsed from $ARGUMENTS.

Arguments

Required:

  • query — the substring to find.

Optional:

  • vault — vault name. Omit for default. Pass * to fan-out across ALL vaults in parallel.
  • contextLength — number of characters of surrounding context per match. Default 100.

Argument parsing from $ARGUMENTS

  • the entire $ARGUMENTS as query is fine for the most common case
  • vault=X query="some words" or vault=* query="words"
  • if user includes --all or "across all vaults", set vault to *

Output format

Render results as a markdown list, one per matching file:

<path> (vault name if cross-vault)
  - <text snippet with surrounding context>

If many matches (>20), summarize counts and show the first 20. Suggest read-search-smart (semantic) if the user seems to be looking by meaning rather than literal text.

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