agentleFS
Sign inStart free
MovGP0MovGP0/Skills/core-actionbook/SKILL.md

core-actionbook

Skill1 starsChanged 4 months agoMITView on GitHub

Internal support skill for actionbook MCP selectors used by Rust documentation research workflows. Use only when another rust-skills workflow explicitly requests actionbook-backed selectors.

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    search_actions: - Search by keyword, returns URL-based action IDs with content previews

    line 14
  2. Distinctive:

    Execute: - Use returned selectors with your browser automation tool

    line 16

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 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

Actionbook

Pre-computed action manuals for browser automation. Agents receive structured page information instead of parsing entire HTML.

Workflow

  1. search_actions - Search by keyword, returns URL-based action IDs with content previews
  2. get_action_by_id - Get full action manual with page details, DOM structure, and element selectors
  3. Execute - Use returned selectors with your browser automation tool

MCP Tools

  • search_actions - Search by keyword. Returns: URL-based action IDs, content previews, relevance scores
  • get_action_by_id - Get full action details. Returns: action content, page element selectors (CSS/XPath), element types, allowed methods (click, type, extract), document metadata

Parameters

search_actions:

  • query (required): Search keyword (e.g., "airbnb search", "google login")
  • type: vector | fulltext | hybrid (default)
  • limit: Max results (default: 5)
  • sourceIds: Filter by source IDs (comma-separated)
  • minScore: Minimum relevance score (0-1)

get_action_by_id:

  • id (required): URL-based action ID (e.g., example.com/page)

Example Response

{
  "title": "Airbnb Search",
  "url": "www.airbnb.com/search",
  "elements": [
    {
      "name": "location_input",
      "selector": "input[data-testid='structured-search-input-field-query']",
      "type": "textbox",
      "methods": ["type", "fill"]
    }
  ]
}

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

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.

Check your setup, free