agentleFS
boazsegevboazsegev/omoya/skills/ai-tool-writing/SKILL.md

ai-tool-writing

Skill1 starsChanged 6 days agoMITView on GitHub

Use to design or review agent-callable tools (harness, MCP, function schemas); not library APIs or skills.

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:

    Run realistic tasks through a model.

    line 43
  2. Distinctive:

    Test schema shape, each normalization and each error text.

    line 44

≈ “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

Write Agent Tools

Split

  • One action per tool, named <subject>-<action> (worker-create), not worker(action: "create").
  • Fit agent tasks, not endpoints (logs-search over logs-read); merge always-chained steps.
  • Inject known context (cwd, agent, session). Separate read-only from mutating tools.

Schema

  • Closed objects (additionalProperties: false). Nest only for a list of same-shape items (edits: [{oldText, newText}]), never as option wrappers. required lists only what the action cannot run without.
  • One type per field; no combinators, type arrays or mixed forms. Two shapes are two tools.
  • Enums for closed sets; split behavior-changing modes into tools.
  • Explicit, consistent names (user_id, timeout_ms). State defaults. Bound output with limit/offset.
  • Intended inputs only.

Parse

  • Normalize in one entry pipeline: strict schema, liberal parser.
  • Infer intent: lone value to array, numeric/boolean/JSON strings to values, enum case, relative paths, defaults, unique prefixes.
  • Error only when no single reading exists: missing required value, conflict, ambiguous match. Destructive actions require exact targets.
  • Errors teach: received value, valid form or candidates, next call.

Describe

  • Each description is a short skill: what it does, when to pick it over neighbors, input meaning, effects, limits, likeliest mistake. One to three sentences.
  • Parameter descriptions add only format, units, example or default.
  • Fix misuse in descriptions or errors first.

Return

  • Names and next-step facts; IDs only when a follow-up call needs them.
  • Confirm mutations (target, count); never return empty.
  • On truncation, state how to get more.

Verify

  • Run realistic tasks through a model; check transcripts for wrong tool, bad arguments, retries.
  • Test schema shape, each normalization and each error text.

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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.