agentleFS
pesappesap/skills/good-api/SKILL.md

good-api

Skill1 starsChanged 3 months agoMITView on GitHub

Assess how users grow from first success to advanced composition without learning contradictory semantics or ejecting from the API. 1. Name the target users: beginner, novice, expert, and enterprise/integration-heavy if relevant. 2. Assess the API as a learning ladder: - Flexible: experts can solve many real problems without hidden restrictions or forced eject paths. - Gradual: novices can learn one stable layer at a time; adding power does not contradict earlier semantics. - Convenient: beginners can solve the common case…

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    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:

    Check for hidden dependencies, restrictive coupling, oversimplified data models, and semantic surprises between layers.

    line 30
  2. Distinctive:

    Treat convenience wrappers as packaging over understandable primitives, not substitutes for a flexible model.

    line 32
  3. Distinctive:

    Read the ladder model when the full vocabulary, failure modes, or source-derived review questions are needed.

    line 37
  4. Very specific:

    Use evals/trigger-prompts.json only when tuning recognition and evals/evals.json only when grading non-trivial API reviews.

    line 39

≈ “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

API design

Assess how users grow from first success to advanced composition without learning contradictory semantics or ejecting from the API.

Core method

  1. Name the target users: beginner, novice, expert, and enterprise/integration-heavy if relevant.
  2. Assess the API as a learning ladder:
    • Flexible: experts can solve many real problems without hidden restrictions or forced eject paths.
    • Gradual: novices can learn one stable layer at a time; adding power does not contradict earlier semantics.
    • Convenient: beginners can solve the common case quickly with defaults, examples, or safe packaging.
  3. Design or recommend changes in this order: flexible primitives first, gradual layers second, convenient wrappers/defaults third.
  4. Check for hidden dependencies, restrictive coupling, oversimplified data models, and semantic surprises between layers.
  5. Treat convenience wrappers as packaging over understandable primitives, not substitutes for a flexible model.

Reference router

  • Read the ladder model when the full vocabulary, failure modes, or source-derived review questions are needed.
  • Use evals/trigger-prompts.json only when tuning recognition and evals/evals.json only when grading non-trivial API reviews.

Output

  • Ladder assessment: flexible / gradual / convenient.
  • Top API risks, ranked by user impact.
  • Concrete redesign recommendations.
  • Tradeoffs and cases where convenience should win.

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