agentleFS
Lethe044Lethe044/agentskill-kit/skills/api-endpoint-tester/SKILL.md

api-endpoint-tester

Skill48 starsChanged 17 days agoMITView on GitHub

Runs a list of HTTP checks against a base URL from a JSON file and reports status codes, response time and body assertions. Use when the user asks whether an API is up, wants a smoke test after a deploy, or needs to verify that endpoints return what the docs promise.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

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:

    After a deploy, to confirm the main endpoints respond.

    line 18
  2. Distinctive:

    When the user suspects an endpoint changed shape or status code.

    line 19

≈ “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 endpoint tester

A tiny smoke test runner with no dependencies. It sends real HTTP requests, so only point it at services the user owns or has permission to test.

When to use

  • After a deploy, to confirm the main endpoints respond.
  • When the user suspects an endpoint changed shape or status code.
  • To turn a list of curl commands into a repeatable check.

How to run

python scripts/check_api.py checks.json --base-url https://staging.example.com

The checks file is a JSON list:

[
  {"name": "health", "method": "GET", "path": "/health", "expect_status": 200, "expect_body_contains": "ok"},
  {"name": "create", "method": "POST", "path": "/items", "json": {"name": "demo"}, "expect_status": 201}
]

Optional fields: headers (object), json (request body), expect_status (default 200), expect_body_contains (string) and timeout (seconds, default 10).

Output

JSON with base_url, passed, failed and results. Each result has name, ok, status, elapsed_ms and, when it failed, a reason. The exit code is 1 when any check fails.

After running

Report failures first with the reason and status code. Mention slow endpoints (over one second). Do not retry failing POST requests automatically, they may create duplicates.

Errors

A missing checks file exits with code 2. Invalid JSON or a malformed check exits with code 3. Connection errors are reported as failed checks, not crashes.

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.