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Endpoint Validator

Community
notque
endpoint-validator

Deterministic API endpoint validation with pass/fail reporting.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill nameendpoint-validator
Stars
443
Forks
50
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by notque on GitHub. Read the source before you install it.

Installation

Install the Endpoint Validator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/notque/vexjoy-agent/tree/main/skills/infrastructure/endpoint-validator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/infrastructure/endpoint-validator .claude/skills/endpoint-validator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Endpoint Validator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Endpoint Validator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Endpoint Validator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Endpoint Validator Skill

Deterministic HTTP endpoint validation: discover config, test each endpoint against expectations, report pass/fail with CI-compatible exit codes.

Deep References

Load on demand when the signal matches.

SignalReferenceContent
Security header WARNs, HSTS/CSP/X-Framereferences/security-headers.mdHeader checks, required values, remediation
Config errors, hardcoded IPs, timeoutsreferences/endpoint-config-preferred-patterns.mdConfiguration failure modes and fixes
401/403 failures, auth patternsreferences/auth-endpoint-patterns.mdBearer, API-key, cookie auth validation

Instructions

Phase 1: DISCOVER

Read repository CLAUDE.md for base URL conventions or env var names.

Search for endpoint config in order: endpoints.json in project root, tests/endpoints.json, inline specification from user. Prefer version-controlled config over ad-hoc lists.

Config shape:

json
{
  "base_url": "http://localhost:8000",
  "endpoints": [
    {"path": "/health", "expect_status": 200},
    {"path": "/api/v1/users", "expect_key": "data", "timeout": 10},
    {"path": "/api/v1/search?q=test", "max_time": 2.0}
  ]
}

Endpoint fields: path (required), expect_status (default 200), expect_key (top-level JSON key), timeout (default 5s), max_time (SLOW threshold), method (default GET), headers (per-endpoint).

Rules:

  • Warn before POST/PUT/DELETE against production base URLs.
  • Use hostnames or ${ENV_VAR} in base_url, not hardcoded IPs.
  • Confirm base URL reachable before the full suite.

Gate: config parsed, base URL reachable, at least one endpoint defined.

Phase 2: VALIDATE

Execute, do not reason. Run the request. Paste the exit code and output.

Test endpoints sequentially. For each:

  1. Send request with configured method and timeout.
  2. Check status code against expect_status. Mismatch -> FAIL.
  3. If expect_key set, parse JSON and check key exists. Missing/invalid -> FAIL.
  4. If max_time set and elapsed exceeds it -> SLOW.
  5. On non-localhost URLs, check security headers (HSTS, CSP, X-Content-Type-Options, X-Frame-Options). Missing -> WARN (not FAIL). Skip HSTS on HTTP-only base URLs. Skip X-Frame-Options if CSP has frame-ancestors.

Failure handling: connection refused -> FAIL "Connection refused"; timeout -> FAIL "Timeout after Ns"; invalid JSON on expect_key -> FAIL "Invalid JSON response".

Gate: all endpoints tested, each has PASS/FAIL/SLOW verdict.

Phase 3: REPORT

Format:

ENDPOINT VALIDATION REPORT
==========================
Base URL: http://localhost:8000
  /api/health                    200 OK      45ms
  /api/products                  500 FAIL   "Internal Server Error"
  /api/slow                      200 SLOW   3.2s > 2.0s threshold

SUMMARY: Passed 13/15 (86.7%), Failed 1, Slow 1

Exit 0 if all passed. Exit 1 if any failed.

Error Handling

ErrorCauseFix
Base URL unreachableWrong port or service downss -tlnp to confirm port
All endpoints timeoutWrong host or proxy issuecurl -v a single endpoint
JSON parse failure on expect_keyNon-JSON response (HTML/XML)Remove expect_key or check Content-Type
FAIL on intentional 404Default expect_status is 200Set "expect_status": 404
401 on auth endpointMissing/expired credentialsAdd auth header; see references/auth-endpoint-patterns.md

CI Integration

bash
# Pre-deployment gate with curl
jq -r '.endpoints[].path' endpoints.json | while read path; do
  curl -sf "http://localhost:8000$path" > /dev/null || { echo "FAIL: $path"; exit 1; }
done

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Endpoint Validator AI skill do?

Deterministic API endpoint validation with pass/fail reporting.

Why use Endpoint Validator on TypingMind?

Because you install it once and use it with any model. Endpoint Validator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Endpoint Validator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/infrastructure/endpoint-validator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Endpoint Validator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Endpoint Validator?

As many as you like. As long as a model supports skills, you can use Endpoint Validator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Endpoint Validator AI skill free?

Yes. It is published on GitHub by notque under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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