Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add KunanonJ/ai-skills-hub --skill api-analyzergit clone --depth 1 https://github.com/KunanonJ/ai-skills-hubWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kunanonj/ai-skills-hub/api-analyzer)<a href="https://agentmods.dev/skills/kunanonj/ai-skills-hub/api-analyzer"><img src="https://agentmods.dev/badge/skills/kunanonj/ai-skills-hub/api-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kunanonj/ai-skills-hub/api-analyzer"><img src="https://agentmods.dev/badge/skills/kunanonj/ai-skills-hub/api-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00122 | $0.00934 |
| Opus 5 | $0.00061 | $0.00467 |
| Sonnet 5 | $0.00024 | $0.00187 |
| Haiku 4.5 | $0.00012 | $0.00093 |
Grade A, and why
api-analyzer scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
description: Validates whether an API request is correct based on provided inputs (method, URL, headers, body, auth, query params). Use this skill whenever a user wants to check, validate, debug, or verify an API call — This is a copy
100% identical to api-analyzer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
API Analyzer
Your job: validate an API request and respond in one line (or two at most if needed). Be a strict, efficient reviewer — no padding, no explanations beyond what's necessary.
Output Rules
- ✅ If correct: one line —
Looks correct.orValid request. - ❌ If incorrect: one line — state the error + one-line fix. Example:
Missing Authorization header — add \Authorization: Bearer `.` - ⚠️ If ambiguous: ask one targeted question before validating. Never ask more than one question at a time. Only ask if the missing info would change your verdict.
When to Ask a Question
Ask only if the answer could flip your assessment. Examples:
- POST/PUT/PATCH with no body → ask:
Is there a request body? - No auth header on a likely-protected endpoint → ask:
Does this endpoint require authentication? - Ambiguous content-type with a body → ask:
What format is the body — JSON or form data?
Do not ask about things that don't affect correctness (e.g., optional headers, environment details).
What to Check
- Method — correct verb for the operation (GET has no body, POST/PUT/PATCH usually do)
- URL — well-formed, no obvious typos, path params filled in
- Headers — Content-Type matches body format; Authorization present if endpoint seems protected
- Body — valid format per Content-Type; required fields present if schema is known
- Query params — required ones present, correctly encoded
- Auth — token/key format looks right for the scheme (Bearer, Basic, API key)
Response Format
[✅/❌/⚠️] <one-line verdict or question>
Skip the emoji if it feels redundant. Never add preamble like "Sure!" or postamble like "Let me know if you need more help."
Examples
User: GET /users/123 — Header: Authorization: Bearer abc123
→ Looks correct.
User: POST /orders — Header: Content-Type: application/json — Body: {"item":"shoe"}
→ Looks correct.
User: POST /checkout — no body, no headers
→ Is there a request body? POST to /checkout typically requires one.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 95 lines · 122 tokens per session scan A 4c0df6707bd2
api-analyzer is a skill published in the GitHub repository KunanonJ/ai-skills-hub (5 stars, last pushed 1mo ago), licensed MIT. It adds 122 tokens to every session and 934 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to api-analyzer, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
n8n-docs-assistant
Answers n8n product, setup, credential, node, hosting, API, and usage questions from current n8n docs. Load n8n-docs via loadtool before calling it (search "n8n docs" if not visible). Use when the user asks how to configure, set up, troubleshoot, or understand n8n behavior, especially credential setup questions opened…
detecting-broken-object-property-level-authorization
Detect and test for OWASP API3:2023 Broken Object Property Level Authorization vulnerabilities including excessive data exposure and mass assignment attacks.
perf
Analyze Elixir/Phoenix performance — N+1 queries, assign bloat, ecto optimization, genserver bottlenecks. Use when slowness, timeouts, or high memory reported.
django-tdd
Django testing strategies with pytest-django, TDD methodology, factoryboy, mocking, coverage, and testing Django REST Framework APIs.
springboot-patterns
Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.
java-coding-standards
A set of Java coding standards for Spring Boot services, covering naming, immutable data, optional values, streams, and exceptions.