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 h4vzz/awesome-ai-agent-skills --skill api-integrationgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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/h4vzz/awesome-ai-agent-skills/api-integration)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/api-integration"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/api-integration/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/h4vzz/awesome-ai-agent-skills/api-integration"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/api-integration.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 | $0.00033 | $0.02491 |
| Opus 5.5 | $0.00013 | $0.00996 |
| Sonnet 5.5 | $0.00007 | $0.00498 |
| Haiku 4.5 | $0.00003 | $0.00249 |
Grade A, and why
api-integration scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to api-integration — 2 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
API Integration
This skill enables an AI agent to integrate applications with external APIs reliably. The agent selects the right integration pattern (REST client, webhook consumer, polling, SDK wrapper), implements authentication (API keys, OAuth, JWT), handles errors with retries and circuit breakers, and respects rate limits. The result is production-grade integration code that handles real-world failure modes.
Workflow
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Analyze the target API: Review the API documentation, OpenAPI spec, or SDK reference to understand available endpoints, authentication requirements, rate limits, and response formats. Identify whether the API supports webhooks for push-based updates or requires polling. Note any idiosyncrasies like non-standard error formats or pagination schemes.
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Choose an integration pattern: Select the appropriate pattern based on the use case. Use a REST client for on-demand request/response interactions. Use webhook consumers for real-time event-driven data. Use polling when the API has no webhook support but you need near-real-time updates. Wrap official SDKs when they exist to add retry logic, logging, and a consistent interface.
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Implement authentication: Configure the correct authentication method—API key in headers, OAuth 2.0 bearer tokens, JWT-based service auth, or basic auth. Store credentials securely using environment variables or a secrets manager. For OAuth flows, implement token refresh logic so long-running integrations don't break when access tokens expire.
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Build the client with error handling: Write the integration code with structured error handling. Catch HTTP errors by status code category: 4xx for client errors (don't retry), 429 for rate limiting (retry with backoff), 5xx for server errors (retry with exponential backoff). Parse error response bodies for actionable messages. Log all requests and responses at debug level for troubleshooting.
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Add retry and circuit breaker logic: Implement exponential backoff with jitter for transient failures. Set a maximum retry count (typically 3-5). Implement a circuit breaker that opens after consecutive failures and periodically allows a test request through. This prevents cascading failures when a downstream API is degraded.
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.
- 1mo ago First seen · 235 lines · 33 tokens per session scan A c0b011725580
api-integration is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 20d ago), licensed MIT. It adds 33 tokens to every session and 2,491 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to api-integration, differing in 2 lines, and is treated as a copy.
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