api-ai-augmented
>
pinned to #54824d6updated 2 weeks ago
Ask your AI client: “install skills/api-ai-augmented”.
Requires the metahub MCP server installed in your client. Set up MCP.
mh install skills/api-ai-augmentedmetahub onboarded this repo on the author's behalf.
If you own github.com/LambdaTest/agent-skills on GitHub, claim the listing to take over publishing. Your claim preserves the existing eval history and badges; only the curator label is replaced with verified-publisher on your next publish.
Stars
325
Last commit
2 weeks ago
Latest release
published
About this skill
Pulled from SKILL.md at publish time.
Design LLM tool definitions, agentic workflows, and natural language API interfaces.
Evaluation report
WarningsAutomated checks the publisher passed at publish time — structure, docs, safety, and whether the artifact behaves as claimed.54824d6· 2 weeks ago
Documentation
32Description qualitywarn
8 words · 52 chars — skills use the description as their trigger; aim higher — manifest description is empty; graded the GitHub repo description instead
Aim for 15+ words and include trigger phrases like “use this skill when …”.
README is present and substantial
6,607 chars · 7 sections · 6 code blocks
Tags / topics declaredwarn
No manifest tags and no GitHub repo topics
Add tags to the manifest (or GitHub topics on the repo) so the registry's search and category filters surface this artifact.
README has usage / example sections
found: Getting Started
Homepage / docs URL declared
https://agentskillsforall.com/
Release history
1- releasecurrent54824d6warn2 weeks ago
Contents
AI-Augmented API Skill
Design LLM tool definitions, agentic workflows, and natural language API interfaces.
Anthropic Tool Use Definition
{
"name": "search_products",
"description": "Search for products by keyword, category, or price range. Use when the user wants to find, browse, or compare products.",
"input_schema": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query keywords"
},
"category": {
"type": "string",
"enum": ["electronics", "clothing", "books", "home"],
"description": "Optional category filter"
},
"min_price": { "type": "number", "description": "Minimum price in USD" },
"max_price": { "type": "number", "description": "Maximum price in USD" },
"limit": { "type": "integer", "default": 10, "description": "Max results to return" }
},
"required": ["query"]
}
}
OpenAI Function Calling Definition
{
"type": "function",
"function": {
"name": "create_order",
"description": "Create a new order for a user. Use when the user wants to purchase a product. Always confirm product and quantity before calling.",
"parameters": {
"type": "object",
"properties": {
"product_id": { "type": "string", "description": "The product ID to order" },
"quantity": { "type": "integer", "minimum": 1, "description": "Quantity to order" },
"shipping_address": {
"type": "object",
"properties": {
"street": { "type": "string" },
"city": { "type": "string" },
"country": { "type": "string" }
},
"required": ["street", "city", "country"]
}
},
"required": ["product_id", "quantity", "shipping_address"]
}
}
}
MCP (Model Context Protocol) Tool Schema
{
"name": "get_build_status",
"description": "Get the status of a HyperExecute test job. Use when the user asks about test results, job status, or CI build outcomes.",
"inputSchema": {
"type": "object",
"properties": {
"job_id": { "type": "string", "description": "The HyperExecute job ID" }
},
"required": ["job_id"]
}
}
🔗 Real-World Integration — TestMu AI HyperExecute Build MCP tools that let AI agents query and control test jobs via the HyperExecute API. Docs: https://www.testmuai.com/support/api-doc/?key=hyperexecute
Tool Design Principles
- One tool = one action: Don't combine search + filter + sort into one tool. Split them.
- Description drives routing: The LLM picks tools from descriptions — be specific and include trigger phrases.
- Required vs optional: Only mark fields
requiredif the API truly needs them. - Enum for constrained values: Use
enuminstead ofstringfor fixed-choice fields. - Idempotent where possible: Prefer read tools over write tools for exploration.
- Confirm before destructive actions: Description should say "Always confirm with the user before calling."
Agentic Workflow Example
User: "Get me the status of my last 3 test builds"
Agent plan:
1. call list_jobs(limit=3, sort="created_at:desc")
→ returns [{id: "job_1", status: "passed"}, {id: "job_2", status: "failed"}, ...]
2. call get_job_details(job_id="job_2") // dig into the failed one
→ returns task breakdown, error logs
3. Synthesize: "Your last 3 builds: job_1 passed, job_2 failed (2 of 15 tasks failed on Chrome/Win10), job_3 passed."
Natural Language → API Mapping Table
Build this mapping for any domain:
| Natural language intent | API call |
|---|---|
| "Find hotels in Paris" | GET /hotels/search?location=Paris |
| "Book a room for 2 nights" | POST /bookings |
| "Cancel my reservation" | POST /bookings/{id}/cancel |
| "Show my past orders" | GET /orders?user=me&sort=date:desc |
| "Is the API working?" | GET /health/ready |
API-as-Plugin (OpenAPI → GPT Plugin / Tool)
Minimal ai-plugin.json:
{
"schema_version": "v1",
"name_for_human": "My API",
"name_for_model": "my_api",
"description_for_human": "Access my service's data and actions.",
"description_for_model": "Use this plugin to search, create, update and delete resources in My API. Always prefer specific endpoints over generic ones. Confirm destructive actions with the user first.",
"auth": { "type": "oauth" },
"api": { "type": "openapi", "url": "https://api.example.com/openapi.json" }
}
Reviews
No reviews yet. Be the first.
Related
Verification Before Completion
Evidence before assertions, always
Writing Plans
Turn specs into phased implementation plans
Test-Driven Development
Red → green → refactor discipline for any feature or bugfix
mh install skills/api-ai-augmented