explore-api

Generate and execute falsifiable claims to validate external API behavior.

7|2|Updated Jan 3, 2026

Search Tags:#automation #testing #documentation #api #verification #sdk

Ready to run

npx skills add https://github.com/BrennonTWilliams/little-loops --skill explore-api-brennontwilliams

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: explore-api
Source: https://github.com/BrennonTWilliams/little-loops/tree/main/.gemini/skills/explore-api
Command: npx skills add https://github.com/BrennonTWilliams/little-loops --skill explore-api-brennontwilliams

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates the uncertainty of integrating external libraries by automating the discovery and verification process, ensuring your code relies on proven facts rather than assumptions.

Core Features & Use Cases

Feathers Learning Test Lifecycle: Guides the agent through Ingest, Hypothesize, Execute, and Refine phases to document API behavior.
Proof Registry: Persists findings to a centralized Learning Test Registry for team-wide knowledge sharing.
Use Case: When integrating a new third-party SDK, use this skill to confirm exact event emission patterns and response shapes before writing production code.

Quick Start

Use the explore-api skill to verify the behavior of the Anthropic SDK streaming functionality and record the proof.

Frequently Asked Questions about explore-api

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I verify external API behavior before integrating an SDK into production code?▼

You can verify external API behavior by generating and executing falsifiable claims that test SDK and HTTP API responses. This process creates persistent learning test records to ensure your code relies on proven facts rather than assumptions.

What is a learning test and when do I need it for API documentation?▼

A learning test is a persistent record that documents exact API behavior through executed proof scripts and YAML-based outcomes. You need it when integrating third-party libraries to eliminate uncertainty about event emission patterns and response shapes.

How do I document API behavior using falsifiable claims for team-wide knowledge sharing?▼

You document API behavior by running proof scripts through a lifecycle of Ingest, Hypothesize, Execute, and Refine phases. Findings are persisted to a centralized Learning Test Registry, enabling team-wide knowledge sharing of verified API facts.

Do I need shell execution to run proof scripts for HTTP API verification?▼

Yes, shell-based execution is required to run the proof scripts that validate external API and library behavior. The verification process relies on executing these scripts and documenting test outcomes in YAML format.

Can I use automated proofs to confirm SDK event emission patterns and response shapes?▼

Yes, automated proofs validate exact event emission patterns and response shapes for SDKs and standard library functions. By generating falsifiable claims, you confirm specific API behaviors before writing production code.

What are the limitations of using learning tests for standard library functions?▼

Learning tests require shell-based execution and YAML documentation, limiting use to environments supporting these formats. They document existing behavior but do not modify library functions or guarantee future API stability.