FrameworkTesting and debugging

skill-conductor

Choose an architecture before creating, evaluating, improving, and packaging reusable AI skills.

Stars: 110
Forks: 15
Updated: Updated Jul 9, 2026

FrameworkTesting and debugging

skill-conductor

Choose an architecture before creating, evaluating, improving, and packaging reusable AI skills.

Stars: 110
Forks: 15
Updated: Updated Jul 9, 2026

FrameworkTesting and debugging

letta-evals

@letta-aiPython

Evaluates stateful agents with datasets, graders, rewards, multi-turn cases, and repeatable suites.

Project overview

Letta Evals organizes agent evaluation from dataset and target through extractors, graders, rewards, and stored results. It supports JSONL or CSV data, multi-turn samples, multiple model handles, cached re-grading, deterministic or model-judge graders, and custom agent factories. Runs target Letta Code through a self-hosted or cloud server, so Python 3.11+, a running Letta service, and provider credentials are required.

Stars: 77
Forks: 12
License: Apache-2.0

FrameworkTesting and debugging

playwright

@microsoftTypeScript

Automate and test Chromium, Firefox, and WebKit through one API.

Stars: 92,830
Forks: 6,091
License: Apache-2.0

FrameworkTesting and debuggingAndroid

lamda

@firerpaPython

Control, automate, and operate Android devices or fleets from one integrated platform.

Project overview

FIRERPA combines Android remote desktop, UI automation, OCR and image matching, traffic capture, Frida, proxy networking, and device operations behind one on-device service and Python client. It can work with or without root and expose controls through a browser, APIs, MCP clients, or a natural-language agent. The platform targets automation engineering, mobile security work, and long-running device farms, so its breadth and deployment complexity go well beyond a simple phone mirroring utility.

Stars: 7,886
Forks: 1,048
License: MIT

FrameworkTesting and debugging

agent-skills-eval

@darkrishabhTypeScript

Compare skill-enabled and baseline runs to measure whether an Agent Skill actually helps.

Project overview

agent-skills-eval tests an Agent Skill against the same prompts in two modes: with the skill loaded and without it as a baseline. A judge model grades both outputs against defined assertions, producing JSON and JSONL artifacts plus a static HTML report that shows measurable lift, failures, timing, and tool-call behavior. The project includes a command-line runner and a TypeScript SDK, supports OpenAI-compatible chat backends and custom providers, and can run in CI. Users supply both the target model and the judge model.

Stars: 620
Forks: 33
License: MIT

skill-conductor

skill-conductor

Project overview

Repository facts

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