AI Agent Skills

标准化实验日志记录——直接上传或读取本地图片、语音和文字，产出带 YAML frontmatter 的 Markdown；可选集成飞书 CLI 与 Obsidian。

Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when use…

🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓

Multi-agent systems, memory, planning, reasoning loops

📚 Two books on harness engineering — the design philosophies behind Claude Code & Codex: constraints, query loops, context governance, multi-agent verification. harness…

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "spli…

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change,"…

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experimen…

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, r…

Package a finished local project into an intentional GitHub repository, create a strong README and visual preview, push it safely, configure a public GitHub Pages URL wh…

A codex plugin for running optimization loops inside a codebase. It is useful when you have a measurable target and many possible changes to try: test runtime, build spe…

The most autonomous agent framework. No approval loops. No babysitting. Configure once, forget forever.

Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Cl…

The ultimate playground to learn, experiment with, and compare modern open-source AI agent frameworks — from basics to production-ready setups.

The living ecosystem where AI agents learn from real-world work through iterative workflow loops, reusable experience, and collective training signal exchange.

The operational layer for coding agents. Memory, validation, and feedback loops that compound between sessions.