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This repository adapts Lauren Tan's opinionated pstack workflow, originally designed for Cursor, to support Claude Code, Codex, Pi, and other agent harnesses. It translates rigorous agent workflows using Cursor primitives to improve coding outcomes across different platforms. The tool includes named policy forks and an optional plugin for formal verification using TLA+ and Lean proofs.

• Enables pstack's structured agent workflows on non-Cursor platforms like Claude Code and Codex.
• Maintains upstream compatibility while supporting specific policy forks via tools/forks.json.
• Offers an agent-formal-verify plugin for TLA+ model checking and Lean proofs.
• Aims to keep code concise, simple, and verified through rigorous agent interactions.
• Install via Claude Code plugin marketplace using the provided add command.

Ponytail is a GitHub repository that provides a single prompt file (SKILL.md) to configure AI coding agents to prioritize extreme code reduction. It aims to mimic a senior developer who replaces verbose implementations with concise, functional one-liners. The project claims significant reductions in code volume, cost, and execution time while maintaining safety, demonstrated in a FastAPI and React context.

• Injects a specific prompt file to force AI agents into a minimalism mindset.
• Claims up to 94% less code and ~20% lower cost in specific benchmarks.
• Requires no complex setup; primarily a configuration change for existing agents.
• Validated on mixed stacks like FastAPI and React with real feature tasks.

Y Combinator president Garry Tan has open-sourced a configuration for Claude Code that utilizes 23 opinionated tools to simulate a full product team. The setup assigns specific roles such as CEO, Designer, and QA to AI agents, aiming to enable a single developer to ship at the pace of a twenty-person group. This repository captures the exact workflow Tan uses to leverage AI for high-velocity development.

• Configures 23 distinct AI tools within Claude Code to automate diverse engineering roles.
• Demonstrates a workflow where one developer outsources design, QA, and management to agents.
• Provides a practical template for engineers wanting to replicate high-leverage AI setups.
• Highlights the shift toward solo builders using AI to rival traditional team output.

The e2e framework allows developers to define test goals using natural language, which an AI agent executes against web or mobile applications. It combines agent-driven action with traditional locators and assertions to verify outcomes. A key feature is its ability to record agent actions during the first run and replay them deterministically without invoking model calls in subsequent executions.

• Write tests in natural language instead of brittle selector chains.
• Agent actions are recorded and replayed deterministically.
• Reduces LLM costs by skipping model calls on replay.
• Validates results using standard assertions and locators.
• Supports both web and mobile application testing scenarios.

OpenMontage is an open-source system that transforms AI coding assistants into full video production studios. It orchestrates multiple AI agents like Claude and ChatGPT to collaborate on 12 distinct production pipelines. The platform offers over 100 tools and hundreds of agent skills to automate complex video generation workflows.

• Enables multi-agent collaboration for video creation without local setup
• Integrates with major LLMs including Claude, ChatGPT, and DeepSeek
• Provides 12 production pipelines and 100+ specialized tools
• Runs in the cloud with web and mobile accessibility
• Turns standard AI assistants into automated video production engines

This repository provides production-grade skills that package senior engineer workflows, quality gates, and best practices for use by AI coding agents. It maps six development phases from idea to ship into specific slash commands, ensuring agents follow consistent patterns across the lifecycle. The skills are designed to be activated automatically to guide AI through specification, implementation, testing, and deployment.

• Encodes senior engineer best practices into reusable skills for AI agents.
• Maps six lifecycle phases (Spec through Ship) to specific slash commands.
• Ensures consistent AI behavior across development, testing, and QA phases.
• Provides a structured workflow rather than ad-hoc coding assistance.

New releases of pg_clickhouse and the chdb extension enhance cross-database compatibility through updates to their underlying C libraries. A key change involves strict validation of character encodings on text and JSON columns, raising exceptions for invalid byte sequences. These updates reflect ongoing efforts to align PostgreSQL and ClickHouse data handling more closely.

• pg_clickhouse v0.11.0 and chdb v0.1.2 are now available on GitHub and PGXN.
• Text and JSON columns now raise exceptions for invalid encoding bytes.
• Updates derive from header-only C libraries: clickhouse-c and pg-clickhouse-c.
• Focus remains on improving cross-database compatibility between PostgreSQL and ClickHouse.