Rules

AI coding agent instructions for project conventions and guidelines.

This file provides comprehensive guidance for AI agents working with code in this repository.

uv run fastapi dev main.py docker compose down -v atlas schema inspect -u "sqlite://file?cache=shared&mode=memory" --format "{{ sql . }}" atlas schema inspect -u "sqlite://issues.db" --format "{{ sql . }}" > migrate.sql

Read CLAUDE.md for complete project context and follow all guidelines there.

`Fear-of-Falling/`), unless the task is explicitly repo-root scoped.

3. **Ensure All Tests Pass:** Before you consider your task complete, you MUST run the full validation pipeline and ensure all tests pass. go run build.go validate **ATTENTION AI AGENT:** This document contains the official operating protocol for all AI-assisted development in the `shoal` repository. You are required to read, understand, and strictly adhere to these rules at all times. Failure to comply with these directives will result in corrective action.

This project has documented patterns and conventions in `.agent/skills/`. **Before implementing any feature, you MUST:** 1. Check which skills are relevant to your task 2. Read the `SKILL.md` file for those skills

oteam is a Python package for coordinating OpenCode agents around a shared git repo. Agents self-assign tickets, plan their work, and push changes with safety checks.

AI coding agent instructions for project conventions and guidelines.

Quick reference for AI agents performing quality assurance tasks in this monorepo.

Visionmate is a multi-modal assistant that continuously captures UVC video, screen content, or audio, streaming them to a VLM (Vision Language Model) to fuse visual and spoken context for near real-time conversational understanding.

This document defines how autonomous engineering agents should operate within the `eo-processor` repository. It covers: 1. Core mission and constraints 2. Repository awareness 4. How to add functionality (Rust + Python)

Use these skills for detailed patterns on-demand: | Skill | Description | URL | | ----------------------- | ------------------------------------------------------ | -------------------------------------------------------- |

AI coding agent instructions for project conventions and guidelines.

This guide explains how LLM-based agents should interact with **Code Executor MCP**. It focuses on the execute_code pattern, wrapper imports, and operational guardrails.

Single-module Go SDK with integration sub-modules in `github.com/getsentry/sentry-go`.

Located in `~/.claude/agents/`:

This document provides guidelines for building, testing, and contributing to the CCCL repository. It is primarily written for agentic AIs, but the information is also useful for CCCL developers.

For your AI agents data to show up in the Sentry [AI Agents Insights](https://sentry.io/orgredirect/organizations/:orgslug/insights/ai/agents/), at least one of the AI spans needs to be created and have well-defined names and data attributes. If the required data (marked with MUST or required) is missing, the data will not show up in the Agents dashbboard.

This guide shows how to take a Python app built with the OpenAI Agents SDK and add the TraceCore evaluation loop without rebuilding the app from scratch.

This guide provides a practical, hands-on introduction to the **Agent Council** and the **`@thinkies` orchestrator**. This system is designed for users who want to leverage the cognitive power of the INDRA ecosystem using natural language, without needing to write or understand the INDRA protocol itself.

Agent development guide providing build commands, code style guidelines, and development workflow instructions for the Terraphim AI project.

Guidelines for AI agents working on the lading codebase. For detailed rationale behind these rules, see the [Architecture Decision Records](docs/adr/README.md).