agent▌

Design AI agent tools with clear schemas, descriptions, and error handling that prevent hallucination and token waste. \n \n Focuses on JSON Schema best practices and description writing that helps LLMs understand tool intent and constraints, not just implementation details \n Covers tool validation, explicit error handling patterns, and recovery strategies that prevent silent failures and agent loops \n Includes guidance on the Model Context Protocol (MCP) standard for tool interoperability acr

Persistent, searchable memory bank for AI agents with automatic project documentation sync. \n \n Provides four core MCP tools: memory_search for querying by text/type/tags, memory_write for recording knowledge and decisions, memory_read for retrieving specific entries, and memory_stats for usage analytics \n Organizes memories by type (architecture, patterns, decisions) and supports custom tagging for flexible retrieval and organization \n Runs as an MCP server that syncs with your project work

Manage Twill Cloud Coding Agent workflows through the public v1 API. \n \n Create, list, retrieve, and manage tasks with support for custom branches, agent selection, and file attachments \n Stream job logs in real time via Server-Sent Events and cancel running jobs at any point \n Full lifecycle control: send follow-up messages, approve plans, archive tasks, and manage task state \n Schedule recurring coding tasks with cron expressions, timezone support, and pause/resume capabilities \n List re

Skill by ara.so — Daily 2026 Skills collection.

Manage multiple local CLI agents in parallel tmux sessions with task assignment and monitoring. \n \n Start, stop, and monitor agents running in isolated tmux sessions with log tailing and health checks \n Assign tasks to specific agents and track their output in real time \n Schedule recurring agent work via cron integration for automated workflows \n Requires tmux and Python 3; agents configured in a local agents/ directory \n

Structured peer-review process using constrained agents to validate designs and surface hidden assumptions before implementation. \n \n Five specialized agent roles with hard scope limits: Primary Designer, Skeptic/Challenger, Constraint Guardian, User Advocate, and Integrator/Arbiter, each with explicit permissions and restrictions \n Sequential review workflow where the designer proposes, then three reviewer agents provide feedback in order, with mandatory Decision Log recording all objections

Decision framework for autonomous agent loops with quality gates, evals, and recovery patterns. \n \n Four loop modes available: continuous-pr for CI/PR workflows, rfc-dag for RFC decomposition, infinite for exploratory parallel generation, and sequential as default \n Recommended production stack combines RFC decomposition, code quality gates, eval harness, and session persistence for robust autonomous execution \n Built-in failure mode detection covers loop churn, repeated retries, merge queue

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Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex.

Automate issue-to-Draft-PR pipeline by labeling issues for GitHub Copilot assignment. \n \n Label an issue with ai-copilot to trigger GitHub Actions, which auto-assigns Copilot via GraphQL and initiates code generation, branch creation, and Draft PR opening \n Requires GitHub Copilot Pro+, Business, or Enterprise; one-time setup deploys the workflow, registers a PAT secret, and creates the label \n Copilot PRs are treated as external contributions and require manual approval before CI runs; subs