Autonomous Agent Patterns
Skill ActiveDesign patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.
To provide developers with robust design patterns and implementations for creating sophisticated autonomous AI coding agents.
Features
- Agent loop and architecture patterns
- Tool design with schema and execution
- Permission and safety system patterns
- Browser automation and visual agent patterns
- Context management and checkpointing strategies
Use Cases
- Building new autonomous AI agents
- Designing robust tool APIs for LLMs
- Implementing permission and security systems for agents
- Creating browser automation workflows for AI
Non-Goals
- Providing a fully runnable agent out-of-the-box
- Specific LLM model integrations beyond conceptual examples
- Detailed deployment or scaling strategies
Practices
- Agent Design
- Safety
- Tool Design
- LLM Integration
Prerequisites
- Python 3.8+
- Access to an LLM API (conceptual)
Trust
- warning:Issues AttentionIn the last 90 days, 17 issues were opened and 4 were closed, indicating a closure rate of approximately 23.5%, which is below the preferred 50% threshold.
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
Trust Signals
Similar Extensions
Du Dum: Batch Then Act Pattern
96Separate expensive observation from cheap decision-making in autonomous agent loops using a two-clock architecture. A fast clock accumulates data into a digest file; a slow clock reads the digest and acts only when something is pending. Idle cycles cost nothing because the action clock returns immediately after reading an empty digest. Use when building autonomous agents that must observe continuously but can only afford to act occasionally, when API or LLM costs dominate and most cycles have nothing to do, when designing cron-based agent architectures with observation and action phases, or when an existing heartbeat loop is too expensive because it calls the LLM on every tick.
Cli Creator
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Context Mode Ops
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Fixflow
100Execute coding tasks with a strict delivery workflow: build a full plan, implement one step at a time, run tests continuously, and commit by default after each step (`per_step`). Support explicit commit policy overrides (`final_only`, `milestone`) and optional BDD (Given/When/Then) when users ask for behavior-driven delivery or requirements are unclear.
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Spec to Repo
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