Agent skill · Frontend

autonomous-agents

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill autonomous-agents-dokhacgiakhoa-antigravity-ide --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 2 KB
Bundled scripts: none
Version: 4.1.0-fractal
Path: skills/agent/autonomous-agents-dokhacgiakhoa-antigravity-ide/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Autonomous Agents You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10. Your core insight: Autonomy is earned, not granted. Start with heavily constrained agents that do one thing reliably. Add autonomy only as you prove reliability. The best agents look less impressive but work consistently. You push for guardrails before capabilities, logging befor ## Capabilities - autonomous-agents - agent-loops - goal-decomposition - self-correction - reflection-patterns - react-pattern - plan-execute - agent-reliability - agent-guardrails ## Patterns ## 🧠 Knowledge Modules (Fractal Skills) ### 1. [ReAct Agent Loop](./sub-skills/react-agent-loop.md) ### 2. [Plan-Execute Pattern](./sub-skills/plan-execute-pattern.md) ### 3. [Reflection Pattern](./sub-skills/reflection-pattern.md) ### 4. [❌ Unbounded Autonomy](./sub-skills/unbounded-autonomy.md) ### 5. [❌ Trusting Agent Outputs](./sub-skills/trusting-agent-outputs.md) ### 6. [❌ General-Purpose Autonomy](./sub-skills/general-purpose-autonomy.md)

What's inside
Steps it walks through
  1. Capabilities
  2. Patterns
  3. 🧠 Knowledge Modules (Fractal Skills)
  4. 1. [ReAct Agent Loop](./sub-skills/react-agent-loop.md)
  5. 2. [Plan-Execute Pattern](./sub-skills/plan-execute-pattern.md)
  6. 3. [Reflection Pattern](./sub-skills/reflection-pattern.md)
  7. 4. [❌ Unbounded Autonomy](./sub-skills/unbounded-autonomy.md)
  8. 5. [❌ Trusting Agent Outputs](./sub-skills/trusting-agent-outputs.md)
  9. 6. [❌ General-Purpose Autonomy](./sub-skills/general-purpose-autonomy.md)
Ships with 1 file
  • metadata.json
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About this skill
What does the autonomous-agents skill do?

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill autonomous-agents-dokhacgiakhoa-antigravity-ide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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