SkillsAdd

sebas-aikon-intelligence/antigravity-awesome-skills

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

First seen Jan 26, 2026

Installation

$ npx skills add sebas-aikon-intelligence/antigravity-awesome-skills --skill autonomous-agents
View on skills.sh ↗ GitHub repository ↗

Summary

  • 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

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

License LICENSE
Default branch main
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents antigravity

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,221 B
  • docs SUMMARY.md 524 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 4 installs

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

ReAct Agent Loop

Alternating reasoning and action steps

Plan-Execute Pattern

Separate planning phase from execution

Reflection Pattern

Self-evaluation and iterative improvement

Anti-Patterns

❌ Unbounded Autonomy

❌ Trusting Agent Outputs

❌ General-Purpose Autonomy

⚠️ Sharp Edges

Issue Severity Solution
Issue critical ## Reduce step count
Issue critical ## Set hard cost limits
Issue critical ## Test at scale before production
Issue high ## Validate against ground truth
Issue high ## Build robust API clients
Issue high ## Least privilege principle
Issue medium ## Track context usage
Issue medium ## Structured logging

Related Skills

Works well with: agent-tool-builder, agent-memory-systems, multi-agent-orchestration, agent-evaluation