autonomous-loops
Enable background self-improvement cycles for agents to evaluate and fix their own performance.
Install
mkdir -p .claude/skills/autonomous-loops-rajbharti06 && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/11810" && unzip -o skill.zip -d .claude/skills/autonomous-loops-rajbharti06 && rm skill.zipInstalls to .claude/skills/autonomous-loops-rajbharti06
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Self-improvement loop patterns — eval → learn → improve cycles running without human inputKey capabilities
- →Run evaluation suites against LLMs
- →Analyze failures to extract causes and fixes
- →Write lessons from failed cases to a lesson store
- →Update instinct confidence based on weak areas
- →Prune expired patterns with low confidence
- →Track score history and trends
How it works
The skill runs self-improvement cycles by evaluating agent performance, analyzing failures, writing lessons, and updating internal confidence scores without human intervention.
Inputs & outputs
When to use autonomous-loops
- →Run auto-evaluation cycle
- →Monitor agent improvement trends
- →Start background learning loops
About this skill
Autonomous Loops Skill
Orchestrator X can run self-improvement cycles without any human input.
How It Works
Every 10 minutes:
┌─────────────────────────────────────────────────────┐
│ 1. RUN EVAL SUITE (8 test cases against real LLM) │
│ Planning, Coding, QA, Stack Detection tests │
│ │
│ 2. ANALYZE FAILURES │
│ Extract cause + fix for each failed case │
│ │
│ 3. WRITE LESSONS │
│ Failed cases → lessonStore.addLesson() │
│ │
│ 4. UPDATE INSTINCT CONFIDENCE │
│ Weak areas → reduce confidence for related │
│ instincts (Bayesian update) │
│ │
│ 5. PRUNE EXPIRED PATTERNS │
│ Remove instincts < 0.2 confidence + 30 days old │
│ │
│ 6. TRACK SCORE HISTORY │
│ Record score, trend, lessons added │
└─────────────────────────────────────────────────────┘
Configuration
# Start with default interval (10 min)
POST /autonomous/start
# Custom interval (5 min)
POST /autonomous/start
{ "intervalMs": 300000 }
# Environment variable
ENABLE_AUTONOMOUS_LOOP=true npm start
Score Interpretation
| Score | Interpretation | Action |
|---|---|---|
| 80-100 | Excellent | Maintain current patterns |
| 65-79 | Good | Minor improvements needed |
| 50-64 | Fair | Investigate weak areas |
| < 50 | Poor | Review lessons and instincts |
Trend Detection
After 3+ eval cycles, the system detects trends:
- Improving (+3 points vs previous average) → current approach is working
- Declining (-3 points vs previous average) → lessons may be stale or conflicting
- Stable → system has reached equilibrium
Self-Improvement Mechanics
The system improves its memory (lessons + instincts), not its source code:
- Safe: no risk of breaking changes
- Measurable: track score history in dashboard
- Reversible: lessons can be manually deleted
- Transparent: all changes visible in
/lessonsand/instincts
When not to use it
- →Modifying the agent's source code
- →When human input is required for improvement cycles
- →When lessons should not be reversible
Limitations
- →Does not modify the agent's source code
- →Lessons can be manually deleted
- →All changes are visible in /lessons and /instincts
How it compares
This skill automates the entire evaluation-learn-improve cycle, focusing on memory updates rather than source code changes, which differs from manual debugging or code refactoring.
Compared to similar skills
autonomous-loops side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| autonomous-loops (this skill) | 0 | 2mo | Review | Advanced |
| opencode-cli | 14 | 7mo | Review | Advanced |
| computer-use-agents | 10 | 6mo | Review | Advanced |
| crewai-developer | 2 | 8mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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