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Advanced context engineering techniques for AI agents. Token-efficient plugins improving output quality through structured reasoning, reflection loops, and multi-agent patterns.
npx skill4agent add founderjourney/claude-skills context-engineering-kit1. Generate initial response
2. Self-evaluate against criteria
3. Identify improvement areas
4. Generate refined response
5. Repeat until quality threshold met# Specification Document
## Requirements
[Clear, testable requirements]
## Acceptance Criteria
[Specific success conditions]
## Constraints
[Limitations and boundaries]
## Examples
[Input/output pairs]┌─────────────────────────┐
│ Orchestrator Agent │
├─────────────────────────┤
│ ┌─────┐ ┌─────┐ │
│ │Gen 1│ │Gen 2│ ... │
│ └─────┘ └─────┘ │
├─────────────────────────┤
│ Quality Gate Agent │
└─────────────────────────┘Observation → Hypothesis → Test → ConclusionPlan → Do → Check → Act → RepeatReview my code with reflexion:
[paste code]
Requirements:
- Error handling
- Performance
- ReadabilityCreate a spec for: User authentication system
Then implement following the spec.Review this PR with multiple perspectives:
- Security focus
- Performance focus
- Maintainability focus
[paste code or PR link]## Context
[Brief, relevant context only]
## Task
[Clear, specific task]
## Output Format
[Expected structure]Review this code for bugs, security issues, performance problems...
## Review: auth.py
### Focus Areas
1. Security (OWASP Top 10)
2. Error handling
3. SQL injection
### Output
- Issues: severity + line number
- Fixes: specific code suggestions
[code]# Pattern: [Name]
## When to Use
[Trigger conditions]
## Process
[Step-by-step]
## Example
[Concrete example]
## Metrics
[How to measure success]