context-engineering

name	Context Engineering
description	This skill should be used when designing context management, implementing tiered fidelity, reducing token waste, applying Four Laws patterns, creating "NOT PASSED" sections, optimizing agent context, or debugging context-related issues. Provides SOTA patterns for context-efficient multi-agent systems achieving 60-80% token reduction.

Context Engineering

Overview

State-of-the-art patterns for managing context in LLM agent systems. These patterns enable complex multi-agent workflows while minimizing token overhead through strategic context engineering.

The Four Laws of Context Management

Law	Principle	Token Impact
1. Selective Projection	Pass only fields each agent needs	-30-50%
2. Tiered Fidelity	Define explicit context tiers per role	-40-60%
3. Reference vs Embedding	Use references for large data	-50-80%
4. Lazy Loading	Load data on-demand, not upfront	-30-50%

For detailed explanations and examples, see references/four-laws.md.

Context Tiers

Tier	Description	Use Case	Typical Size
FULL	Complete data	Initial analysis	5-20K tokens
SELECTIVE	Relevant subset	Domain workers	1-5K tokens
FILTERED	Criteria-matched	Validators	500-2K tokens
MINIMAL	Mode + counts	Routing	100-500 tokens
METADATA	Stats only	Synthesis	50-200 tokens

For tier selection guidance, see references/context-tiers.md.

Quick Reference: Input Section Pattern

Before (Anti-pattern)

## Input
You receive:
- snapshot: Full context snapshot
- all_findings: Complete list
- full_config: Everything

After (SOTA Pattern)

## Input
You receive (SELECTIVE context):
- analysis_summary: Key findings only
- relevant_files: Files for this focus area
- mode: Analysis depth setting

**NOT provided** (context isolation):
- Full plugin contents
- Unrelated analysis results
- Other agents' intermediate work

Anti-Patterns to Avoid

Anti-Pattern	Problem	Fix
Snapshot Broadcasting	Same data to every agent	Tier by role
Defensive Inclusion	"Maybe they need this"	Document NOT PASSED
Grounding Everything	Validating low-priority	Severity batching
Large Embeddings	Full arrays when counts suffice	Reference pattern
Repeated Context	Same data multiple times in chain	Pass once, reference later

Handoff Protocol

Standard handoff between agents:

handoff:
  from_agent: coordinator
  to_agent: analyzer
  context_level: SELECTIVE

  payload:
    mode: deep
    analysis_summary:
      claim_count: 15
      high_risk_count: 4
    relevant_files:
      - file: "[path]"
        content: "[content]"

  not_passed:
    - full_snapshot
    - unrelated_files
    - other_agents_data

  expected_output:
    format: yaml
    schema: AnalysisOutput

For complete handoff patterns, see references/handoff-protocols.md.

Severity-Based Batching

Reduce validation operations by priority:

batching:
  HIGH:     [all_validators]    # 4 agents
  MEDIUM:   [checker, estimator] # 2 agents
  LOW:      [checker]            # 1 agent
  INFO:     []                   # Skip

# Result: 60-70% fewer validation operations

Metrics to Track

Metric	Target	Calculation
Tier Compliance	100%	Agents with tier / Total agents
Redundancy Ratio	< 0.1	Duplicate data / Total data
Context per Agent	< 2K	Avg tokens per agent
NOT PASSED Coverage	100%	Agents with exclusions / Total

Additional Resources

• references/four-laws.md - Detailed law explanations with examples
• references/context-tiers.md - Tier definitions and selection guide
• references/handoff-protocols.md - YAML schema patterns
• references/examples.md - Production examples from red-agent