reduce-delegate-framework（减少委托框架）

Reduce & Delegate Framework Skill

Apply the R&D framework to optimize prompts, workflows, and context management.

Purpose

There are only two ways to manage context: Reduce and Delegate. This skill helps you systematically apply both strategies to any context optimization challenge.

When to Use

• Context window approaching limits
• Agent performance degrading over conversation
• Prompts growing unwieldy
• Workflows consuming too many tokens
• Need to scale agent work

The R&D Analysis Process

Step 1: Identify the Context Problem

Categorize the issue:

Step 2: Apply Reduce Strategies

For each context element, ask:

1. Is this necessary for the current task?
2. Can this be loaded on-demand instead?
3. Is this information stale or outdated?
4. Does this contradict other context?

Reduction techniques:

Step 3: Apply Delegate Strategies

For complex or parallel work, ask:

1. Does this subtask need different context?
2. Can this run independently?
3. Would a specialized agent perform better?
4. Is there parallel work opportunity?

Delegation techniques:

Optimization Workflow

1. Measure current context state
   - Use /context command
   - Check token consumption

2. Analyze composition
   - What's consuming most tokens?
   - What's unnecessary?

3. Apply Reduce
   - Remove unnecessary context
   - Start fresh if needed
   - Control output verbosity

4. Apply Delegate
   - Offload subtasks
   - Use specialized agents
   - Enable parallel work

5. Verify improvement
   - Measure new state
   - Compare performance

Common Optimization Patterns

Pattern: Bloated Memory File

Before:

# CLAUDE.md (5KB+)
Contains: everything about the project

After (Reduce):

# CLAUDE.md (1KB)
Contains: only universals

# .claude/commands/prime.md
Contains: task-specific context loading

Pattern: Long Conversation

Problem: Multi-turn conversation with context rot

Solution (Reduce):

1. Start fresh instance
2. Use priming command to load current state
3. Continue with clean context

Pattern: Complex Research Task

Before:

Primary agent does research -> context polluted
Primary agent implements -> struggles with focus

After (Delegate):

Primary agent delegates research -> sub-agent
Sub-agent returns summary -> primary continues
Primary agent implements -> clean context

Pattern: Parallel Independent Tasks

Before:

Task A -> Task B -> Task C (sequential, context accumulates)

After (Delegate):

Task A (agent 1) \
Task B (agent 2)  -> Aggregate results
Task C (agent 3) /

Output Format

When optimizing, report:

{
  "analysis": {
    "current_state": "Context at 80% capacity",
    "primary_issue": "Long conversation with accumulated history",
    "secondary_issues": ["Verbose tool outputs", "Unused MCP servers"]
  },
  "reduce_recommendations": [
    {
      "action": "Start fresh instance",
      "impact": "Reset accumulated history",
      "effort": "Low"
    },
    {
      "action": "Apply concise output style",
      "impact": "50% reduction in output tokens",
      "effort": "Low"
    }
  ],
  "delegate_recommendations": [
    {
      "action": "Create research sub-agent",
      "impact": "Isolate research context",
      "effort": "Medium"
    }
  ],
  "expected_improvement": "40-60% context reduction"
}

Decision Matrix

When to Reduce vs Delegate:

Key Quote

"There are only two ways to manage your context window: Reduce and Delegate. Every technique fits into one or both of these buckets."

Cross-References

• @rd-framework.md - Framework reference
• @context-audit skill - Audit before optimizing
• @context-layers.md - Understanding what to optimize
• @context-rot-vs-pollution.md - Diagnosing the problem

Version History

• v1.0.0 (2025-12-26): Initial release

Last Updated

Date: 2025-12-26 Model: claude-opus-4-5-20251101