task-delegation
Project-agnostic skill for delegating execution work to subagents with skill activation, parallel execution, and quality verification.
When & Why to Use This Skill
The Task Delegation skill transforms Claude into a powerful project orchestrator capable of managing complex workflows by delegating execution to specialized subagents. It optimizes productivity through parallel execution, automated skill activation via keyword triggers, and a strategic model assignment system (Haiku, Sonnet, Opus) that balances high-quality output with cost efficiency. By implementing a 'Plan-Delegate-Review' pattern, it ensures rigorous quality verification and architectural consistency across multi-step projects.
Use Cases
- Parallel Research and Analysis: Launching multiple subagents simultaneously to gather data on different topics, significantly reducing the time required for comprehensive market or academic research.
- Multi-Stage Content Production: Orchestrating a pipeline where different models handle specific steps—such as Haiku for research compilation, Sonnet for structured drafting, and Opus for deep analytical critique.
- Large-Scale Technical Migrations: Breaking down repetitive mechanical tasks like code formatting, documentation updates, or file migrations into independent subtasks for parallel subagent execution.
- Automated Quality Assurance: Using subagents to perform rigorous verification against acceptance criteria and skill-specific checklists before final work is committed to the main project.
- Cost-Effective Task Scaling: Optimizing operational costs by automatically assigning simpler mechanical work to smaller models while reserving advanced reasoning models for complex synthesis and architectural decisions.
| name | task-delegation |
|---|---|
| description | Project-agnostic skill for delegating execution work to subagents with skill activation, parallel execution, and quality verification. |
Task Delegation Skill
Delegation Pattern
Plan → Create Task → Design Approach → Delegate → Review → Commit
Main Thread (You): Planning, architecture, coordination, review, user communication Subagents: Execution of well-defined work items with clear outputs
When to Delegate
✅ Always delegate:
- Execution work taking >5 minutes of focused effort
- Writing, research, analysis with defined scope
- Work that can run in parallel with other tasks
- Repetitive mechanical tasks (formatting, compilation, migration)
- Well-defined scope with clear output format
✅ Benefits:
- Parallel execution across independent tasks
- Better planning with full project context
- Focused execution produces higher quality
- Cost optimization through model assignment
When NOT to Delegate
❌ Never delegate:
- Task creation or search (use MCP tools directly in main thread)
- Planning and architectural decisions (requires full context)
- Editorial judgment (requires project continuity)
- Answering user questions (requires conversation flow)
- Dependency modeling (requires cross-task analysis)
- Work taking <5 minutes (delegation overhead exceeds execution time)
Model Assignment Strategy
Choose model based on task complexity and cost optimization:
| Task Type | Model | Rationale |
|---|---|---|
| Simple mechanical work | Haiku | Cost-efficient; Skills compensate for smaller model |
| Research compilation | Haiku | Search and compile work; Skills enforce quality |
| Structured writing | Sonnet | Balance capability/cost; structured output |
| Technical analysis | Sonnet | Synthesis required beyond Haiku capability |
| Deep critique | Opus | Maximum analytical insight needed |
| Complex synthesis | Opus | Scholarly rigor, philosophical depth, 10K+ word expansion |
Cost Optimization Principle: Use smallest model capable of task with Skills active. Skills elevate Haiku/Sonnet to achieve quality standards previously requiring larger models.
How to Assign:
- Check task's
assigneefield in Backlog (if project uses task assignment) - Specify model explicitly when delegating:
model='haiku'ormodel='opus' - Default to Sonnet for general-purpose work if uncertain
Task Description for Skill Activation
Skills activate automatically when task description contains trigger keywords.
Pattern: [Action] + [Skill Trigger] + [Context]
Examples:
# Activates essay-research-compilation skill
"Compile comprehensive research for Essay 11 (Step 1)"
# Activates essay-first-draft skill
"Write first draft for Essay 11 based on research.md (Step 2)"
# Activates essay-critique skill
"Create comprehensive critique for Essay 11 draft (Step 3)"
Trigger Keywords by Skill Type:
- Research: "research compilation", "Step 1", "gather sources", "compile research"
- Drafting: "first draft", "Step 2", "write draft"
- Critique: "comprehensive critique", "Step 3", "analyze draft"
- Final drafts: "final draft", "Step 4", "expand to 10,000 words"
Best Practices:
- Include step number if workflow uses numbered steps
- Reference input files explicitly ("based on research.md")
- State output format ("produce draft-1.md")
- Keep description concise but keyword-rich for matching
Verifying Skill Activation
After delegating, check subagent's initial response for Skill confirmation.
Positive Confirmation:
"Using essay-research-compilation Skill to guide research compilation..."
"Activating essay-first-draft Skill for structured writing..."
If Skill Did NOT Activate:
- Check task description for missing trigger keywords
- Redelegate with clearer keywords: "research compilation (Step 1)" not just "gather data"
- Confirm Skill exists in
.claude/skills/directory - Verify Skill's trigger patterns in SKILL.md frontmatter
Why Activation Matters: Skills provide comprehensive templates, examples, and verification checklists. Without activation, subagent lacks guidance that prevents common errors.
Delegation Workflow
1. Plan in Main Thread
- Understand full scope and dependencies
- Break work into independent subtasks if possible
- Identify which subtasks can run in parallel
- Design approach and output format
2. Create Tasks (if using Backlog)
# Create parent task
task_create(title, description, acceptanceCriteria, assignee=['model-name'])
# Create subtasks with dependencies
task_create(title, description, parentTaskId='parent-id', assignee=['haiku'])
3. Delegate to Subagent
# Single delegation
Task tool with:
- Clear description (triggers Skills)
- Model specification (haiku/sonnet/opus)
- Reference to input files
- Expected output format
# Parallel delegation (independent tasks)
Multiple Task tool calls in single message:
- Task 1: "Research Essay 3 (Step 1)" → haiku
- Task 2: "Research Essay 5 (Step 1)" → haiku
- Task 3: "Research Essay 10 (Step 1)" → haiku
4. Review Output
- Check against task acceptance criteria
- Verify Skill guidelines followed (see Skill's verification checklist)
- Confirm output format matches expectations
- Test for scope creep (output should match defined scope, nothing more)
5. Commit and Document
git commit -m "[task-id] Brief description
Detailed explanation of what was completed."
Update task with commit reference:
task_edit(id='task-id', notesAppend=['Completed in commit abc123'])
Parallel Execution
Pattern: Launch multiple subagents for independent tasks in single message.
When to Use:
- Tasks have no dependencies between them
- Different essays at same workflow step
- Different sections of large document
- Independent research compilations
Example:
# Single message with 3 parallel Task tool calls:
Task 1: "Research Essay 3 Data Readiness (Step 1)" model=haiku
Task 2: "Research Essay 5 Technical Debt (Step 1)" model=haiku
Task 3: "Research Essay 10 Infrastructure (Step 1)" model=haiku
Benefits:
- Maximize throughput (3 tasks complete in parallel vs sequential)
- Efficient use of session time
- No coordination overhead (tasks are independent)
Dependency Modeling: If Task B depends on Task A output, run sequentially:
- Delegate Task A, wait for completion
- Review Task A output
- Delegate Task B with reference to Task A output
Reviewing Subagent Output
Verification Checklist:
Completeness:
- All acceptance criteria met?
- Output file exists at expected path?
- Required sections present?
Skill Adherence:
- Check against Skill's verification checklist (in SKILL.md)
- Example: essay-research-compilation requires 100% citation completeness
- Example: essay-first-draft requires minimum 5 human experience paragraphs
Format:
- Matches expected structure?
- File naming correct?
- No unexpected additions?
Scope:
- Output matches defined scope (no scope creep)?
- Subagent didn't make editorial decisions requiring main thread judgment?
If Quality Insufficient:
- Provide specific feedback referencing Skill section
- Example: "Section 2 needs causal analysis per essay-first-draft guidelines"
- Redelegate with clarification, not vague "improve this"
Handling Subagent Failures
Common Issues and Fixes:
| Issue | Root Cause | Fix |
|---|---|---|
| Skill didn't activate | Missing trigger keywords | Redelegate with "Step X" or skill name in description |
| Output missing required sections | Unclear acceptance criteria | Add specific checklist to task description |
| Scope creep | Vague task description | Define exact input files and output format |
| Wrong model used | Model not specified | Explicitly set model parameter in Task tool |
| Quality below standard | Skill verification skipped | Review against Skill checklist, return specific sections |
Escalation Pattern:
- First attempt: Redelegate with clarification
- Second attempt: Provide Skill excerpt as context
- Third attempt: Break into smaller subtasks or execute in main thread
Success Criteria
Well-delegated task produces:
- ✅ Output matching acceptance criteria exactly
- ✅ Skill guidelines followed (verified via checklist)
- ✅ No scope creep (output ≤ defined scope)
- ✅ Correct format and location
- ✅ Can commit immediately without revision
Signs of poor delegation:
- ❌ Multiple revision rounds needed
- ❌ Subagent made editorial decisions
- ❌ Output contains work outside defined scope
- ❌ Missing required sections
- ❌ Skill didn't activate (preventable with better description)
Quick Reference
Delegation Decision Tree:
Is this execution work >5 min? → NO → Do in main thread
↓ YES
Is scope well-defined? → NO → Plan first, then delegate
↓ YES
Can it run in parallel? → YES → Launch multiple subagents
↓ NO
Does it depend on other work? → YES → Wait for dependency
↓ NO
Delegate with:
- Clear description (trigger Skills)
- Model assignment (haiku/sonnet/opus)
- Input files referenced
- Output format specified
Model Selection:
- Mechanical/research → Haiku
- Structured writing → Sonnet
- Complex synthesis → Opus
- When uncertain → Sonnet (balanced default)
Parallel Pattern:
- Independent tasks → Single message, multiple Task calls
- Dependent tasks → Sequential delegation
Review Pattern:
- Check acceptance criteria
- Verify Skill checklist
- Confirm format
- Test scope