Use when deciding whether to launch an agent, selecting which agent to use, or coordinating multiple agents. Covers delegation criteria, external-model patterns, task isolation, and agent selection strategies.
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Use when deciding whether to launch an agent, selecting which agent to use, or coordinating multiple agents. Covers delegation criteria, external-model patterns, task isolation, and agent selection strategies.
Iron Law: "NO AGENT LAUNCH WITHOUT CLEAR DELEGATION CRITERIA"
When to Use
Use this skill when:
Considering launching an agent with the Task tool
Evaluating whether a task requires agent delegation
Selecting between different agent types or external models
Coordinating multiple agents in a workflow
Implementing external-model for external model delegation
Debugging agent coordination failures
This skill prevents premature agent launches, redundant agent usage, and poor task isolation that wastes thinking budget and causes coordination failures.
Red Flags (Violation Indicators)
Agent for single grep - Launching agent to run one grep/glob command (trivial-task anti-pattern)
Missing external-model model - Using external-model without explicit model name specification
No task isolation - Agent task description lacks independent context or success criteria
No success criteria - Task description doesn't define what "done" looks like
Default thinking pattern - Not considering whether task needs deep thinking vs. fast execution
Multiple agents without coordination - Launching 2+ agents without clear result routing plan
Result not used - Launching agent but not routing/validating its output
Agent for trivial decision - Using agent to make decision you could make directly
No tool exhaustion check - Launching agent before trying native tools first
Missing timeout consideration - Not evaluating if task needs extended thinking time
No error handling plan - Not defining what happens if agent fails or returns partial results
- Not identifying what specific expertise the agent provides
Skill gap unclear
Key Concepts
1. Agent vs. Native Tools Decision Tree
Does the task require:
โโ Single tool call (grep, read, edit)?
โ โโ โ NO AGENT - Use native tool directly
โโ 2-3 sequential tool calls?
โ โโ โ NO AGENT - Use tools directly in sequence
โโ Multi-step investigation with branching logic?
โ โโ โ AGENT - Task tool with developer/architect agent
โโ External model expertise (Grok, DeepSeek, etc.)?
โ โโ โ AGENT - external-model pattern with model specification
โโ Parallel exploration of multiple code paths?
โ โโ โ AGENT - Multiple Task calls with coordination
โโ High-risk change needing isolation?
โโ โ AGENT - Task tool with sandbox/review focus
2. Task Isolation Requirements
Every agent task must be independently executable:
Bad (not isolated):
Task: "Fix the bug we discussed earlier"
Good (properly isolated):
Task: "Debug the TypeError in src/components/UserProfile.tsx line 42.
Context: User reports 'Cannot read property name of undefined' when viewing profile page.
Evidence: Error occurs after recent commit abc123 that changed user data structure.
Success criteria: Identify root cause, propose fix, verify with test scenario."
3. External Model Pattern
When delegating to external models via claudish CLI:
claudish --model x-ai/grok-code-fast-1 --stdin --quiet <<EOF > analysis.md
Analyze the React component rendering performance issue in Dashboard.tsx.
Context:
- File: src/components/Dashboard.tsx (247 lines)
- Issue: Component re-renders 40+ times on data updates
- Recent changes: Added real-time WebSocket updates in commit f4a2c1b
Success Criteria:
- Identify unnecessary re-renders (provide line numbers)
- Propose memoization strategy
- Estimate performance improvement
Constraints:
- Max 3 minutes analysis time
- Focus on React 19 compiler-friendly patterns
EOF
When to Use Agents
Multi-Step Investigation
Trigger: Task requires 5+ tool calls with conditional branching
Agent: developer, architect
Example: "Trace data flow through 3 layers to find where user.email becomes null"
External Model Expertise
Trigger: Need specialized model capabilities (code speed, vision, reasoning)
Agent: external-model with specific model
Example: "Use Grok Code Fast to refactor 15 files for consistency in < 2 minutes"
Parallel Work
Trigger: Multiple independent tasks that can run simultaneously
Agent: Multiple Task calls with result aggregation
Example: "Analyze frontend performance (Task 1) while auditing API security (Task 2)"
Risk Isolation
Trigger: High-risk changes needing review before merging to main workflow
Agent: review-focused agent with checkpoint
Example: "Evaluate if this database migration will cause downtime"
Skill Gaps
Trigger: Current agent lacks specific skill that another agent has
Agent: specialist agent (security, performance, accessibility)
Example: "Launch accessibility agent to audit ARIA compliance"
When NOT to Use Agents
Single Grep/Glob
Instead: Use native Grep or Glob tool directly
# โ DON'T
Task: "Find all files using the deprecated API"
# โ DO
Grep("oldApiCall", output_mode: "files_with_matches", type: "js")
Simple Tool Execution
Instead: Use tool directly
# โ DON'T
Task: "Read the config file and tell me the API URL"
# โ DO
Read("/path/to/config.json")
// Parse and extract apiUrl field
Decision Already Made
Instead: Execute the decision
# โ DON'T
Task: "I think we should use React Query. What do you think?"
# โ DO
// Just implement React Query since decision is made
Write("src/hooks/useApiQuery.ts", reactQueryCode)
Sequential Tool Calls
Instead: Chain tools directly
# โ DON'T
Task: "Find the function, read it, and edit it"
# โ DO
Grep("functionName", output_mode: "files_with_matches")
// => result: src/utils/helper.ts
Read("src/utils/helper.ts")
Edit("src/utils/helper.ts", old_string, new_string)
Nuanced Context Required
Instead: Handle in current agent
# โ DON'T
Task: "Based on our earlier discussion about performance vs. maintainability trade-offs, decide if we should cache this"
# โ DO
// Current agent already has context, make decision directly
if (performanceIsCritical) {
implementCaching()
}
Output format - JSON, markdown, code snippet, report
Completeness - What must be covered
Quality bar - Minimum acceptable quality
Constraints - Time, token, tool limits
Example:
Success Criteria:
- Output: JSON array of {file, line, issue, suggestion}
- Completeness: All React components in src/ analyzed
- Quality: Each suggestion must include before/after code
- Constraints: Complete within 5 minutes, use only Read/Grep tools
4. Result Routing
Pattern:
1. Launch agent with external-model
2. Capture result in variable or file
3. Validate result against success criteria
4. Route to next step:
- If success: Use result in main workflow
- If partial: Request clarification
- If failure: Fall back to native tools
Example:
result = Task("external-model: x-ai/grok-code-fast-1\n\nRefactor 10 components for React 19...")
if (result.contains("Refactored successfully")) {
// Apply changes to codebase
applyRefactorings(result.changes)
} else {
// Fall back to manual refactoring
manualRefactor()
}
Task Isolation Checklist
Before launching an agent, verify:
Independent understanding - Task description is self-contained (no "as discussed", "the bug we saw")
Success criteria defined - Clear definition of what "done" looks like
Dependencies listed - All required files, services, credentials specified
Result format specified - Expected output structure (JSON, markdown, code, report)
Error handling clear - What happens if agent fails or returns partial results
Timeout reasonable - Time limit matches task complexity
Tool attempts exhausted - Tried native tools first, agent is not premature
Model selection justified - Chosen model matches task requirements (speed vs. reasoning)
Examples
Example 1: Bad Agent Usage (Python)
# โ VIOLATION: Agent for single grep
Task: "Find all files importing the old database client"# โ CORRECT: Use native tool
Grep("from old_db_client import", type: "py", output_mode: "files_with_matches")
Example 2: Good Agent Usage (TypeScript)
// โ CORRECT: Multi-step investigation with agentTask: "Debug the race condition in WebSocket message handling.
Context:
- File: src/services/websocket.ts (342 lines)
- Issue: Messages arrive out of order 5% of the time
- Environment: Production only (not reproducible in dev)
- Recent changes: Added message batching in commit a3f9c21
Success Criteria:
- Identify race condition root cause (provide line numbers)
- Propose synchronization strategy
- Verify solution handles edge cases
Constraints:
- Max 10 minutes analysis
- Use Read, Grep, and Bash tools only
- No code changes (diagnosis only)"
Example 3: external-model with External Model (Go)
// โ CORRECT: Fast refactoring with Grok
external-model: x-ai/grok-code-fast-1
Refactor 15 handler functions in handlers/ to use consistent error handling pattern.
Context:
- Directory: internal/handlers/ (15 files, ~200 lines each)
- Current state: Inconsistent error responses (some use Error(), some use Errorf(), some return raw errors)
- Target pattern: Use custom AppError type with status codes and messages
Success Criteria:
- All 15 handlers use AppError consistently
- Preserve existing business logic (only change error handling)
- Provide git diff summary
Constraints:
- Complete within 3 minutes
- Use Read and Grep tools for analysis
- Return refactored code for all 15 files
Integration with Other Skills
Works with:
verification-before-completion - Validate agent results before marking tasks complete
systematic-debugging - Use agents for multi-step debugging investigations
orchestration skills - Multi-agent coordination patterns from orchestration plugin
Prevents:
Premature agent launches - Check delegation criteria first
Agent thrashing - Avoid launching agents that just launch more agents
Budget waste - Don't use slow models for fast tasks or vice versa
Anti-Patterns Table
Anti-Pattern
โ Without Discipline
โ With Discipline
Trivial task delegation
Launch agent to run single grep
Use Grep tool directly
Missing isolation
"Fix the bug we discussed"
"Debug TypeError in UserProfile.tsx line 42: 'Cannot read property name of undefined'. Context: ..."
Before Task tool call, check if task description includes success criteria
Before external-model, verify model name is explicitly specified
Before agent launch, confirm native tools were attempted first
After agent completes, verify result is validated before use
Correction:
If missing success criteria โ Add "Success Criteria:" section to task description
If trivial task โ Cancel agent launch, use native tool
If wrong model โ Reconsider model selection based on task requirements
If result unused โ Add validation and routing logic
Validation:
Agent Task Checklist (all must be true):
โ Task requires 5+ tool calls OR external model expertise
โ Success criteria defined (output format, completeness, quality bar)
โ Context is self-contained (no references to earlier discussion)
โ Model selection justified (speed vs. reasoning trade-off considered)
โ Result routing planned (validation + next steps)
โ Error handling defined (fallback if agent fails)
โ Native tools attempted first (or explicitly not applicable)
