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fan-out

elliotjlt/fan-out

Planning

About

SKILL.md

fan-out

elliotjlt/fan-out

Planning

About

Orchestration pattern for parallelizable tasks...

SKILL.md

Fan-Out

Some tasks are embarrassingly parallel - researching 5 APIs, analyzing 10 files, testing 3 approaches. Running them sequentially wastes time. This elixir orchestrates parallel execution: decompose work, fan out to agents, collect and synthesize results.

When To Activate

Trigger when:

Task involves analyzing multiple independent items
Research spans several unrelated topics
Need to explore multiple approaches simultaneously
Batch processing with no inter-item dependencies
User says "check all of these" or "investigate each"

Do NOT trigger for:

Sequential tasks where B depends on A
Single-item deep analysis
Tasks requiring context accumulation across items
When parallel execution would overwhelm system resources

The Pattern

                    ┌──→ [Agent 1] ──→ Result 1 ──┐
                    │                              │
[Task] → Decompose ─┼──→ [Agent 2] ──→ Result 2 ──┼──→ Aggregate → [Final]
                    │                              │
                    └──→ [Agent 3] ──→ Result 3 ──┘

Instructions

Step 1: Validate Parallelizability

Confirm the task is fan-out appropriate:

Subtask independence check:
- Can subtask B complete without subtask A's result? [YES/NO]
- Do subtasks share mutable state? [YES/NO - should be NO]
- Is order of completion irrelevant? [YES/NO - should be YES]

If any check fails → Use 'pipeline' pattern instead

Step 2: Decompose the Task

Break work into discrete, independent units:

Original task: [Description]

Subtasks:
1. [Subtask 1] - Agent type: [explore/research/analyze]
2. [Subtask 2] - Agent type: [explore/research/analyze]
3. [Subtask 3] - Agent type: [explore/research/analyze]
...

Expected outputs:
- Subtask 1 → [What result looks like]
- Subtask 2 → [What result looks like]

Step 3: Spawn Parallel Agents

Launch agents simultaneously:

Spawning [N] parallel agents:

Agent 1: [Subtask description]
Agent 2: [Subtask description]
Agent 3: [Subtask description]

[Wait for all to complete]

Use Task tool with appropriate subagent_type for each.

Step 4: Collect Results

Gather outputs from all agents:

Results received:
- Agent 1: [Summary]
- Agent 2: [Summary]
- Agent 3: [Summary]

Step 5: Synthesize

Aggregate results into coherent output:

Synthesis:
- [Combined insight 1]
- [Combined insight 2]
- [Patterns across results]
- [Conflicts or contradictions]

Step 6: Report

Present unified findings:

## Fan-Out Results: [Task]

### Summary
[High-level findings]

### Details by Subtask
1. [Subtask 1 findings]
2. [Subtask 2 findings]
...

### Cross-Cutting Insights
[Patterns that emerged across subtasks]

### Recommendations
[Actionable next steps]

Resource Management

**Agent limits:** - Recommended max: 5 parallel agents - Hard limit: Based on system capacity

When to limit parallelism:

Complex subtasks → fewer agents (3 max)
Simple lookups → more agents (5+)
Resource-intensive work → sequential fallback

Failure handling:

If agent fails: Log error, continue with others
If >50% fail: Abort and report
Partial results: Clearly mark incomplete

NEVER

Fan out dependent tasks (use pipeline instead)
Spawn more agents than subtasks warrant
Ignore failed agents - always report what succeeded/failed
Assume all results are equally valid without synthesis
Fan out trivial tasks (overhead not worth it)

ALWAYS

Verify independence before fanning out
Define clear, specific prompts for each agent
Set expectations for what each agent should return
Synthesize results, don't just concatenate
Report which subtasks succeeded/failed

Examples

Example 1: Multi-File Analysis

User: "Check all our API endpoints for authentication issues"

Subtask independence check:
- Can each file be analyzed independently? YES
- Shared state? NO
- Order irrelevant? YES

→ Fan-out appropriate

Decomposing:
1. Analyze src/api/users.ts for auth issues
2. Analyze src/api/orders.ts for auth issues
3. Analyze src/api/products.ts for auth issues
4. Analyze src/api/admin.ts for auth issues

[Spawns 4 parallel explore agents]

Results:
- users.ts: Missing rate limiting on login endpoint
- orders.ts: No auth check on order history
- products.ts: Clean
- admin.ts: Using deprecated auth method

Synthesis:
3 of 4 files have auth issues. Most critical: orders.ts
exposes user data without authentication.

Example 2: Research Multiple Technologies

User: "Compare Redis, Memcached, and DynamoDB for our caching layer"

Subtask independence check:
- Independent research? YES
- Shared state? NO
- Order irrelevant? YES

→ Fan-out appropriate

Decomposing:
1. Research Redis: features, performance, pricing
2. Research Memcached: features, performance, pricing
3. Research DynamoDB: features, performance, pricing

[Spawns 3 parallel research agents]

Results synthesized into comparison table with
cross-cutting analysis of trade-offs.

Example 3: Not Appropriate for Fan-Out

User: "Build a feature: first design it, then implement, then test"

Subtask independence check:
- Can implementation happen without design? NO
- Can tests run without implementation? NO

→ NOT fan-out appropriate
→ Use 'pipeline' pattern instead

What DOESN'T work: - Fanning out dependent tasks: Results are incoherent, agents make conflicting assumptions - Too many agents: System overwhelmed, diminishing returns - Vague agent prompts: Results are too varied to synthesize - No synthesis step: User gets disconnected bullet points, not insight

Why This Elixir Exists

Sequential processing of independent tasks wastes time. A human would delegate parallel work to multiple people. This elixir gives Claude the same capability: decompose, distribute, synthesize.

The key insight: parallelism only helps when tasks are truly independent. Otherwise, you get chaos, not speed.

About

SKILL.md

About

Orchestration pattern for parallelizable tasks...

SKILL.md

Fan-Out

When To Activate

Trigger when:

Task involves analyzing multiple independent items
Research spans several unrelated topics
Need to explore multiple approaches simultaneously
Batch processing with no inter-item dependencies
User says "check all of these" or "investigate each"

Do NOT trigger for:

Sequential tasks where B depends on A
Single-item deep analysis
Tasks requiring context accumulation across items
When parallel execution would overwhelm system resources

The Pattern

                    ┌──→ [Agent 1] ──→ Result 1 ──┐
                    │                              │
[Task] → Decompose ─┼──→ [Agent 2] ──→ Result 2 ──┼──→ Aggregate → [Final]
                    │                              │
                    └──→ [Agent 3] ──→ Result 3 ──┘

Instructions

Step 1: Validate Parallelizability

Confirm the task is fan-out appropriate:

Subtask independence check:
- Can subtask B complete without subtask A's result? [YES/NO]
- Do subtasks share mutable state? [YES/NO - should be NO]
- Is order of completion irrelevant? [YES/NO - should be YES]

If any check fails → Use 'pipeline' pattern instead

Step 2: Decompose the Task

Break work into discrete, independent units:

Original task: [Description]

Subtasks:
1. [Subtask 1] - Agent type: [explore/research/analyze]
2. [Subtask 2] - Agent type: [explore/research/analyze]
3. [Subtask 3] - Agent type: [explore/research/analyze]
...

Expected outputs:
- Subtask 1 → [What result looks like]
- Subtask 2 → [What result looks like]

Step 3: Spawn Parallel Agents

Launch agents simultaneously:

Spawning [N] parallel agents:

Agent 1: [Subtask description]
Agent 2: [Subtask description]
Agent 3: [Subtask description]

[Wait for all to complete]

Use Task tool with appropriate subagent_type for each.

Step 4: Collect Results

Gather outputs from all agents:

Results received:
- Agent 1: [Summary]
- Agent 2: [Summary]
- Agent 3: [Summary]

Step 5: Synthesize

Aggregate results into coherent output:

Synthesis:
- [Combined insight 1]
- [Combined insight 2]
- [Patterns across results]
- [Conflicts or contradictions]

Step 6: Report

Present unified findings:

## Fan-Out Results: [Task]

### Summary
[High-level findings]

### Details by Subtask
1. [Subtask 1 findings]
2. [Subtask 2 findings]
...

### Cross-Cutting Insights
[Patterns that emerged across subtasks]

### Recommendations
[Actionable next steps]

Resource Management

**Agent limits:** - Recommended max: 5 parallel agents - Hard limit: Based on system capacity

When to limit parallelism:

Complex subtasks → fewer agents (3 max)
Simple lookups → more agents (5+)
Resource-intensive work → sequential fallback

Failure handling:

If agent fails: Log error, continue with others
If >50% fail: Abort and report
Partial results: Clearly mark incomplete

NEVER

Fan out dependent tasks (use pipeline instead)
Spawn more agents than subtasks warrant
Ignore failed agents - always report what succeeded/failed
Assume all results are equally valid without synthesis
Fan out trivial tasks (overhead not worth it)

ALWAYS

Verify independence before fanning out
Define clear, specific prompts for each agent
Set expectations for what each agent should return
Synthesize results, don't just concatenate
Report which subtasks succeeded/failed

Examples

Example 1: Multi-File Analysis

User: "Check all our API endpoints for authentication issues"

Subtask independence check:
- Can each file be analyzed independently? YES
- Shared state? NO
- Order irrelevant? YES

→ Fan-out appropriate

Decomposing:
1. Analyze src/api/users.ts for auth issues
2. Analyze src/api/orders.ts for auth issues
3. Analyze src/api/products.ts for auth issues
4. Analyze src/api/admin.ts for auth issues

[Spawns 4 parallel explore agents]

Results:
- users.ts: Missing rate limiting on login endpoint
- orders.ts: No auth check on order history
- products.ts: Clean
- admin.ts: Using deprecated auth method

Synthesis:
3 of 4 files have auth issues. Most critical: orders.ts
exposes user data without authentication.

Example 2: Research Multiple Technologies

User: "Compare Redis, Memcached, and DynamoDB for our caching layer"

Subtask independence check:
- Independent research? YES
- Shared state? NO
- Order irrelevant? YES

→ Fan-out appropriate

Decomposing:
1. Research Redis: features, performance, pricing
2. Research Memcached: features, performance, pricing
3. Research DynamoDB: features, performance, pricing

[Spawns 3 parallel research agents]

Results synthesized into comparison table with
cross-cutting analysis of trade-offs.

Example 3: Not Appropriate for Fan-Out

User: "Build a feature: first design it, then implement, then test"

Subtask independence check:
- Can implementation happen without design? NO
- Can tests run without implementation? NO

→ NOT fan-out appropriate
→ Use 'pipeline' pattern instead

Why This Elixir Exists

Sequential processing of independent tasks wastes time. A human would delegate parallel work to multiple people. This elixir gives Claude the same capability: decompose, distribute, synthesize.

The key insight: parallelism only helps when tasks are truly independent. Otherwise, you get chaos, not speed.