The Chain Builder skill helps you design and execute multi-step prompt chains where the output of one prompt becomes the input to the next. This enables complex, multi-stage processing that would be difficult to accomplish in a single prompt, while maintaining clarity, modularity, and debuggability.
This skill guides you through chain design, identifying optimal breakpoints, managing data flow between steps, handling errors, and validating outputs at each stage. It's particularly valuable for tasks that involve multiple distinct phases like research → analysis → synthesis → formatting, or ideation → planning → implementation → review.
Use this skill when you have complex tasks that benefit from sequential processing, when you need intermediate validation between steps, or when different parts of a task require different specialized prompts or tools.
Core Workflows
Workflow 1: Design Prompt Chain from Goal
Clarify
the end goal:
What's the final output?
What quality standards apply?
What constraints exist?
Decompose
into logical stages:
Identify distinct phases
Determine stage boundaries
Order by dependencies
Design
individual prompts:
Purpose of each stage
Input requirements
Output specifications
Success criteria
Define
data flow:
What passes between stages?
What format for intermediate outputs?
What state is maintained?
Add
validation:
Checkpoints after each stage
Quality gates
Error handling
Document
the chain:
Chain purpose
Stage descriptions
Data flow diagram
Usage examples
Test
end-to-end
Workflow 2: Execute Existing Chain
Load
chain definition:
Read chain specification
Understand stages
Prepare inputs
Initialize
chain state:
Set initial inputs
Prepare storage for outputs
Initialize tracking
Execute
each stage sequentially:
Run stage prompt
Validate output
If validation fails
Handle error
If validation passes
Continue
Store intermediate result
Pass output to next stage
Monitor
progress:
Track current stage
Log outputs
Report status
Validate
final output
Report
results and any issues
Workflow 3: Debug Chain Failure
Identify
failure point:
Which stage failed?
What was the input to that stage?
What error occurred?
Analyze
root cause:
Was input malformed?
Was prompt unclear?
Was validation too strict?
Was context insufficient?
Test
stage in isolation:
Run stage with known good input
Verify prompt works correctly
Check output format
Fix
the issue:
Update prompt if unclear
Adjust validation if too strict
Add error handling if needed
Improve data passing if malformed
Retest
from failure point
Document
the fix
Workflow 4: Optimize Chain Performance
Analyze
current chain:
Execution time per stage
Token usage per stage
Success rate per stage
Bottlenecks
Identify
optimization opportunities:
Stages that could be parallelized
Redundant processing
Overly complex prompts
Unnecessary validation
Refactor
for efficiency:
Combine related stages
Parallelize independent stages
Simplify prompts
Optimize data passing
Maintain
quality:
Don't sacrifice accuracy for speed
Keep validation comprehensive
Preserve error handling
Test
optimized chain
Measure
improvements
Quick Reference
Action
Command/Trigger
Design new chain
"Design a prompt chain for [goal]"
Execute chain
"Run this chain: [chain spec]"
Debug chain failure
"Debug this chain: [error details]"
Optimize chain
"Optimize this chain: [chain spec]"
Validate chain design
"Review this chain design: [spec]"
Add stage to chain
"Add stage for [purpose] to this chain"
Parallelize stages
"Which stages can run in parallel?"
Best Practices
Keep Stages Focused
Each stage should have one clear purpose
Don't mix research and analysis in one prompt
Don't combine formatting with content generation
Each stage = one transformation
Make Outputs Explicit
Define exactly what each stage produces
Specify format (JSON, markdown, etc.)
Define required fields
Set quality criteria
Provide examples
Validate Between Stages
Catch errors early
Check output format
Verify required fields exist
Validate against criteria
Fail fast if something's wrong
Handle Errors Gracefully
Plan for failures
Define retry logic
Provide fallback options
Log failures for debugging
Don't cascade bad data
Maintain State Carefully
Track what you need, discard what you don't
Pass only necessary data forward
Keep intermediate outputs for debugging
Clear state between independent chains
Version chain state if long-running
Optimize Data Passing
Minimize token usage
Extract only needed information
Summarize when possible
Use references instead of duplication
Compress verbose outputs
Document Thoroughly
Make chains maintainable
Purpose of each stage
Expected inputs/outputs
Validation rules
Error handling
Example executions
Test Incrementally
Build confidence stage by stage
Test each stage in isolation
Test pairs of stages
Test full chain
Test with edge cases
Chain Design Patterns
Sequential Processing
Stage 1: Collect → Stage 2: Process → Stage 3: Format → Output
Use when
Each stage depends on previous stage's complete output
Example
Web scraping → Data cleaning → Analysis → Report generation
Branching Chain
Stage 1: Analyze →
If condition A: Stage 2a → Merge
If condition B: Stage 2b → Merge
→ Stage 3: Synthesize
Use when
Different processing paths based on criteria
Example
File type detection → [JSON parser | CSV parser | XML parser] → Normalize
Parallel Aggregation
Input →
[Stage 1a, Stage 1b, Stage 1c] (parallel) →
Stage 2: Combine →
Output
Use when
Independent analyses that merge later
Example
[Syntax check, Type check, Lint] → Aggregate results → Report
Iterative Refinement
Stage 1: Generate →
Stage 2: Critique →
If quality met: Output
If not: Refine → back to Stage 1 (max N iterations)
Use when
Output quality improves through iteration
Example
Write → Review → [Good? → Done | Revise → Write]
Fan-Out/Fan-In
Stage 1: Split →
[Process chunk 1, Process chunk 2, ..., Process chunk N] →