Workflow Patterns
Use these patterns when skills need multi-step processes with quality controls.
Sequential Workflows
Break complex tasks into clear, sequential steps with a checklist Claude can track:
## PDF form filling workflow
Copy this checklist and track your progress:
Task Progress:
- Step 1: Analyze the form (run analyze_form.py)
- Step 2: Create field mapping (edit fields.json)
- Step 3: Validate mapping (run validate_fields.py)
- Step 4: Fill the form (run fill_form.py)
- Step 5: Verify output (run verify_output.py)
**Step 1: Analyze the form**
Run: `python scripts/analyze_form.py input.pdf`
This extracts form fields and saves to `fields.json`.
**Step 2: Create field mapping**
Edit `fields.json` to add values for each field.
**Step 3: Validate mapping**
Run: `python scripts/validate_fields.py fields.json`
Fix any validation errors before continuing.
**Step 4: Fill the form**
Run: `python scripts/fill_form.py input.pdf fields.json output.pdf`
**Step 5: Verify output**
Run: `python scripts/verify_output.py output.pdf`
If verification fails, return to Step 2.
Clear steps with checklists help both Claude and users track progress.
Conditional Workflows
For tasks with branching logic, guide Claude through decision points:
## Document modification workflow
1. Determine the modification type:
**Creating new content?** → Follow "Creation workflow" below
**Editing existing content?** → Follow "Editing workflow" below
2. Creation workflow:
- Use docx-js library
- Build document from scratch
- Export to .docx format
3. Editing workflow:
- Unpack existing document
- Modify XML directly
- Validate after each change
- Repack when complete
Tip: If workflows become large, push them into separate files and tell Claude to read the appropriate file based on the task.
Feedback Loops
Common pattern: Run validator → fix errors → repeat
This pattern greatly improves output quality.
Example 1: Style Guide Compliance (No Code)
## Content review process
1. Draft your content following the guidelines in STYLE_GUIDE.md
2. Review against the checklist:
- Check terminology consistency
- Verify examples follow the standard format
- Confirm all required sections are present
3. If issues found:
- Note each issue with specific section reference
- Revise the content
- Review the checklist again
4. Only proceed when all requirements are met
5. Finalize and save the document
Example 2: Document Editing (With Code)
## Document editing process
1. Make your edits to `word/document.xml`
2. **Validate immediately**: `python scripts/validate.py unpacked_dir/`
3. If validation fails:
- Review the error message carefully
- Fix the issues in the XML
- Run validation again
4. **Only proceed when validation passes**
5. Rebuild: `python scripts/pack.py unpacked_dir/ output.docx`
6. Test the output document
The validation loop catches errors early.
Research Synthesis Workflow (No Code)
For skills without executable code:
## Research synthesis workflow
Copy this checklist and track your progress:
Research Progress:
- Step 1: Read all source documents
- Step 2: Identify key themes
- Step 3: Cross-reference claims
- Step 4: Create structured summary
- Step 5: Verify citations
**Step 1: Read all source documents**
Review each document in `sources/`. Note main arguments and evidence.
**Step 2: Identify key themes**
Look for patterns across sources. What themes appear repeatedly?
**Step 3: Cross-reference claims**
For each major claim, verify it appears in source material.
**Step 4: Create structured summary**
Organize findings by theme:
- Main claim
- Supporting evidence
- Conflicting viewpoints (if any)
**Step 5: Verify citations**
Check every claim references the correct source. If incomplete, return to Step 3.
Verifiable Intermediate Outputs
For complex tasks, create plan files that get validated before execution:
Problem: Asking Claude to update 50 form fields based on a spreadsheet without validation could result in referencing non-existent fields, conflicting values, or missed required fields.
Solution: Create changes.json → validate → execute
## Batch update workflow
1. Analyze source data and target document
2. Create `changes.json` with planned modifications:
```json
{
"field_name": "new_value",
"another_field": "another_value"
}
- Run:
python scripts/validate_changes.py changes.json - If validation fails, fix
changes.jsonand re-validate - Only when validation passes:
python scripts/apply_changes.py
**Why this works:**
- **Catches errors early** — Validation finds problems before changes apply
- **Machine-verifiable** — Scripts provide objective verification
- **Reversible planning** — Claude can iterate on plan without touching originals
- **Clear debugging** — Error messages point to specific problems
**When to use**: Batch operations, destructive changes, complex validation rules, high-stakes operations.
**Implementation tip**: Make validation scripts verbose:
Field 'signature_date' not found. Available fields: customer_name, order_total, signature_date_signed