Procedure Refiner

Overview

Manage the iterative refinement of LA-Bench experimental procedures by orchestrating validation and regeneration cycles. This skill handles reading procedures from output JSONL files by ID, coordinating with procedure-checker and procedure-generator skills, and persisting improvements back to the same files.

When to Use This Skill

Use this skill when:

• Refining generated experimental procedures for specific LA-Bench IDs
• Validating and improving procedures against LA-Bench evaluation criteria
• Managing iterative improvement cycles for multiple procedure entries
• Working with output JSONL files from procedure generation runs

Core Workflow

1. Single Procedure Refinement

To refine a single procedure entry:

1. Read the current procedure using scripts/procedure_io.py:

   python scripts/procedure_io.py read <output_jsonl_path> <entry_id>
   

2. Validate with procedure-checker skill:

   • Invoke the procedure-checker skill with the current procedure
   • Review validation results for formal and semantic issues

3. Regenerate if needed using procedure-generator skill:

   • If validation fails, use procedure-generator skill to create improved version
   • Provide validation feedback to inform regeneration

4. Update the output file:

   python scripts/procedure_io.py update <output_jsonl_path> <entry_id> '<procedure_steps_json>'
   

5. Repeat steps 2-4 until validation passes or maximum iterations reached

2. Batch Refinement

To refine multiple entries from an output JSONL file:

1. Read all entry IDs from the JSONL file
2. For each entry ID, execute the Single Procedure Refinement workflow
3. Track progress and maintain iteration counts per entry
4. Report results summarizing validation status for all entries

Iteration Management

Maximum iterations per entry: 3-5 iterations recommended to prevent infinite loops

Iteration strategy:

• Iteration 1: Initial validation, identify major issues
• Iteration 2: Regenerate with validation feedback
• Iteration 3: Fine-tune remaining issues
• Beyond 3: Only if consistent improvement is observed

Exit conditions:

• Validation passes all criteria
• Maximum iterations reached
• No improvement observed between iterations

Input/Output File Management

Reading Procedures

Always use the procedure_io.py script to read from output JSONL files:

python scripts/procedure_io.py read <jsonl_path> <entry_id>

This ensures:

• Correct parsing of both compact and pretty-printed JSONL
• Consistent data structure handling
• Proper error messages if entry not found

Writing Procedures

Always use the procedure_io.py script to persist changes:

python scripts/procedure_io.py update <jsonl_path> <entry_id> '<procedure_steps_json>'

Important: The script preserves all other entries in the JSONL file unchanged.

File Paths

Common output JSONL file locations:

• outputs/runs/generated_<timestamp>.jsonl - Timestamped generation runs
• outputs/refined/refined_<timestamp>.jsonl - Refinement results (optional separate output)

Integration with Other Skills

procedure-checker Skill

Purpose: Validate procedures against LA-Bench criteria and Completed Protocol standards

Validation includes:

• Formal constraints (step count, sentence limits)
• Semantic quality (alignment with expected outcomes)
• Completed Protocol criteria (parameter explicitness, reagent flow, physical constraints)

When to invoke:

• After reading a procedure from JSONL
• After regenerating a procedure
• To assess current quality before deciding to regenerate

Expected output: Validation report with formal and semantic feedback including Completed Protocol assessment

Gemini Validation Option:

Before or during validation, offer the user the option to use gemini for an alternative evaluation perspective:

1. Ask the user: "Would you like me to also validate this procedure using gemini? (y/n)"

2. If yes, prepare validation prompt containing:

   • The procedure steps
   • LA-Bench evaluation criteria
   • Expected final states
   • Any specific validation focus areas

3. Execute gemini validation:

   gemini -p "Validate the following experimental procedure against LA-Bench criteria: [procedure details and evaluation criteria]"
   

4. Compare and synthesize results:

   • Review both Claude's validation (procedure-checker skill) and gemini's evaluation
   • Identify consensus issues (flagged by both)
   • Note divergent perspectives
   • Present unified validation feedback to inform regeneration

Benefits:

• Cross-validation with different model perspectives
• May catch issues overlooked by single evaluator
• Provides richer feedback for procedure improvement

procedure-generator Skill

Purpose: Generate or regenerate procedures from LA-Bench input data following Completed Protocol standards

Generation includes:

• Quantitative specifications for all parameters
• Complete experimental design
• Logical temporal ordering
• Reproducibility measures
• Completed Protocol requirements (explicit parameters, reagent flow, physical constraints)

When to invoke:

• When validation identifies issues requiring regeneration
• When initial procedure quality is insufficient

Required inputs:

• LA-Bench input data (from la-bench-parser skill)
• Validation feedback from previous iteration (if available)
• Focus areas from Completed Protocol assessment

Expected output: New procedure_steps array with enhanced detail level

la-bench-parser Skill

Purpose: Extract input data from LA-Bench JSONL files

When to invoke:

• Before regenerating a procedure (to get original input data)
• To retrieve instruction, mandatory_objects, source_protocol_steps, etc.

Usage:

python .claude/skills/la-bench-parser/scripts/parse_labench.py <input_jsonl> <entry_id>

Example Refinement Session

User request: "Refine the procedure for public_test_1 in outputs/runs/generated_20251119_082022.jsonl"

Execution steps:

1. Read current procedure:

   python scripts/procedure_io.py read outputs/runs/generated_20251119_082022.jsonl public_test_1
   

2. Ask user: "Would you like me to also validate this procedure using gemini? (y/n)"

3. Validate the procedure:

   • Always invoke procedure-checker skill with the retrieved procedure
   • If user agreed, also run gemini validation:

     gemini -p "Validate the following LA-Bench experimental procedure: [procedure + criteria]"
     

   • If both validations used, synthesize and compare results

4. If validation fails:

   • Invoke la-bench-parser skill to get original input data
   • Invoke procedure-generator skill with input data + validation feedback (from Claude and/or gemini)
   • Update JSONL with new procedure:

     python scripts/procedure_io.py update outputs/runs/generated_20251119_082022.jsonl public_test_1 '<new_steps_json>'
     

5. Re-validate with procedure-checker skill (and gemini if user opted in)

6. Repeat until validation passes or max iterations reached

7. Report final status to user with summary of both evaluations (if dual validation was used)

Best Practices

Avoid Direct JSONL Manipulation

Do not read or write JSONL files directly. Always use procedure_io.py to ensure:

• Consistent parsing logic
• Proper preservation of file structure
• Error handling for missing entries

Provide Iteration Context

When regenerating procedures, include:

• Which iteration number (e.g., "Iteration 2 of 5")
• What issues were identified in validation
• What changes are being targeted
• Specific Completed Protocol criteria to address (e.g., "Add missing centrifuge parameters", "Clarify reagent flow")

Track Improvements

Compare validation scores across iterations:

• Monitor whether issues are being resolved
• Track improvement in Completed Protocol criteria scores
• Detect if regeneration is introducing new issues
• Decide when to stop iterating

Focus on Completed Protocol Criteria

When validation identifies Completed Protocol issues:

• Parameter explicitness: Add specific values and ranges
• Operation parameters: Complete all missing parameters (speed, time, temperature, etc.)
• Reagent flow: Clarify defines/kills for each operation
• Physical constraints: Verify container capacities and volumes
• Termination criteria: Quantify ambiguous conditions

Handle Errors Gracefully

If procedure_io.py returns errors:

• Verify file path exists
• Check that entry ID is correct
• Ensure JSONL file format is valid

Resources

scripts/procedure_io.py

Python script for JSONL file I/O operations supporting:

• Read mode: Extract a single procedure entry by ID
• Update mode: Modify an existing entry's procedure_steps
• Write mode: Add new entry or update existing (upsert operation)

CLI usage:

# Read
python scripts/procedure_io.py read <jsonl_path> <entry_id>

# Update
python scripts/procedure_io.py update <jsonl_path> <entry_id> '<procedure_steps_json>'

# Write (upsert)
python scripts/procedure_io.py write <jsonl_path> <entry_id> '<procedure_steps_json>'

Python API:

from procedure_io import read_procedure, update_procedure, write_procedure

# Read
entry = read_procedure("outputs/runs/generated.jsonl", "public_test_1")

# Update
success = update_procedure("outputs/runs/generated.jsonl", "public_test_1", new_steps)

# Write
success = write_procedure("outputs/runs/generated.jsonl", "public_test_1", new_steps)