Reference Library Updater Skill
Purpose
Improve the reference library based on successful shot generation, extracting high-quality generated images to serve as improved references.
Trigger
After visual continuity validation and manual review of episode.
Inputs Required
- •
SHOTS_EP{{XX}}/*.png- Generated shots - •
SHOT_QA_REPORT_EP{{XX}}.md- Quality scores - •
VISUAL_CONTINUITY_REPORT_EP{{XX}}.md - •
CHARACTER_REFS/*/refs/*.png- Current references - •
LOCATION_REFS/*/refs/*.png- Current references - •
CANON_DB.json
Outputs Produced
- •Updated reference images (if improvements found)
- •
REFERENCE_UPDATE_LOG.md - •Updated
CANON_DB.json(new reference paths)
Process
Step 1: Identify High-Quality Shots
From QA reports, find shots that:
- •Scored highly on consistency (0.90+)
- •Passed all quality checks
- •Received positive manual review
Step 2: Evaluate for Reference Potential
A shot is a good reference candidate if:
- •Character is clearly visible
- •Pose/expression is useful for future generation
- •Lighting is representative
- •No distracting elements
Best Candidates:
- •Clean single-character shots
- •Clear facial expressions
- •New poses not in current references
- •Better quality than existing references
Step 3: Compare to Existing References
For each candidate:
- •Identify which reference it could replace/supplement
- •Compare quality objectively
- •Determine if it adds value to library
Replacement Criteria:
- •Clearly better consistency scores when used
- •Better detail/quality
- •More representative of character
Supplement Criteria:
- •New pose not currently covered
- •New expression not currently covered
- •Different context useful for future shots
Step 4: Extract and Process
For approved candidates:
- •Crop to appropriate framing
- •Ensure consistent dimensions
- •Apply any necessary color correction
- •Save to appropriate reference directory
Naming Convention:
{character}_{type}_{variant}_gen.png
The _gen suffix indicates generated (not hand-crafted) reference.
Step 5: Test New References
Before committing:
- •Use new reference to regenerate a known shot
- •Compare quality to original generation
- •Verify improvement
Step 6: Update Reference Library
If tests pass:
- •Add new reference to appropriate directory
- •Update CANON_DB.json with new path
- •Keep old reference as backup
json
"reference_images": {
"front_neutral": "CHARACTER_REFS/ALICE/refs/alice_front_neutral.png",
"front_neutral_gen": "CHARACTER_REFS/ALICE/refs/alice_front_neutral_gen.png",
"action_running": "CHARACTER_REFS/ALICE/refs/alice_running_gen.png"
}
Step 7: Document Changes
Log all updates:
markdown
# Reference Library Update Log ## Update: 2026-01-25 ### ALICE_CHEN **Added**: - `alice_determined_gen.png` - Source: EP01_SC08_SH03 - Reason: Better determined expression than original - Quality: 0.94 **Considered but Rejected**: - EP01_SC05_SH02 - Reason: Lighting too specific to scene ### PRECINCT_BULLPEN **Added**: - `bullpen_night_gen.png` - Source: EP01_SC12_SH01 - Reason: Night variant not previously covered
Reference Quality Hierarchy
Tier 1: Seed References (Original)
- •Hand-crafted turnarounds
- •Expression packs
- •Core poses
- •Never replace, only supplement
Tier 2: Generated References (High Quality)
- •Extracted from successful shots
- •High consistency scores
- •Tested and validated
- •Can supplement Tier 1
Tier 3: Context References
- •Scene-specific variations
- •Lower priority
- •Used for specific shot types
- •Use when Tier 1/2 not suitable
When NOT to Update
- •Don't replace seed references
- •Don't add marginal improvements
- •Don't bloat library with redundant refs
- •Don't add scene-specific refs as general refs
Feedback Loop
The update cycle improves generation over time:
code
Generate Shots → Quality Check → Find High Performers
↑ ↓
└──── Update References ← Extract Best ←
Notes
- •Be conservative about updates
- •Quality over quantity in reference library
- •Seed references remain authoritative
- •Generated refs supplement, don't replace
- •Track performance of new refs
- •Roll back if new refs cause problems