Reference Catalog Maintenance

Use this skill to audit and update SD.Next model reference catalogs using a phased approach: validate structure first, then resolve duplicates/conflicts, then apply minimal deterministic edits.

When To Use

• Adding or updating model entries in data/reference*.json
• Cleaning duplicates, stale entries, or inconsistent metadata
• Verifying category placement across base/cloud/quant/distilled/nunchaku/community
• Syncing catalog entries with thumbnail files in models/Reference

Guidance

• Consult .github/instructions/core.instructions.md for relevant core runtime guidance before proceeding.

Catalog Files In Scope

• data/reference-base.json (base)
• data/reference-cloud.json
• data/reference-quantized.json
• data/reference-distilled.json
• data/reference-nunchaku.json
• data/reference-community.json

Core Rules

Priority 1 - data safety and category stability:

• Verify category placement across catalogs and report conflicts first.
• Move entries between categories only when explicitly requested, or when placement is supported by at least two independent metadata sources.
• Keep changes targeted to only affected records.

Priority 2 - schema and formatting consistency:

• Preserve existing field names and conventions used by neighboring entries.
• Prefer deterministic normalization (stable key order, consistent value style).

Priority 3 - assets and size backfill:

• Do not overwrite real thumbnails with placeholders.
• For size backfill, use cli/hf-info.py -> section info -> field size as the primary source of truth.

Validation Checklist

1. Structural validity

• Confirm JSON parses cleanly.
• Ensure top-level structure matches existing catalog conventions.

2. Entry integrity

• Required identifiers exist and are non-empty.
• URLs/repo references are syntactically valid.
• No malformed numeric/string fields compared with peer entries.

3. Cross-catalog consistency

• Detect likely duplicates across reference*.json files.
• Flag conflicting metadata for the same model key/name.
• Resolve duplicates by keeping the most complete record in the correct category, then merge missing non-conflicting metadata from duplicate records.
• Report category conflicts; only auto-fix when rules are explicit.

4. Thumbnail alignment

• Check expected thumbnail presence under models/Reference.
• If missing and requested, create zero-byte placeholder only.
• Never replace existing non-empty image assets with placeholders.

5. Deterministic formatting

• Keep formatting style consistent with nearby file conventions.
• Avoid broad reformatting unrelated to edited records.

6. Fields checks

• Detect missing or extra fields compared to similar entries.
• Validate field value formats (e.g. size in GB, date format).
• Ensure that all fields are consistent and not null, empty or contain zero values.

7. Size backfill checks (size: 0)

• Enumerate all entries with "size": 0 across data/reference*.json.
• For each Hugging Face repo-style path (owner/name), run cli/hf-info.py.
• Parse info.data.size from tool output when present (format is MB string, e.g. "23933.4MB").
• Convert MB to GB using deterministic rounding: gb = round(mb / 1024, 2).
• Update only the size field for resolvable records; do not modify unrelated fields.
• If cli/hf-info.py returns ok: false, missing data.size, or non-repo paths, leave size unchanged and report as unresolved.
• Do not invent fallback sizes unless explicitly requested.

Safe Edit Workflow

1. Identify target entries and category intent.
2. Audit only relevant catalog files first.
3. Run size backfill using cli/hf-info.py.
4. Propose minimal edits (or apply when asked).
5. Re-validate JSON and duplicate checks.
6. Summarize exact changed records and rationale.

Common Failure Modes To Prevent

• Adding a model to wrong category file
• Duplicating near-identical entries under different names
• Breaking JSON structure while editing by hand
• Inconsistent key naming across similar entries
• Creating placeholder thumbnail over an existing asset
• Running cli/hf-info.py with the wrong Python environment/interpreter
• Treating subfolder variants as unsupported when the repo path itself is valid
• Writing guessed size values when cli/hf-info.py returns no size

Output Contract

When using this skill, provide:

• Files audited
• Validation findings grouped by severity
• Exact records changed (before/after summary)
• Duplicate/conflict report across catalogs
• Thumbnail sync result for models/Reference
• size: 0 backfill report: total candidates, updated count, unresolved count, unresolved reasons
• Residual risks or follow-up items