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reference-catalog

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Maintain and validate SD.Next model reference catalogs in data/reference*.json, including schema consistency, deduplication, link checks, and thumbnail alignment.

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Source SKILL.md: https://github.com/vladmandic/sdnext/blob/HEAD/.github/skills/reference-catalog/SKILL.md

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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.
  1. Entry integrity
  • Required identifiers exist and are non-empty.
  • URLs/repo references are syntactically valid.
  • No malformed numeric/string fields compared with peer entries.
  1. 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.
  1. 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.
  1. Deterministic formatting
  • Keep formatting style consistent with nearby file conventions.
  • Avoid broad reformatting unrelated to edited records.
  1. 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.
  1. 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