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Backfill and maintain model-bank metadata (knowledgeCutoff, family, generation). Use when adding models, fixing cutoff/family data, running a metadata sweep across aiModels providers, or researching official knowledge cutoffs.
LobeHub product design values / principles / checklists. Load this skill whenever the work touches user-interface features or implementation — designing or building any user-facing flow — to get better UX results.
LobeHub React component conventions. Use when editing TSX UI, choosing base-ui vs @lobehub/ui vs antd, styling with antd-style, routing, desktop variants, layouts, or component state.
Vitest testing guide. Use when writing or updating tests, fixing failing tests, improving coverage, debugging test issues, or setting up mocks.
Version release workflow — release process and GitHub Release notes (not docs/changelog pages).
Agentic end-to-end testing for LobeHub: backend verification via the CLI, frontend verification via agent-browser (Electron), full-stack verification in the browser, and bot-channel verification via osascript. Local-first today, designed to extend to cloud automation. Triggers on 'cli test', 'test with cli', 'verify with cli', 'backend test with cli', 'local test', 'test in electron', 'test desktop', 'test bot', 'bot test', 'test in discord', 'test in telegram', 'test in slack', 'test in wechat', 'test in weixin', 'test in lark', 'test in feishu', 'test in qq', 'manual test', 'osascript', 'test report', or any local end-to-end verification task.
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| name | model-bank-metadata |
| description | Backfill and maintain model-bank metadata (knowledgeCutoff, family, generation). Use when adding models, fixing cutoff/family data, running a metadata sweep across aiModels providers, or researching official knowledge cutoffs. |
| user-invocable | false |
How to populate and maintain the three structured metadata fields on packages/model-bank/src/aiModels/*.ts model cards, at single-model scale (new model PR) or repo-wide scale (sweep across ~80 provider files / ~1900 entries).
| Field | Format | Meaning |
|---|---|---|
knowledgeCutoff | 'YYYY-MM' (or 'YYYY' if only the year is published) | World-knowledge cutoff. When a vendor distinguishes a "reliable knowledge cutoff" from the broader training-data cutoff (Anthropic does), always use the reliable one. |
family | lowercase slug (claude, gpt, o-series, qwen, deepseek, llama, glm, …) | Model lineage, finer than organization. Lets the UI group models and match the same model across aggregator providers. |
generation | family slug + version (claude-4.6, gpt-5.2, qwen3.5, llama-3.1) | Generation within the family. Only set when confidently derivable from the model line's naming. Rolling aliases (qwen-max, deepseek-chat, gemini-flash-latest) get family only. |
All three are optional. The cardinal rule: only fill what an authoritative source states or naming rules derive — never guess. An empty field is correct for vendors that publish nothing.
No DB migration is ever needed for these: builtin models are merged from model-bank at read time (repositories/aiInfra/index.ts spreads the whole card), so new card fields flow to the client automatically.
Accept only:
Reject:
2024-06 for four distinct base models; none of the cited Cohere pages said that, and the only cutoff Cohere actually publishes is Feb 2023 for the 08-2024 Command R/R+ refresh.releasedAt — a release date is not a cutoff.Variant inheritance: dated snapshots (-2024-08-06), speed/price tiers of the same checkpoint, quantizations (-fp8, -awq), context-length variants (-32k), ollama :NNb tags, and cloud-prefixed ids (anthropic./us./global. Bedrock ids) share their base model's cutoff. Distills do not inherit from teacher or base — use the distill's own published value or leave empty. Sizes within one generation can genuinely differ: Llama 3 8B is Mar 2023 while 70B is Dec 2023 (per Meta's own card) — don't "fix" that to one family-wide value.
Vendors that publish no cutoffs (leave empty, don't chase): Qwen, DeepSeek, GLM/Zhipu, ERNIE, Doubao, Hunyuan, SenseNova, Spark, MiniMax, StepFun, Yi (mostly), Moonshot.
Known per-vendor footguns:
2025-05, Sonnet 4.6 is 2025-08 — easy to swap. Claude 3.7 is 2024-10 (system card: trained through Nov 2024, knowledge cutoff end of Oct 2024). Cite system cards / the models overview, not the Help Center article (a living page that drops retired models — citation rot).2023-04 vs gpt-4-0125-preview 2023-12).Rule-based, no research needed: scripts/derive-family.ts holds the per-family regex rules. Traps already encoded there — keep them when extending:
claude-sonnet-4-20250514 is generation claude-4, not claude-4.2.llama-3-8b → llama-3 (not llama-3.8); gemma-7b-it is gemma-1 (not gemma-7).qwen2p5 = qwen2.5, llama-v3p1 = llama-3.1, ollama :NNb tags, Bedrock us./global./anthropic. prefixes.claude-X.0 normalizes to claude-X.claude-fable-5) don't match the opus/sonnet/haiku regex — they are the Mythos class — family: 'claude-mythos', generation: 'mythos-5' (set manually; the launch page calls Fable 5 "the generally available Mythos-class model").bun .agents/skills/model-bank-metadata/scripts/extract-model-ids.ts → unique normalized chat-model ids (normalization = last path segment, lowercased). Non-chat types (image/video/embedding/tts) have no knowledge cutoff — skip them.{id, cutoff, source} with the sourcing rules above baked into the prompt, plus one adversarial verify agent per chunk that re-fetches cited sources and refutes unsupported claims. The verify pass is load-bearing: it caught the Cohere aggregator copy-paste and the AWS launch-date conflation.sources map — e.g. drop everything sourced to aws.amazon.com).bun scripts/apply-cutoffs.ts <map.json> and bun scripts/apply-family.ts <map.json> (run from repo root). Both are idempotent codemods keyed on normalized id — aggregator providers get the same values automatically; entries that already have the field are skipped. They rely on the uniform prettier formatting of the data files (entries start { / end },, fields at 4-space indent).cd packages/model-bank && bunx vitest run src/aiModels/__tests__/index.test.ts && bunx tsc --noEmit.anthropic.ts for reference values).git grep -c knowledgeCutoff -- 'packages/model-bank/src/aiModels/*.ts' before vs after. A three-way stack of model PRs once silently dropped all 10 Anthropic cutoffs during conflict resolution.