Model-Bank Metadata (knowledgeCutoff / family / generation)

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 semantics

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.

Sourcing rules for knowledgeCutoff

Accept only:

• Vendor official docs (platform.openai.com / developers.openai.com, docs.x.ai, ai.google.dev, docs.anthropic.com / platform.claude.com)
• Official Hugging Face org model cards (huggingface.co/meta-llama/..., etc.)
• Official tech reports / system cards / launch blog posts

Reject:

• Third-party aggregator sites (aiknowledgecutoff.com and similar) — proven to copy one model's value across a whole family. A Cohere sweep once claimed 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.
• AWS Bedrock model cards as sole source — proven to conflate launch date with knowledge cutoff (DeepSeek R1's card lists both as "Jan 2025"). If Bedrock is the only place a value appears, leave the field empty.
• Inference from 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:

• Anthropic: Opus 4.6 reliable cutoff is 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).
• xAI: docs.x.ai has one blanket sentence covering grok-3/grok-4; mini variants are not named there. Grok 4.20/4.3 have no official cutoff anywhere.
• OpenAI: per-model docs pages (developers.openai.com/api/docs/models/<id>) state cutoffs explicitly, including snapshot differences (gpt-4-1106-preview 2023-04 vs gpt-4-0125-preview 2023-12).

family/generation derivation

Rule-based, no research needed: .agents/skills/model-bank-metadata/scripts/derive-family.ts holds the per-family regex rules. Traps already encoded there — keep them when extending:

• Date suffixes are not versions: claude-sonnet-4-20250514 is generation claude-4, not claude-4.2.
• Size suffixes are not versions: llama-3-8b → llama-3 (not llama-3.8); gemma-7b-it is gemma-1 (not gemma-7).
• Vendor spelling variants: qwen2p5 = qwen2.5, llama-v3p1 = llama-3.1, ollama :NNb tags, Bedrock us./global./anthropic. prefixes.
• claude-X.0 normalizes to claude-X.
• Fable/Mythos-class ids (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").

Repo-wide sweep workflow

1. Extract ids: 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.
2. Research (multi-agent): chunk ids by family (≤50 per chunk) and fan out one research agent per chunk (Workflow tool), each returning {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.
3. Policy filter: before applying, drop entries whose only source is a rejected category (check the returned sources map — e.g. drop everything sourced to aws.amazon.com).
4. Apply: bun .agents/skills/model-bank-metadata/scripts/apply-cutoffs.ts <map.json> and bun .agents/skills/model-bank-metadata/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).
5. Verify: cd packages/model-bank && bunx vitest run src/aiModels/__tests__/index.test.ts && bunx tsc --noEmit.

Maintenance rules

• New model PRs should fill all three fields inline, citing the official source in the PR body (see the Anthropic entries in anthropic.ts for reference values).
• After resolving merge conflicts in model-bank data files, sanity-check that metadata didn't vanish: 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.
• Dirty ids exist in aggregator data (a sambanova id once carried a trailing tab). The codemods match ids verbatim — if a map key won't apply, check for invisible characters before assuming the model is missing.