# Model Bank Metadata > Use for model knowledgeCutoff, family and generation metadata: onboarding, corrections, research and bulk backfills. - Skill: `lobehub/model-bank-metadata` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add lobehub/model-bank-metadata` - Raw SKILL.md: https://api.skillmd.com/api/skills/lobehub/model-bank-metadata/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: lobehub (https://skillmd.com/u/lobehub) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/lobehub/model-bank-metadata --- # 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/) 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: `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 scripts/apply-cutoffs.ts ` and `bun scripts/apply-family.ts ` (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.