Fill Model Descriptions

Goal

Replace empty description: "" strings in packages/llm-info/data/models.yml with concise, accurate one-sentence summaries sourced from OpenRouter first, then provider documentation as a fallback.

Scope and Contract

• Only modify entries where description is the empty string. Existing non-empty descriptions are human-curated; never overwrite them.
• The file is a top-level YAML map keyed by provider id (anthropic:, openai:, …). Each provider's value is an array of model entries.
• Do not modify any other field, reorder entries, or change formatting (flow-style arrays like roles: [chat, edit], blank lines between entries, blank line between provider sections).
• This skill requires network access.

Workflow

1. Read packages/llm-info/data/models.yml. Parse it with the yaml library's parseDocument (NOT parse) so formatting and comments are preserved on write-back.

2. Collect every entry where description is the empty string. Track them as { provider, model } pairs.

3. Source descriptions in this priority order:

   a. OpenRouter — GET https://openrouter.ai/api/v1/models (no auth required). Build a lookup keyed by lowercased id. For each { provider, model }, try candidates in this order:

   • ${vendor}/${model} where vendor comes from the Vendor Map below.
   • ${vendor}/${model.replaceAll('-', '.')} (some OR ids use . separators, e.g. claude-3.5-sonnet).
   • For Bedrock entries with region/alias prefixes (us., eu., jp., global., anthropic., minimax., mistral., zai., nvidia.), strip the prefix and retry with the appropriate vendor.
   • For Google entries with @default suffix (e.g. claude-opus-4-7@default), strip the suffix and try anthropic/${rest}.

   b. Provider docs — if no OpenRouter hit, search the canonical provider's docs page (anthropic.com/news, platform.openai.com/docs/models, ai.google.dev/gemini-api/docs/models, mistral.ai/news, x.ai/blog, etc.) for the model's one-line summary. Use the WebSearch or WebFetch tool.

   c. Skip — if neither source has a confident answer, leave description: "" and record the { provider, model } for the final summary. Empty is better than wrong.

4. Truncate each sourced description per the rules in the "Truncation" section.

5. Write the YAML back using targeted Pair mutations on the parsed Document, not text replacement. Reference implementation: packages/llm-info/src/sync-models.ts shows how to load a Document, mutate sequence/map nodes, and stringify with { lineWidth: 0, flowCollectionPadding: false } to preserve formatting.

6. Validate by running:

   bashCopy

   pnpm --filter @marimo-team/llm-info test
   

   The schema.test.ts test will catch any structural drift.

7. Report to the user:

   • How many entries got descriptions.
   • How many were skipped (with the provider/model list).

Vendor Map

OpenRouter id vendor prefixes for each marimo provider:

Marimo provider	OpenRouter vendor prefix	Notes
anthropic	anthropic	Direct match in most cases
openai	openai	Direct match
google	google	Strip @default suffix if present
mistral	mistralai	Note the ai suffix
xai	x-ai	Hyphenated
azure	openai	Azure mostly re-exposes OpenAI models
bedrock	varies	Strip region prefix (us./eu./jp./global.) and use the embedded vendor (anthropic.foo → anthropic/foo)
github	varies	Ids like openai/gpt-4.1 carry the vendor; strip the github layer
openrouter	embedded in id	Ids like anthropic/claude-opus-4.7-fast already carry the vendor
wandb	embedded in id	Ids like deepseek-ai/DeepSeek-V3.1; the prefix is the vendor
opencode-go	rarely on OR	Source from provider docs
ollama	not on OR	Source from the Ollama model card

Truncation

• One sentence. Split on . and take the first. Strip trailing whitespace and any markdown formatting (**, _, backticks).
• Hard cap at 200 characters. If the first sentence is longer, cut at the last word boundary before 200 and append ….
• Strip leading articles for consistency: prefer "Frontier reasoning model optimized for…" over "This is a frontier reasoning model optimized for…".
• Prefer capability statements over marketing. If OpenRouter gives "the best model from X" but the provider docs give "optimized for tool use and long-context reasoning", prefer the latter.

Examples

Good (matches the tone of existing hand-curated entries):

• Latest Opus model, strongest for coding and long-running professional tasks
• Most capable Sonnet-class model, with frontier performance across coding, agents, and professional work
• Lightweight Gemma model trained by Google, designed to run on a single GPU
• Multimodal Mixture-of-Experts model optimized for complex tool use and reasoning

Bad (do not produce these):

• Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at coding, vision… — too long, contains marketing
• The best model from Anthropic — vague, no information content
• An AI model — useless
• **Claude 3.5 Sonnet** is a _frontier_ model… — markdown not stripped

Quick Reference

• Data file: packages/llm-info/data/models.yml
• Schema: packages/llm-info/src/index.ts (AiModel interface)
• YAML write reference: packages/llm-info/src/sync-models.ts (buildEntryNode, addProviderSection)
• OpenRouter API: https://openrouter.ai/api/v1/models (public, no auth)
• Validation: pnpm --filter @marimo-team/llm-info test