LLM Finder

Recommend models that fit the stated task, with explicit quality, deployment, and cost tradeoffs.

This skill is coordination-exempt: skip the ai-coord gate for its declared work.

User instructions take precedence over these defaults. This skill reads sources and reports in chat. Save a report only when the user requests a file. Repository writes beyond this declared work must enter the applicable coordination gate. Do not implement a recommendation, install tools, or run paid inference by default. Explicit task or session authorization governs those actions.

This skill runs only through explicit invocation or an authorized parent workflow. A parent workflow may load these instructions and apply narrower requirements. Do not infer a product directory or require a repository.

Workflow

1. Define the workload

Read the caller's requirements and relevant supplied context before researching candidates. State material assumptions. Ask only when missing information changes scope, deployment safety, or the ranking. Continue independent research while that information is pending.

Record the task, modalities, required languages, output contract, context size, latency, throughput, and expected volume. Record the current model and exact version when available. Record budget, hardware, license, region, retention, and no-training constraints. Treat deployment restrictions as caller requirements. Do not impose a particular gateway or hosting platform.

2. Find current candidates

Read references/sources.md before gathering evidence. Start with creator documentation and model cards. Check provider availability, pricing, capabilities, and retirement notices. Use task-matched leaderboards to test quality claims. Use Hugging Face metadata, cards, revisions, licenses, and hardware evidence for open-weights candidates.

Cover hosted APIs, gateways, and local inference when they fit the workload. Narrow the search when the caller restricts deployment. Batch independent source lookups. Give brief progress updates during sustained research.

Record exact model IDs, versions or revisions, serving providers, quantizations, and retrieval dates. Distinguish a creator's model from a provider's deployment. Confirm that a benchmark result matches the proposed version and settings.

Use public APIs and plain page retrieval first. When a required page needs rendering, use the chromium-browser skill. Do not install a browser, CLI, or package automatically.

3. Compare evidence and fit

Apply hard requirements before ranking quality or cost. Check input and output modalities, schema and tool support, context limits, languages, licenses, lifecycle, region, and retention. For local inference, check memory, runtime support, quantization, and expected speed on the available hardware.

Keep demonstrated quality separate from popularity and adoption. Downloads, likes, usage share, and creator recognition can support adoption claims. They do not establish task accuracy. Do not reject a newcomer solely for missing popularity or an arbitrary adoption threshold. Apply an explicit caller reputation gate when one exists.

Use task-relevant benchmark categories and evaluation methods. Record score dates, settings, uncertainty, and material method differences. Missing evidence does not establish equal quality. Label unsupported quality claims and cost gaps. Do not fabricate scores, prices, latency, or availability.

When a discrete decision precedes language generation, assess a decision model plus a language model. Recommend the pair only when its modalities fit and its expected cost, latency, or control benefit justifies both stages.

4. Compare cost on one workload

Use the same workload and volume for every option. State input, output, reasoning, image, audio, caching, batch, and region assumptions when applicable. Include both stages of a pair and any routing frequency. For local inference, separate hardware and hosting expense from token charges. Do not treat local inference as free by assumption.

Show cost per request and at the expected volume. Compare absolute savings or premiums and ratios against the current model. If its price is unavailable, name another baseline. Keep observed costs separate from estimates. Use scenarios when usage or prices remain uncertain. Do not infer total cost from input price alone.

5. Deliver the recommendation

Lead with the preferred option and useful alternatives. Explain when each option is preferable. State whether the recommendation changes the current model. Do not turn alternatives into a routing implementation requirement.

Include these fields in the report:

• Workload, constraints, baseline, and material assumptions.
• Exact candidate IDs and versions, deployment paths, retrieval dates, and fit decisions.
• Task-relevant quality evidence and separate adoption evidence, with source links.
• Prices, cost assumptions, estimated costs, and relative costs on the common workload.
• Excluded candidates and reasons, including unavailable or retired deployments.
• Evidence gaps and the smallest evaluation needed to resolve a material uncertainty.

Completion Evidence

Complete the authorized research and deliver the report in chat. If a file was requested, also save it at the requested path. Do not stop after a shortlist or a progress report. Stop for a missing requirement only when it prevents a useful recommendation. State the exact missing input and complete independent research first.

Gateway Helper

Resolve scripts/ relative to this skill directory. The helper uses runtime builtins and needs no package installation. It reads the public catalog without authentication:

bun run <skill-root>/scripts/gateway-models.ts --input image --tag structured-output --region eu --max-input 2
node <skill-root>/scripts/gateway-models.ts --type evaluation --input text
bun run <skill-root>/scripts/gateway-models.ts --endpoints <creator/model>

Default: non-deprecated language models, newest first. Options: --type <t>, --match <a,b>, --creator <a,b>, --input <a,b>, --tag <a,b>, --region <r>, --zdr, --max-input <USD per 1M>, --max-output <USD per 1M>, --include-deprecated, --describe, --endpoints <id>, and --json. --zdr accepts models whose catalog value is all or some. Verify the chosen provider when a requirement needs universal zero data retention.

The helper preserves unknown model fields in JSON output. Missing prices display as - and fail an active price ceiling. Requests time out after 20 seconds. Exit codes: 0 for success, 2 for invalid arguments, and 1 for network or response-validation errors. Catalog values are evidence of the provider's claims, not an independent service guarantee.