# Add Provider > Add a new inference provider to genai-prices, including provider data, matching, usage extractors, update automation, cassettes, agentic checks, generated artifacts, and Python/JavaScript parity tests. Use for a new provider, not a new model on an existing provider. - Skill: `pydantic/add-provider` (Agent Skill) - Install (CLI): `npx skillmds@latest add pydantic/add-provider` - Raw SKILL.md: https://api.skillmd.com/api/skills/pydantic/add-provider/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: pydantic (https://skillmd.com/u/pydantic) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pydantic/add-provider --- # Add a provider Add the provider as a complete, maintainable integration. Read `AGENTS.md` before editing. Preserve unrelated worktree changes. ## Establish the supported scope Use the provider's official pricing page and model catalog. Record the exact source URLs. Read `prices/units.yml` before mapping prices. Include only billing units represented by the current published contract. Do not add a unit to make a provider fit because that is a v3 contract change. Explain excluded model categories in the provider's `price_comments`. Decide whether the official source is a complete catalog or only a pricing page. This determines whether missing models can be treated as potential removals. ## Author the provider YAML Create `prices/providers/.yml`. Never edit generated artifacts directly. Cover these fields when they apply: - `id`, `name`, and official `pricing_urls` - `api_pattern` for the provider's actual API hosts and paths - `provider_match` for common provider identifiers - `model_match` only when the provider owns an unambiguous model-ID namespace - `fallback_model_providers` only when another provider's prices are intentionally inherited - `price_comments` for free allowances, unsupported units, regional differences, or other calculation boundaries For each supported model: - use the exact API model ID as the canonical `id` - add precise aliases without creating matches against sibling models - map official rates to the correct units, such as `_mtok`, `_kcount`, or `_mchars` - include cache, reasoning, modality, request, and tool rates when represented by the unit registry - set `prices_checked` to the verification date - preserve historical price changes with dated conditional prices instead of overwriting old rates - mark officially deprecated models without inferring removal from an incomplete source Keep models sorted by ID. Use names and context windows from official documentation when available. ## Add usage extraction Inspect recorded or documented response bodies for every supported API flavor. Do not assume that an OpenAI-compatible endpoint has the same response as the provider's native endpoint. Add provider extractors for flavors that report usage. Extract the model when the response includes it. When the response omits the model, return the usage with a nullable model so callers can provide the request model during price calculation. Map cached tokens separately. Any extractor that maps `completion_tokens` must also map the provider's reasoning-token breakdown to `output_reasoning_tokens`. Test each flavor through the public Python and JavaScript extraction APIs. If an API response does not report usage, leave that flavor unsupported and document the limitation instead of inventing a mapping. ## Add deterministic updates when possible When the official source is public and structured, add `prices/src/prices/source_.py`: - fetch with `httpx2` - parse prices as `Decimal` - update `prices_checked` while preserving curated names, aliases, context windows, and lifecycle flags - append changed rates as dated conditional prices instead of overwriting pricing history - add newly discovered in-scope models - refuse empty or suspiciously small results before writing - fail loudly when the source is unreadable or changes shape Expose `get__prices` through `prices.__main__`. Add `-get` to `Makefile` and include it in `get-all-prices`. Test the real HTTP boundary with `@pytest.mark.vcr`. Record the official response, inspect the cassette, remove credentials and transient cookies, commit it, and prove replay with: ```bash uv run pytest tests/test_source_.py --record-mode=none ``` Use compact synthetic inputs only for pure parser and update-behavior tests. Do not replace the external request with a hand-written HTTP mock. ## Add agentic monitoring Add the provider to `.github/agentic-price-check-providers.yml` with its YAML path, exact official sources, supported scope, and unit-mapping notes. Add every source domain to `network.allowed` in `.github/workflows/agentic-price-check-direct-providers.md`. Update the workflow's provider count and `.github/workflows/AGENTIC_PRICE_CHECK.md`. Compile the generated lock file with the pinned repository version. Never hand-edit the lock file. ```bash gh aw compile agentic-price-check-direct-providers --no-check-update gh aw compile agentic-price-check-direct-providers --no-check-update --no-emit --validate --actionlint ``` The agentic workflow is read-only. It reports price discrepancies, new models, potential removals, unchecked fields, and unreadable sources through one rolling issue. ## Verify both runtimes Add focused Python and JavaScript tests for: - provider selection by explicit provider ID - provider selection by API URL and model namespace when supported - representative input, cached-input, and output price calculation - every usage extractor flavor - aliases and context-sensitive matching that could collide - updater parsing, metadata preservation, write guards, and cassette replay Run `make build` after every provider-data edit. This regenerates the v2 feed, package data, schemas, and provider inventory. Never hand-edit those outputs or `tests/dataset/usages.json`. Before handoff, run: ```bash make all git diff --check ``` Inspect the provider YAML, cassette, and generated diff. Confirm that frozen v1 files are unchanged. Follow `AGENTS.md` for the commit, PR description, AI disclaimer, CI monitoring, and review resolution.