| name | kiln-check-deprecation |
| description | Check Kiln's model list for deprecated or sunset models across all providers. Use when the user wants to find deprecated models, check model availability, audit the model list for stale entries, or mentions model deprecation/sunset/end-of-life. |
Check Model Deprecation in Kiln
Audit libs/core/kiln_ai/adapters/ml_model_list.py to find models that have been deprecated, removed, or are approaching end-of-life across all providers. All checks are free ā they use model-listing endpoints, not inference.
Global Rules
- Sandbox: All
curl, uv run, and python3 commands that hit the network MUST use required_permissions: ["all"]. The sandbox blocks network access.
- Env vars: Source
.env before running check scripts: export $(grep -v '^#' .env | xargs)
- Vertex AI auth: The Vertex check requires
gcloud CLI authentication. Before running, ask the user to run gcloud auth login if they haven't recently. If gcloud auth print-access-token fails, prompt the user to authenticate.
- Non-destructive: This skill only sets
deprecated=True on individual provider entries. It never removes models or providers from the list.
- Skip already-deprecated: Only check providers where
deprecated=False (the default). Don't re-check entries already marked deprecated.
Phase 1 ā Extract Current Model-Provider Pairs
Run the extraction script from the repo root:
python3 .agents/skills/kiln-check-deprecation/scripts/extract_models.py > /tmp/kiln_extracted.json
This fetches the published model list from https://remote-config.getkiln.ai/kiln_config_v2.json and extracts:
- The provider name
- The model_id
- The parent model's enum name
It skips entries already marked deprecated=True.
Output: JSON to stdout (pipe to file), human summary to stderr.
The JSON contains:
deprecated_count: number of already-deprecated entries
providers: dict of provider_name ā sorted unique list of model_ids
entries: list of {enum, provider, model_id} for each non-deprecated entry
Phase 2 ā Check Each Provider
Option A: Check all providers at once
export $(grep -v '^#' .env | xargs)
python3 .agents/skills/kiln-check-deprecation/scripts/check_provider.py all > /tmp/kiln_check_results.json
Option B: Check a single provider
export $(grep -v '^#' .env | xargs)
python3 .agents/skills/kiln-check-deprecation/scripts/check_provider.py openrouter > /tmp/kiln_check_openrouter.json
The script handles all provider API quirks automatically:
- Together AI returns a flat JSON array (not
{data: [...]})
- Gemini API needs both v1 and v1beta endpoints (preview/Gemma models only on v1beta)
- Gemini model names are prefixed with
models/ ā stripped automatically
- Anthropic uses
x-api-key header, not Authorization: Bearer
- OpenRouter is public (no auth) and includes
expiration_date fields
- Fireworks AI
/v1/models only lists serverless models; the script instead checks each model individually via the model detail API (GET /v1/{model_id}), which covers all tiers (serverless, on-demand, fine-tune). A model is only flagged as missing if it returns HTTP 404 from the detail API.
- OpenRouter
:exacto is a virtual routing suffix (quality-first provider sorting) that never appears in model listings. The script strips it before checking. :free and :thinking are real model entries that appear in the listing when available ā if they're missing, it's a genuine removal.
- Vertex AI uses the v1beta1 publisher models endpoint with
x-goog-user-project header. Requires gcloud CLI auth. Kiln entries may use meta/ prefix for LiteLLM routing ā stripped automatically. Versioned aliases (e.g. gemini-2.0-flash-001 ā gemini-2.0-flash) are handled by stripping 3-digit version suffixes.
Output: JSON to stdout with per-provider results, human summary to stderr.
Each provider result contains:
missing: model_ids not found in the provider's listing
expiring: model_ids with upcoming expiration dates (OpenRouter only)
entries_to_deprecate: full enum/provider/model_id entries for each missing model
skipped / error: if credentials missing or API call failed
Supported providers
| Provider | Env Var | Auth |
|---|
| OpenRouter | (none ā public API) | None |
| OpenAI | OPENAI_API_KEY | Bearer |
| Anthropic | ANTHROPIC_API_KEY | x-api-key |
| Gemini API | GEMINI_API_KEY | Query param |
| Fireworks AI | FIREWORKS_API_KEY | Bearer |
| Together AI | TOGETHER_API_KEY | Bearer |
| SiliconFlow CN | SILICONFLOW_CN_API_KEY | Bearer |
| Cerebras | CEREBRAS_API_KEY | Bearer |
| Groq | GROQ_API_KEY | Bearer |
| Vertex AI | VERTEX_PROJECT_ID + gcloud CLI auth | OAuth (gcloud) |
Providers NOT covered by the script (check manually if needed)
Amazon Bedrock
Requires aws CLI configured. If not installed, skip.
aws bedrock list-foundation-models --output json | \
jq '[.modelSummaries[] | {modelId, status: .modelLifecycle.status, endOfLife: .modelLifecycle.endOfLifeTime}]'
Flag any model where status == "LEGACY" or endOfLifeTime is approaching.
LiteLLM Model DB (supplementary signal)
Cross-reference against LiteLLM's public model database for deprecation_date fields. This mostly covers Azure/OpenAI date-stamped models but is a useful secondary signal.
curl -s https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json | \
jq 'to_entries[] | select(.value.deprecation_date != null) | {model: .key, deprecation_date: .value.deprecation_date}'
Providers to skip
These don't have meaningful deprecation to check:
ollama ā local models, user-managed
kiln_fine_tune ā internal
kiln_custom_registry ā internal
openai_compatible ā user-configured
docker_model_runner ā local
azure_openai ā typically mirrors OpenAI, covered by OpenAI check
huggingface ā serverless inference, model availability varies
Phase 3 ā Report Findings
Read the JSON output from check_provider.py and present findings in a clear table to the user:
Deprecation Audit Results
=========================
ā REMOVED (model not found in provider's model list):
- ModelName.gemini_1_5_pro ā gemini_api (model_id: gemini-1.5-pro)
- ModelName.gemini_1_5_pro ā openrouter (model_id: google/gemini-pro-1.5)
ā ļø EXPIRING SOON (within 90 days):
- ModelName.claude_3_7_sonnet ā openrouter (model_id: anthropic/claude-3.7-sonnet, expires: 2026-05-05)
ā ļø LEGACY (Bedrock lifecycle status):
- ModelName.mistral_large ā amazon_bedrock (model_id: mistral.mistral-large-2407-v1:0, status: LEGACY)
ā
ALL CLEAR:
- openai: 20/20 models found
- anthropic: 8/11 models found (3 deprecated above)
āļø SKIPPED (no credentials):
- groq
- amazon_bedrock
Group by provider within each category. Use the entries_to_deprecate field from the JSON to get the ModelName enum for each entry.
Phase 4 ā Mark Deprecated
For each confirmed-removed model, set deprecated=True on the affected KilnModelProvider entry.
To find the line to edit, grep for the model_id in ml_model_list.py:
grep -n 'model_id="<model_id>"' libs/core/kiln_ai/adapters/ml_model_list.py
Then add deprecated=True, after the model_id= line in the matching KilnModelProvider block.
Rules:
- Only mark a provider deprecated if its model_id is confirmed missing from that provider's model list
- If ALL providers for a
KilnModel are deprecated, note this to the user ā they may want to consider removing the model entirely
- For "expiring soon" models, inform the user but don't mark deprecated yet ā let them decide
- For Bedrock
LEGACY status, mark deprecated (the model still works but is on its way out)
Ask the user to confirm before making changes. Present the list of changes and wait for approval.
Phase 5 ā Verify
After marking providers deprecated, run a quick sanity check:
grep -c "deprecated=True" libs/core/kiln_ai/adapters/ml_model_list.py
Ensure the count matches what you expect (previous count + newly marked).
Checklist