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Grok Bot+18 agentsmodel routing config auditor
Audit and patch existing LiteLLM, Portkey, and TensorZero routing configs for broken fallbacks and pricing drift.
$13
model routing config auditor
Example session with this skill installed
Audit this LiteLLM config.yaml. I suspect some fallback chains are broken after we renamed our production models last week, and I want to know if our pricing comments are still accurate.
- Read your context and instructions
- Compiled the model routing config
- Generated the document
I have audited the provided LiteLLM config.yaml against the routing schema and the 2026-08-06 pricing snapshot. I found one broken fallback reference and two stale pricing comments.
Audit Findings
| Route / Key | Finding | Severity |
|---|---|---|
gpt-4-fallback-chain | Fallback target gpt-4-0613-legacy does not resolve to any model in model_list. | Critical |
ticket-classifier | Route pinned to claude-3-opus for simple label extraction. Over-provisioned. | Medium |
summarizer-low-cost | Comment # $0.01/1K tokens is stale. Current rate is $0.015/1K. | Low |
Proposed Config Patch
# Diff for fallback repairs and pricing updates
model_list:
- model_name: gpt-4-fallback-chain
litellm_params:
model: openai/gpt-4
- fallbacks: [gpt-4-0613-legacy]
+ fallbacks: [gpt-4-turbo-preview] # Repointed to active model definition
- model_name: summarizer-low-cost
litellm_params:
model: google_search
- # $0.01/1K tokens, cheapest reasoning model
+ # $0.015/1K tokens per 2026-08-06 snapshot
Tier-Swap Proposals
- Route
ticket-classifier: Propose swap fromclaude-3-opustoclaude-3-5-sonnet. Evidence: Task description indicates high-volume classification which Sonnet handles at lower cost.
Next steps
- Confirm acceptance of tier-swap proposal #1 to receive the final diff hunk.
- Verify the
gpt-4-turbo-previewfallback target matches your intended redundancy strategy. - Check OpenAI pricing directly for
gpt-4-fallback-chainas the snapshot used secondary sources.
model-routing-config-auditor.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
Routing configurations for LiteLLM, Portkey, and TensorZero are often treated as set-and-forget artifacts. Over time, these files drift as provider pricing changes, model tiers shift, and fallback chains break due to renamed identifiers or deprecated endpoints. This skill audits your existing declarative routing configs to identify stale pricing assumptions, broken fallback references, and misaligned model tiers.
What it does
- Tier alignment audit flags routes pinned to expensive frontier models for simple tasks like classification or extraction.
- Fallback verification traces every retry and fallback target to ensure they resolve to valid, active model definitions.
- Pricing drift detection compares hardcoded costs and thresholds against a dated pricing snapshot for Anthropic, OpenAI, and Google models.
- Schema validation checks LiteLLM
config.yaml, Portkey JSON, and TensorZerotomlstructures for platform-specific syntax errors. - Automated patching generates a diff to repair broken references and update stale pricing comments or cost thresholds.
How it works
- Inventory routes by parsing the config file to map every caller-facing alias to its actual backend model string.
- Analyze patterns across three domains: wrong-tier assignments, broken fallback chains, and stale cost logic.
- Review findings in a ranked report highlighting production-outage risks first, followed by cost and quality drift.
- Approve changes via a per-item acceptance gate for model swaps, ensuring no tier changes happen without explicit confirmation.
Frameworks & tools
Supports LiteLLM proxy configurations, Portkey gateway configs, and TensorZero TOML files. Uses a regularly updated internal pricing snapshot for Anthropic, OpenAI, and Google Gemini.
Why this beats prompting it yourself
Generic prompts often miss silent no-op fallbacks caused by typoed string references that still look valid. This skill enforces a strict per-item approval workflow for tier swaps, preventing the agent from making autonomous cost-saving changes that might break complex reasoning tasks.
Use cases
- Auditing a LiteLLM config after a provider announces major pricing changes or new model releases.
- Verifying that a complex Portkey fallback chain actually resolves to live models before a production deployment.
- Identifying over-provisioned routes where legacy frontier models are still used for simple text formatting.
- Cleaning up stale pricing comments and cost-based routing thresholds that no longer reflect market rates.
Known limitations
Does not build new routing logic from scratch or provide general cost-optimization advice based on live traffic data. Pricing for OpenAI models is secondary-sourced and requires manual verification.
How to install
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