model-management
Manage the lifecycle of AI models from integration to empirical testing.
Install
mkdir -p .claude/skills/model-management && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4590" && unzip -o skill.zip -d .claude/skills/model-management && rm skill.zipInstalls to .claude/skills/model-management
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Add, update, or remove text/image/video/audio/embeddings models. Covers the full lifecycle: files to touch, what to verify, and how to test empirically before merging.Key capabilities
- →Audit field-parity for model updates
- →Execute empirical model performance matrices
- →Manage model slug and alias mappings
- →Verify pricing and provider configuration
- →Generate PR checklists for model lifecycle changes
How it works
Follows a defined checklist of verification steps and test matrices to ensure model parity before committing provider changes to production.
Inputs & outputs
When to use model-management
- →Adding a new AI model provider
- →Verifying model pricing or capabilities
- →Updating model aliases
- →Testing model parity
About this skill
Model management
Use this workflow for every model change. Keep the implementation minimal, preserve the public contract unless the user explicitly approves a change, and prove provider behavior with real requests.
Load context first
- Read the repository
AGENTS.md. - Read the live registry entry, runtime config, handler, schemas, and tests. The repository is the source of truth for what Pollinations currently offers.
- Read the relevant local active plan when present:
temp/manage_inference.mdtemp/manage_inferenceport.mdtemp/manage_gpus.mdtemp/manage_azure_limits.md
- Check GitHub for open or merged PRs that may already implement or conflict with the work.
- Check current official provider documentation, catalog, pricing, availability, quotas, and deprecation notices. Then probe the exact route; the live response wins over documentation.
The temp plans are ignored operational state, not repository truth. When working in a linked worktree where they are absent, locate the primary checkout with git worktree list and read them there. Never copy balances, prices, PR statuses, quotas, or candidate rankings into this skill.
Read operating-policy.md before recommending a route or model. Read only the other references needed for the task:
| Task | Required references |
|---|---|
| Find code or run locally | repository-and-local-testing.md |
| Add, update, reroute, rename, or remove | change-and-test-matrix.md |
| New model, provider, model ID, or price | billing-verification.md |
Mandatory confirmation gate
Do not edit any model until the user confirms its complete business and inference contract. Inspect the code and provider first; do not ask the user to discover values for you.
Show one complete row per model:
| Field | Required value |
|---|---|
| Canonical name | Public model ID after the change |
| Aliases | Every compatibility alias, or none |
priceMultiplier | Exact multiplier after provider cost |
paidOnly | Whether purchased pack balance is required |
| Pollinations GPU | yes only if Pollinations operates the production hardware |
| Registry provider | Configured primary provider |
| Primary route | Provider, deployment/host, and exact upstream model ID |
| Pollinations fallback | Complete alternative route or none |
Ask:
Please confirm: canonical name X, aliases A/none, price multiplier M, paid-only yes/no, Pollinations GPU yes/no, registry provider P, primary route R, and Pollinations fallback F/none. Are all of these correct?
An answer approves only the values shown. If a value is unknown, inferred, conflicting, or route-dependent, label it UNKNOWN, explain the evidence, and wait for that exact decision. A batch approval is valid only when every row is complete.
Public API changes require separate confirmation
Model approval does not authorize adding, renaming, removing, or changing a public endpoint, method, transport, request or response schema, streaming behavior, or event protocol. Do not propose an API-surface change without a concrete user or developer problem it solves.
Before editing, present:
- the user/developer problem and the current public contract;
- the exact proposed routes, methods, transports, schemas, streaming behavior, and events;
- the compatibility reference, resolved by the order in step 4;
- whether the change is additive, behavioral, deprecating, or breaking, and the affected clients;
- the migration, coexistence, and removal plan, or
none.
State plainly: This adds/changes the public API: ... Then ask for explicit confirmation of that exact API change. If the problem, standard, or compatibility impact is unclear, do not edit.
Secrets are a separate approval
Model approval never authorizes adding, rotating, synchronizing, deploying, revoking, or otherwise mutating a credential. Follow the exact approval wording, dedicated-PR requirement, execution order, verification, and rollback rules in AGENTS.md. Do not duplicate or weaken that process here.
Workflow
1. Reconcile current state
- Resolve aliases to the canonical registry entry.
- Before any canonical rename, count production and staging API keys whose
permissions.modelscontains the old canonical ID. Registry aliases do not preserve restricted-key access because authorization compares the resolved canonical ID. - Audit every modality registry and every model change merged to
mainsince the current production revision; production can lag behindmain. - Trace every reachable runtime route and any configured fallback.
- Distinguish the configured provider from the provider that served an observed request.
- Compare the intended change with open PRs and active plan entries.
- Remove shipped work from active plans after production verification; do not keep a completed archive.
2. Research the exact route
- Use official provider/model sources for release, model identity, pricing, regions, preview status, quotas, rate limits, context, inputs, outputs, tools, caching, and deprecation.
- Deduplicate the canonical model across providers.
- List material route differences. Equal model names do not prove equal capabilities.
- Probe the exact deployment and request shape Pollinations will use.
- Inspect provider-managed routing/fallback defaults and controls. Report identity, capability, pricing, residency, and observability tradeoffs.
3. Confirm the contract
Present the mandatory row and obtain explicit confirmation before editing. If a capability or access change is intentional, state it plainly.
4. Implement the smallest complete change
- Reuse existing handlers, transforms, provider configs, schemas, and generic fallback infrastructure.
- Do not add speculative abstractions, compatibility shims, or fallbacks.
- Expose a confirmed new public capability (per the API-change confirmation above) through two surfaces backed by one implementation: a Pollinations-native route outside
/v1and a standard-compatible route under/v1. - Resolve the compatibility contract in this order: (1) current official OpenAI API; (2) if OpenAI defines no equivalent, the current published OpenRouter contract — a protocol-design reference here, not an inference-provider fallback; (3) if neither defines the capability, stop for an explicit API-contract decision. Document the exact reference checked.
- Treat
/v1as a compatibility namespace: match the selected standard's route, transport, request, response, streaming, and event contracts exactly, and keep provider-specific protocols behind the route adapters — never a Pollinations-specific or upstream-provider schema under/v1. - Prefer the selected standard's schema on the Pollinations-native route too; deliberate native divergence requires its own explicit API-change confirmation.
- Do not collapse capabilities with materially different inputs, outputs, or transports into one endpoint merely by switching
model. Keep distinct operations separate while reusing their shared internal handler, authorization, billing, and observability paths. - Treat aliases as identity-only: resolve to the canonical model, then discard the requested alias for behavior. Never infer parameters from alias spelling such as
-high,-search,-reasoning, or-1080p; only explicit request parameters and canonical defaults apply. Keep a separate canonical model if the old behavior must remain. - Use the resolved registry entry for canonical model identity in generic handlers. Never maintain handler-level lists of model IDs for response, tracking, billing, or routing behavior.
- Canonicalize all stale stored aliases found by the same registry-wide audit in one D1 migration PR. Replace old IDs, preserve unrelated permission fields and array order, deduplicate old/new pairs, prove idempotence, and verify all audited old-ID counts are zero after deployment.
- Keep every migration statement within D1's per-query CPU limit: one statement
per alias, and prefilter with
instr()insideCASEso JSON functions never run on non-matching rows. A single whole-table JSON scan fails with error 7429 at production scale (~150k apikey rows). - API-key create and update paths must store recognized aliases as canonical IDs, while preserving unknown and community IDs, so migrations do not need to repair newly written aliases again.
- For a pending canonical rename, merge the migration before the model PR but do not promote the replacement-only migration while production still resolves the old ID. Promote a revision containing both changes so D1 runs immediately before the Worker deploy. Keep mappings in migrations only; do not add a runtime normalization layer.
- Update every consumer of a changed public ID at once.
- Keep one PR per model or tightly coupled model-family change.
- Never edit generated
APIDOCS.md; update the source schema or route.
5. Verify end to end
- Run the relevant rows in change-and-test-matrix.md.
- For new models and provider/model-ID changes, run the full declared-modality matrix and billing-verification.md.
- Test aliases, permissions, errors, caching, capacity, and
/modelsmetadata. - Verify all media is fully returned within the supported synchronous time budget.
- Record exact evidence and uncertainty in the PR.
6. Open the PR
Before publishing:
- Format changed files with the repository formatter.
- Run focused tests and type checks for every touched service.
- Review the complete diff for unrelated changes and dead code.
- Include the approved contract, exact provider/model ID, pricing source, live probes, E2E results, billing evidence, capacity results, limitations, an
Content truncated.
When not to use it
- →When deploying services directly to production
- →For investigating model-internal runtime errors
Prerequisites
Limitations
- →Requires manual execution of empirical tests
- →Limited to tracking model registration and parity
- →Not a replacement for real-time model debugging
How it compares
It replaces ad-hoc testing with a standardized lifecycle process, reducing the risk of regression in AI model integrations.
Compared to similar skills
model-management side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| model-management (this skill) | 1 | 2mo | Caution | Intermediate |
| llama-factory | 15 | 8mo | No flags | Advanced |
| senior-prompt-engineer | 7 | 7mo | Review | Advanced |
| dspy | 4 | 7mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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