More screenshots
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+20 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+17 agents🔁 Model Migration Checker
Review model-version coupling in code, or compare reviewed deployments against exact, dated provider/platform/endpoint migration rules.
$15
🔁 Model Migration Checker
Example session with this skill installed
Run the included risky-sample fixture with Model Migration Checker v1.1.1 and explain what needs review without assuming its parameters are deprecated.
- Read your context and instructions
- Compiled the model migration checker
Packaged fixture run, not customer evidence
Command: python scripts/scan_model_migration.py fixtures/risky-sample
The run returns 11 findings. All locations below are in fixtures/risky-sample/client.py.
- MMC001, medium, lines 1, 2, 3 and 25: four hardcoded model-name/version findings.
- MMC003, medium, line 4: hardcoded token/context-window limit assumption.
- MMC006, low, lines 5 and 27: two per-token cost constant findings.
- MMC002, medium, line 14: max_tokens parameter coupling, REVIEW_CANDIDATE.
- MMC002, medium, line 15: functions parameter coupling, REVIEW_CANDIDATE.
- MMC005, low, line 15: tool/function-schema format coupling.
- MMC004, low, line 28: response parsing tied to one output shape.
Interpretation
Deprecation is not confirmed without provider/platform/endpoint evidence. Coverage is incomplete; environment files are excluded. Review relevant call sites and source documentation before changing them. The separate scoped-rule mode can evaluate supplied dated rules against reviewed deployments, but this fixture run does not execute that mode or test a replacement model.
Connects securely to your tools. The creator never sees your data.
About this skill
Two local review modes
Model Migration Checker package v1.1.1 includes a heuristic scanner and a separate opt-in scoped-rule evaluator. The scanner flags model strings, parameter coupling, token-limit assumptions, response parsing, tool schemas and cost constants. Bare parameter matches are review candidates, not confirmed deprecation.
The evaluator compares reviewed deployment records against dated, source-linked rules you supply. Results distinguish APPLIES, DOES_NOT_APPLY and UNKNOWN_SCOPE. It checks scope and freshness but does not authenticate sources or bundle a universal current deprecation database.
Before you buy
Agent-loaded skill, not a hosted app. Requires Python 3.10+ using the standard library and an agent host that can retrieve supporting files and run Python. Agensi supplies access, not execution. Extract the complete folder only for a separately obtained download. Supply reviewed non-secret input. Neither mode calls models, rewrites code or proves migration safety.
First run
python scripts/scan_model_migration.py fixtures/risky-sample
Review the sample, then substitute your approved source folder. For the separate scoped mode, read the included scoped-rules reference. Missing, linked or unreadable scanner input produces CANNOT_ASSESS.
What changed
Scoped rules, freshness checks and explicit unknowns; removal of universal target/deprecation claims; stricter input safety. Regression evaluation remains separate and requires actual observations.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill goes through the trust checks below.
Security scanned
Checked by our 8-point scan before listing
Fresh listing
Recently published to Agensi
Money-back guarantee
Doesn't do what its page promises? You get your money back.
Trust & safety
Security scan
Security scan passed with 2 warnings 6 days ago
Requires