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- π Model Migration Checker
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π Model Migration Checker
Review model-version coupling in code, or compare reviewed deployments against exact, dated provider/platform/endpoint migration rules.
$15
π Model Migration Checker
Example session with this skill installed
Run the included risky-sample fixture with Model Migration Checker v1.1.0 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.
What you get
About this skill
Two local review modes
Model Migration Checker package v1.1.0 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
Downloadable skill, not a hosted app. Python 3.10+, standard library only; use a compatible agent with local execution or a terminal. Extract the complete folder. 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 install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
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Verified clean 13 days ago
- Passed all security checks, Safe to install