OSTEO-GC: Glucocorticoid-Induced Osteoporosis Modeling

v1.0.0

Models BMD T-score trajectories and 10-year fracture risk in chronic glucocorticoid users with uncertainty and ACR 2022 osteoporosis treatment guidance.

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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Benign
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Benign
medium confidence
Purpose & Capability
Name, description, SKILL.md usage example, and the included python module all align: a Monte Carlo T-score/fracture-risk projector that embeds ACR-style guidance. Required binaries/env/config are none, which is proportionate for a pure-Python modelling utility.
Instruction Scope
SKILL.md instructs only local usage (importing functions, calling project_tscore, printing report). It does not instruct reading unrelated system files, accessing environment variables, or sending data externally in the visible instructions.
Install Mechanism
No install spec (instruction-only) and the code declares only Python standard-library dependencies. No downloads or archive extraction are requested.
Credentials
The skill declares no environment variables or credentials and the visible code does not reference os.environ, secrets, or any external service credentials—appropriate for the stated purpose.
Persistence & Privilege
always is false and the skill is user-invocable (normal); there are no instructions or code that attempt to modify other skills or system-wide configurations in the reviewed portions.
Assessment
What to check before installing: 1) Review the complete osteo_gc.py file (the provided snippet was truncated) to confirm there are no hidden network calls, file writes, subprocesses, or use of environment variables at the end of the file. 2) Run a local static scan (look for imports like requests, urllib, socket, subprocess, os.environ, open) and run the code in an isolated environment with non-identifiable test data. 3) Do not use outputs as sole clinical decision-making—this appears to be a modeling aid and should be validated clinically against known cases and local guidelines. 4) Confirm license/attribution and validate the clinical assumptions (dose-response, FRAX approximations, treatment effect sizes) match your institutional practice before acting on recommendations.

Like a lobster shell, security has layers — review code before you run it.

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License

MIT-0
Free to use, modify, and redistribute. No attribution required.

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