Tone Adjuster

v1.0.0

Use when converting medical text between academic and patient-friendly tones, translating medical jargon for patients, adapting research papers for public au...

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byAIpoch@aipoch-ai
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
high confidence
Purpose & Capability
The name and description (converting medical text between academic and patient-friendly tones) match the included Python implementation (ToneAdjuster) and packaged reference material. The script contains a jargon dictionary and conversion/assessment functions that align with the stated purpose.
Instruction Scope
Runtime instructions are focused on validating inputs and running the bundled script. Minor documentation mismatches: SKILL.md's Quick Start imports 'scripts.tone_adjuster' although the provided module file is scripts/main.py, and the README references editing an in-file 'CONFIG' block that doesn't exist in the code. These are documentation inconsistencies but not evidence of malicious behaviour.
Install Mechanism
No install spec or external downloads are present; the skill is instruction-only with a single local Python script. This is low-risk from an install perspective.
Credentials
The skill requests no environment variables, credentials, or config paths. The code does not access environment variables or network resources, so requested access is proportionate to purpose.
Persistence & Privilege
The skill is not always-enabled and has no special privileges. It does not modify other skills or system-wide settings.
Assessment
This package appears to implement a local text conversion tool and is coherent with its description. Before installing or running it: (1) review the small documentation mismatches (Quick Start import path vs. scripts/main.py) and correct the invocation; (2) run python -m py_compile scripts/main.py and inspect the output on non-sensitive sample text; (3) because the skill deals with medical text, avoid feeding protected health information (PHI) into it unless you trust the runtime environment — although the code has no network calls, always confirm your execution environment's policies; (4) if you will run it in production, consider adding pinned dependency versions and unit tests, and correct the SKILL.md inconsistencies. Overall: low-risk but verify the minor documentation issues before trusting it with sensitive data.

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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