Market Access Value

v1.0.0

Use market access value for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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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
high confidence
Purpose & Capability
Name/description describe a market-access/value proposition writer and the package contains a single Python script (scripts/main.py) that generates a value-proposition text file — the requested resources (none) and provided implementation align with the stated purpose.
Instruction Scope
SKILL.md focuses on validating inputs and running the packaged Python script (python scripts/main.py). Minor inconsistencies: SKILL.md mentions editing an in-file CONFIG block and references 'Python/R scripts' in places, but only a single Python script is present and it has no CONFIG block. Otherwise instructions do not ask for unrelated files, credentials, or network calls.
Install Mechanism
No install spec is provided (instruction-only with an included script). There is no download-from-URL, package installation, or archive extraction; risk from installation is minimal.
Credentials
The skill declares no required environment variables or credentials and the code does not access environment variables or external services. Requested environment access is proportional to the task.
Persistence & Privilege
The skill does not request always:true, does not modify other skills or agent-wide settings, and only writes a local output file as part of normal operation.
Assessment
This skill appears coherent and low-risk, but take these practical precautions before running it: 1) Inspect scripts/main.py (already included) to confirm behavior — it prints and writes a plain text file and does not make network calls or read credentials. 2) Run the quick checks suggested in SKILL.md (python -m py_compile scripts/main.py and python scripts/main.py --help). 3) When running, provide a non-sensitive output path (use --output) to avoid accidental overwrite of important files. 4) Do not feed confidential or regulated data unless you run the script in an isolated/sandboxed environment and you accept local file-write behavior. 5) Note minor documentation mismatches (mentions of an in-file CONFIG and 'R' scripts) — confirm there are no additional hidden entry points before trusting this package for automated workflows. Finally, the included audit_result.json reports a prior evaluation; do not rely solely on it — re-run the provided checks in your environment.

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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