Self Aware Prediction System

v1.0.0

Provides predictions with quantified uncertainty by evaluating data completeness, prediction type, and confidence to inform decision-making risks.

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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
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Benign
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Benign
high confidence
Purpose & Capability
Name/description (quantifying prediction uncertainty) matches the included docs and the single assessment script. There are no unrelated requirements (no credentials, binaries, or config paths).
Instruction Scope
SKILL.md stays on-topic (assess information completeness, prediction type, run the uncertainty assessment). It does not instruct reading unrelated files, exfiltrating data, or contacting external endpoints.
Install Mechanism
No install spec (instruction-only skill aside from a small included script). Nothing is downloaded or written to disk at install time by the skill itself.
Credentials
The skill declares no required environment variables, credentials, or config paths. The included Python script is local and does not access environment variables or external services.
Persistence & Privilege
always is false, the skill does not request persistent/system-wide changes or elevated privileges, and it does not modify other skills' configurations.
Assessment
This skill appears internally coherent and low-risk: it contains documentation and a small local Python function that computes a simple confidence score based on declared inputs. Before installing or enabling for automated use, consider: 1) test the script on representative inputs to confirm its behavior and that its simple heuristics match your needs (it uses a fixed base score and static weights); 2) verify there are no hidden files or later updates that introduce network calls or credential usage; 3) if you plan to supply sensitive content to the skill, ensure your agent's privacy settings are appropriate — the skill itself does not exfiltrate data but your agent may log or route inputs elsewhere; 4) if you need stronger uncertainty quantification, review or replace the heuristic with a more rigorous/statistical method. Overall, nothing in the package requests disproportionate access or contradicts its described purpose.

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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