Validator Correlated Judgment
v1.1.0Helps identify when multiple attestation validators share training data, model architecture, or organizational upstream — causing correlated blind spots that...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description describe analysis of validator correlation; declared requirements (curl, python3) are reasonable for fetching attestations and running statistical or trace-comparison code. No credentials, config paths, or unrelated binaries are requested.
Instruction Scope
SKILL.md limits inputs to validator provenance, attestation results, behavioral tests, or evaluation traces. It does not instruct reading arbitrary system files or environment variables beyond user-provided inputs. The analysis methods described (provenance overlap, behavioral correlation, trace similarity) are coherent with the stated goals.
Install Mechanism
Instruction-only skill with no install spec and no code files — lowest install risk. It expects existing curl and python3 on PATH; nothing is downloaded or written by the skill itself.
Credentials
No environment variables, credentials, or config paths are required. The lack of secrets requested is proportionate to an analysis/reporting tool that operates on user-supplied data.
Persistence & Privilege
always is false and autonomous invocation is allowed (platform default). The skill does not request permanent presence or modify system/agent-wide settings; no elevated persistence is claimed.
Assessment
This skill appears coherent and low-risk, but before using it: (1) Only supply attestation traces, logs, or datasets you are permitted to share — evaluation traces and provenance can contain sensitive or proprietary information. (2) If you plan to run behavioral tests against third-party validators, verify their terms of service and rate limits; automated probing can be disallowed. (3) Because the skill uses curl/python3, watch for any network requests you didn't expect (inspect prompts/commands if running interactively or review audit logs if automating). (4) If you need higher assurance, run the skill in a sandboxed environment and review any outputs for sensitive data exfiltration before sharing them further.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.
Runtime requirements
🧠 Clawdis
Binscurl, python3