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v1.0.0Rigorous comparisons with confidence parity, weighted criteria, and research depth tracking.
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byIván@ivangdavila
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 (rigorous comparisons) aligns with the included files and runtime instructions. The skill only references local domain defaults, confidence rules, traps, and preference tracking — all consistent with producing balanced comparisons.
Instruction Scope
Instructions are explicit and scoped to: (a) load domains.md, (b) consult user preferences from memory, (c) research both items to equal depth, (d) compute weighted scores, and (e) update preferences.md. This is coherent. Note: the skill expects the agent to perform external research (web/API calls) and to read/merge agent memory; both are necessary for the stated goal but mean the agent will fetch external sources and access stored preferences. It also writes to preferences.md to persist learned priorities.
Install Mechanism
Instruction-only skill with no install spec, no binaries, and no code files. Lowest-risk install profile.
Credentials
Requires no environment variables, no credentials, and no system config paths. The declared requirements are minimal and appropriate for the function.
Persistence & Privilege
always:false (normal). The skill writes/updates its local preferences.md and reads agent memory — reasonable for learning user preferences but worth noting because it persists learned settings and reads stored memory data.
Assessment
This skill appears internally consistent and safe to install, but consider these practical points before using it: (1) it will perform external research (web/API calls) to collect sources — avoid giving it items that require confidential or proprietary research unless you trust the agent's browsing/data-access settings; (2) it reads agent memory to overlay user preferences, so review what is stored in memory if you care about privacy; (3) it updates preferences.md to persist learned priorities — if you don’t want persistent preference changes, back up or inspect that file after runs; (4) pay attention to the sources it cites (quality and recency) — the skill emphasizes parity, but quality of sources still matters; (5) if you prefer to approve each run, restrict autonomous invocation or call the skill only when needed. Overall: coherent and proportional to its 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.