Skill audit report
Quant research experiment executor: discover an offline source database under the workdir's code-repo, build a panel, run a Research Artifact's entry point to compute research-object values, and evaluate IC/ICIR/RANKIC/coverage metrics. Runtime = Experiment Executor; it runs a Research Artifact via a Python-native Entry Point and is agnostic to research-object type and expression form. Self-contained: panel building and IC metrics are implemented inside this skill. The dataset is identified at runtime by discover_data.py (never hard-coded). Use when: a research proposal is ready and you need to actually run the proposed factor/method on real data and get quantitative metrics. Do NOT use for: designing which experiments to run (use experiment-pipeline / paper-planning), debugging a single failed experiment (use experiment-craft), or searching papers (use local-paper-navigator).
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO62
212 GitHub stars
Stars/forks activity
WARN51
212 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
9d since push
License clarity
PASS86
Apache-2.0
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
WARN54
command execution surface, external package install surface
Install availability
PASS92
npx skills add CamusGIT/EvoQuant --skill quant-experiment-runtime
Install command safety
PASS92
standard package or runtime install path
Permission surface
WARN50
shell or command execution, filesystem or document access
Repository evidence
PASS86
https://github.com/CamusGIT/EvoQuant/tree/main/EvoQuant/skills/quant-experiment-runtime
Review status
INFO66
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add CamusGIT/EvoQuant --skill quant-experiment-runtime
Repository
88
https://github.com/CamusGIT/EvoQuant/tree/main/EvoQuant/skills/quant-experiment-runtime
License
86
Apache-2.0
Maintenance
100
9d since push
AI review
55
The skill executes arbitrary code from LLM-generated research artifacts, which inherently requires a secure sandbox environment. The documentation mentions the EvoQuant sandbox but does not explicitly detail sandboxing constraints or risk mitigations within the skill itself.
Warnings
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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README/SKILL.md completeness
86
Usable description available
Dependency risk
54
command execution surface, external package install surface
Install command safety
92
standard package or runtime install path
Permission surface
50
shell or command execution, filesystem or document access
Stars/forks activity
51
212 stars, 3 forks; issue activity unavailable in current metadata
Adoption
68
212 GitHub stars
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
Shell or command execution
highSkill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
Browser automation
mediumSkill may drive a browser or interact with web pages.
Network access
mediumSkill likely fetches remote pages, APIs, repositories, or external services.
Filesystem access
mediumSkill may read or write project files, documents, generated artifacts, or local workspace state.