Laporan audit skill
quant-experiment-runtime Laporan audit.
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).
Trust Score OpenAgentSkill
Trust Score OpenAgentSkill
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
Adopsi GitHub
Info62
212 star GitHub
Aktivitas star/fork
Peringatan51
212 star dan 3 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Pemeliharaan terbaru
Lulus88
1 bulan sejak push
Kejelasan lisensi
Lulus86
Apache-2.0
Kelengkapan README/SKILL.md
Lulus86
Metadata memuat konteks penggunaan dan alur kerja yang cukup
Risiko dependensi/runtime
Peringatan54
command execution surface, external package install surface
Ketersediaan pemasangan
Lulus92
npx skills add CamusGIT/EvoQuant --skill quant-experiment-runtime
Keamanan perintah pemasangan
Lulus92
Jalur pemasangan paket atau runtime standar
Cakupan izin
Peringatan50
shell or command execution, filesystem or document access
Bukti repositori
Lulus86
https://github.com/CamusGIT/EvoQuant/tree/main/EvoQuant/skills/quant-experiment-runtime
Status peninjauan
Info66
Data tinjauan AI tersedia
Hasil terbukti Agent
Info54
Belum ada data hasil Agent
Pemeriksaan
Tinjauan pemasangan dan adopsi
Jalur pemasangan
92
npx skills add CamusGIT/EvoQuant --skill quant-experiment-runtime
Repositori
88
https://github.com/CamusGIT/EvoQuant/tree/main/EvoQuant/skills/quant-experiment-runtime
Lisensi
86
Apache-2.0
Pemeliharaan
88
1 bulan sejak push
Tinjauan AI
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.
Kelengkapan README/SKILL.md
86
Usable description available
Risiko dependensi
54
command execution surface, external package install surface
Keamanan perintah pemasangan
92
Jalur pemasangan paket atau runtime standar
Cakupan izin
50
shell or command execution, filesystem or document access
Aktivitas star/fork
51
212 star dan 3 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Adopsi
68
212 star GitHub
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Peringatan
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- 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.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: shell or command execution, filesystem or document access
- Stars/forks activity: 212 stars, 3 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, external package install surface
- Permission surface: shell or command execution, filesystem or document access
Metode
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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