Rapport d’audit du skill
quant-experiment-runtime Rapport d’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.
Adoption GitHub
Info62
212 stars GitHub
Activité stars/forks
Avertissement51
212 stars et 3 forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles
Maintenance récente
Validé88
1 mois depuis le dernier push
Clarté de licence
Validé86
Apache-2.0
Complétude README/SKILL.md
Validé86
Les métadonnées incluent suffisamment de contexte d’usage et de workflow
Risque dépendances/runtime
Avertissement54
command execution surface, external package install surface
Disponibilité de l’installation
Validé92
npx skills add CamusGIT/EvoQuant --skill quant-experiment-runtime
Sécurité de la commande d’installation
Validé92
Chemin d’installation standard de package ou runtime
Surface de permissions
Avertissement50
shell or command execution, filesystem or document access
Preuve du dépôt
Validé86
https://github.com/CamusGIT/EvoQuant/tree/main/EvoQuant/skills/quant-experiment-runtime
État de revue
Info66
Données de revue IA disponibles
Résultats prouvés par Agent
Info54
Pas encore de données de résultats Agent
Vérifications
Revue d’installation et d’adoption
Chemin d’installation
92
npx skills add CamusGIT/EvoQuant --skill quant-experiment-runtime
Dépôt
88
https://github.com/CamusGIT/EvoQuant/tree/main/EvoQuant/skills/quant-experiment-runtime
Licence
86
Apache-2.0
Maintenance
88
1 mois depuis le dernier push
Revue IA
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.
Complétude README/SKILL.md
86
Usable description available
Risque de dépendances
54
command execution surface, external package install surface
Sécurité de la commande d’installation
92
Chemin d’installation standard de package ou runtime
Surface de permissions
50
shell or command execution, filesystem or document access
Activité stars/forks
51
212 stars et 3 forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles
Adoption
68
212 stars GitHub
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Avertissements
- 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
Méthode
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.
Comparer les options proches
Skills associés à auditer ensuite
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K Stars · Rapport d’audit
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
64K Stars · Rapport d’audit
Academic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38K Stars · Rapport d’audit