Skill audit report
Owns data understanding BEFORE any model is designed. Places and executes `data/eda.py` (a jupytext `# %%` script) via the shared in-process runner, reads the streamed digest, then writes a persisted `data/eda.md` report (plus linked `data/eda_<table>.html` skrub `TableReport` pages) and the `## Data understanding (EDA)` section of `journal/JOURNAL.md`. The point is to surface the dataset facts — shape, dtypes, missingness, cardinality, target balance / skew, datetime / group structure, feature associations — that JUSTIFY the later learner / splitter / metric decisions, so the user understands *why* the modelling choices are made. Uses `skrub.TableReport` for dataframe overviews and the shared runner `audit-ml-pipeline/scripts/run_cells.py`. Stops at "EDA executed, `data/eda.md` + HTML written, JOURNAL EDA section updated." Never designs the model, never edits `src/<pkg>/`, never modifies the user's raw data files. TRIGGER — any of: - `iterate-ml-experiment` § 0 bootstrap, BEFORE the b
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO62
119 GitHub stars
Stars/forks activity
WARN51
119 stars, 7 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
30d since push
License clarity
PASS86
BSD-3-Clause
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
INFO64
credential or environment access, network or browser surface
Install availability
PASS92
npx skills add probabl-ai/skills --skill explore-ml-data
Install command safety
PASS92
standard package or runtime install path
Permission surface
WARN46
secrets or environment access, filesystem or document access
Repository evidence
PASS86
https://github.com/probabl-ai/skills/tree/main/skills/explore-ml-data
Review status
PASS88
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add probabl-ai/skills --skill explore-ml-data
Repository
88
https://github.com/probabl-ai/skills/tree/main/skills/explore-ml-data
License
86
BSD-3-Clause
Maintenance
100
30d since push
AI review
88
Approved with no listed issues
README/SKILL.md completeness
86
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.
Compare nearby options
Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
16K Stars · Audit report
🕵️♂️ Collect a dossier on a person by username from 3000+ sites
33K Stars · Audit report
Nuclei is a fast, customizable vulnerability scanner powered by the global security community and built on a simple YAML-based DSL, enabling collaboration to tackle trending vulnerabilities on the internet. It helps you find vulnerabilities in your applications, APIs, networks, DNS, and cloud configurations.
29K Stars · Audit report
Usable description available
Dependency risk
64
credential or environment access, network or browser surface
Install command safety
92
standard package or runtime install path
Permission surface
46
secrets or environment access, filesystem or document access
Stars/forks activity
51
119 stars, 7 forks; issue activity unavailable in current metadata
Adoption
68
119 GitHub stars
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
The tracked source changed or could not be synchronized. Review the current source before installing.
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.
Secrets or environment access
highSkill metadata references credentials, tokens, environment variables, or secret-bearing workflows.