Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add khalilbenaz/claude-skills-collection --skill data-governance-guidegit clone --depth 1 https://github.com/khalilbenaz/claude-skills-collectionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/data-governance-guide)<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/data-governance-guide"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/data-governance-guide/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/data-governance-guide"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/data-governance-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.02447 |
| Opus 5 | $0.00028 | $0.01223 |
| Sonnet 5 | $0.00011 | $0.00489 |
| Haiku 4.5 | $0.00006 | $0.00245 |
Grade A, and why
data-governance-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Governance Guide
Étape 1 — Poser le cadre organisationnel
Avant tout outil, définir les rôles et la charte de gouvernance.
| Rôle | Périmètre | Exemple concret |
|---|---|---|
| Data Owner | Responsabilité métier & budget | CFO pour les données financières |
| Data Steward | Qualité, définitions, règles | Analyste BI du domaine |
| Data Custodian | Stockage, sécurité, accès technique | DBA / Data Engineer |
| Data Consumer | Utilisation conforme aux politiques | Analyste, Data Scientist |
Charte minimale (artefact à créer dès le kick-off) :
1. Périmètre couvert (domaines métier)
2. Instances de décision (Data Governance Council — cadence mensuelle)
3. Processus d'escalade qualité
4. Politique de classification des données
5. Indicateurs de succès (KPIs)
Critère de passage : chaque dataset critique a un Data Owner nommé et joignable.
Étape 2 — Déployer le Data Catalog
Choix de l'outil selon le contexte :
| Contexte | Outil recommandé |
|---|---|
| Open-source / budget limité | DataHub (LinkedIn), Apache Atlas |
| Cloud AWS | AWS Glue Data Catalog + DataHub |
| Cloud Azure | Microsoft Purview |
| Cloud GCP | Dataplex |
| Enterprise avec budget | Collibra, Alation |
| Stack dbt | dbt Docs + Elementary |
Ingestion automatique avec DataHub :
pip install acryl-datahub
# Crawler PostgreSQL
datahub ingest -c postgres_recipe.yaml
# postgres_recipe.yaml
source:
type: postgres
config:
host_port: "localhost:5432"
database: "my_db"
username: "datahub"
password: "${POSTGRES_PASSWORD}"
include_tables: true
profile_patterns:
allow: ["public.*"]
sink:
type: datahub-rest
config:
server: "http://datahub-gms:8080"
Métadonnées minimales obligatoires par dataset :
- Description métier (en langage non-technique)
- Data Owner + Data Steward
- Niveau de classification (public / interne / confidentiel / restreint)
- Schéma avec descriptions des colonnes
- Fréquence de mise à jour + SLA de fraîcheur
- Tags domaine métier
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 307 lines · 56 tokens per session scan A 3a6f82b8aa40
data-governance-guide is a skill published in the GitHub repository khalilbenaz/claude-skills-collection (22 stars, last pushed 17d ago), licensed MIT. It adds 56 tokens to every session and 2,447 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
workflow
Starts and manages autonomous agent workflows. Triggers: workflow, start workflow, autonomous agents, agent pipeline.
autonomous-dev
Drives a brief, specification, issue or existing PR through implementation, review, tests and QA to a ready PR. Persists ownership, progress and commit-bound evidence for safe resumption. Use for autonomous software delivery or finishing an interrupted development run.
plan
Breaks features/goals into phased plans with task lists, agent assignments, dependencies. Triggers: plan feature, implementation roadmap, break down task, project phases.
prd-to-plan
Converts PRD into phased plan via tracer-bullet vertical slices. Triggers: PRD to plan, break down PRD, implementation plan, tracer bullets, phased plan.
qa-session
Interactive QA: user reports bugs conversationally, agent files GitHub issues. Triggers: QA session, report bug, file issue, conversational QA, bug intake.
onboard
Sets up ai-toolkit in a project: symlinks, CLAUDE.md, intent interview. Triggers: onboard, setup project, install ai-toolkit, migrate project.