claude-skills is a large collection of reusable skills, commands, plugins, and supporting resources for coding agents across engineering, business, research, compliance, and productivity work. It is used by people who want agents such as Claude Code, Codex, Gemini CLI, or Cursor to follow specialized workflows. The catalogue contains selected skills and settings from the collection.
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 alirezarezvani/claude-skills --skill chief-data-officer-advisorgit clone --depth 1 https://github.com/alirezarezvani/claude-skillsWrote 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/alirezarezvani/claude-skills/chief-data-officer-advisor)<a href="https://agentmods.dev/skills/alirezarezvani/claude-skills/chief-data-officer-advisor"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-skills/chief-data-officer-advisor/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/alirezarezvani/claude-skills/chief-data-officer-advisor"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-skills/chief-data-officer-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 1 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00131 | $0.02826 |
| Opus 5.5 | $0.00052 | $0.01130 |
| Sonnet 5.5 | $0.00026 | $0.00565 |
| Haiku 4.5 | $0.00013 | $0.00283 |
Grade A, and why
chief-data-officer-advisor 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 2d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chief Data Officer Advisor
Strategic data leadership for startup CDOs and founders without one. Four decisions, no surveys:
- Can we train our model on this data? — origin × consent × use-case matrix
- Warehouse, lakehouse, or mesh — and what do we build vs buy? — stage-driven architecture
- What is our customer data worth? — strategic value + M&A multiplier + productization paths
- What data role do we hire next? — stage-to-role map, centralize-vs-embed trigger
This skill does not cover tactical data engineering. For schema design, observability, query optimization, RAG, or ML platform implementation, see engineering/database-designer/, engineering/observability-designer/, engineering/data-quality-auditor/, engineering/sql-database-assistant/, engineering/rag-architect/, engineering/llm-cost-optimizer/.
Keywords
CDO, chief data officer, AI training data, consent provenance, training rights, GDPR Article 6 lawful basis, GDPR Article 22, EU AI Act high-risk, ePrivacy, copyright fair use, hiQ v. LinkedIn, scraped data, synthetic data, data product, data mesh, lakehouse, medallion architecture, dbt, Snowflake, BigQuery, Databricks, Fivetran, Airbyte, reverse ETL, feature store, customer data as asset, data monetization, data productization, anonymization, k-anonymity, differential privacy, M&A data diligence, data org, analytics engineer, data engineer, data scientist, data product manager, centralize vs embed, hub and spoke
Quick Start
# Audit data sources for AI training eligibility
python scripts/ai_training_data_audit.py # uses embedded sample
python scripts/ai_training_data_audit.py path/to/sources.json
# Pick data architecture + build-vs-buy + sequencing
python scripts/data_product_strategy_picker.py # uses embedded Series A SaaS
python scripts/data_product_strategy_picker.py path/to/profile.json
# Value the customer data corpus + productization viability
python scripts/data_asset_valuator.py # uses embedded B2B sample
python scripts/data_asset_valuator.py path/to/corpus.json
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/ai_training_data_rights.md 7.4 KB
- references/customer_data_as_asset.md 10 KB
- references/data_product_strategy.md 8.4 KB
- references/data_team_org_evolution.md 10 KB
- scripts/ai_training_data_audit.py 19 KB runs code
- scripts/data_asset_valuator.py 14 KB runs code
- scripts/data_product_strategy_picker.py 14 KB runs code
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.
- 2d ago Changed · +205 lines · +131 tokens per session 53ac5dd4a6a9
- 1mo ago First seen · 1 lines · 0 tokens per session scan A a0c6afa8311a
chief-data-officer-advisor is a skill published in the GitHub repository alirezarezvani/claude-skills (28,721 stars, last pushed 3d ago), licensed MIT. It adds 131 tokens to every session and 2,826 once invoked, about $0.0005 per session on Opus 5.5. 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.
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