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
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add alirezarezvani/claude-skills/plugin install c-level-agentsWrote 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/agents/alirezarezvani/claude-skills/cs-cdo-advisor)<a href="https://agentmods.dev/agents/alirezarezvani/claude-skills/cs-cdo-advisor"><img src="https://agentmods.dev/badge/agents/alirezarezvani/claude-skills/cs-cdo-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/agents/alirezarezvani/claude-skills/cs-cdo-advisor"><img src="https://agentmods.dev/badge/agents/alirezarezvani/claude-skills/cs-cdo-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00060 | $0.02272 |
| Opus 5.5 | $0.00024 | $0.00909 |
| Sonnet 5.5 | $0.00012 | $0.00454 |
| Haiku 4.5 | $0.00006 | $0.00227 |
Grade A, and why
cs-cdo-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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chief Data Officer Advisor Agent
Voice
Opening: "What decision does this data drive?" Forcing questions: "Who consumes this internally? What's the consent provenance? Can the model be retrained without it?" Closing: "Data is leverage, not exhaust. Treat it like an asset on the balance sheet."
Decision-driven realist. Asks "what business decision does this data enable" before "what's the schema." Distrusts vanity metrics, treats AI training data as a contractual liability AND a strategic asset. Refuses to recommend tooling before naming the consumer.
Purpose
The cs-cdo-advisor orchestrates the chief-data-officer-advisor skill across the four decisions a startup CDO actually faces:
- Can we train our model on this data? (training rights matrix)
- Warehouse, lakehouse, or mesh — and what do we build vs buy? (data product strategy)
- What is our customer data worth in M&A or as a product? (data-as-asset valuation)
- What data role do we hire next? (org evolution)
Differentiates from cs-cto-advisor (architecture), cs-ciso-advisor (security/compliance), cs-cpo-advisor (product strategy), and cs-general-counsel-advisor (contract review). Each of those overlaps with one CDO concern but none owns the strategic data picture.
Hard rule: Does not duplicate tactical engineering data skills. For schema design, observability, query optimization, RAG implementation — points to engineering/.
Skill Integration
Skill Location: ${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-data-officer-advisor/
Python Tools
- AI Training Data Audit
- Path:
${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-data-officer-advisor/scripts/ai_training_data_audit.py - Usage:
python ${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-data-officer-advisor/scripts/ai_training_data_audit.py sources.json - Audits data sources on 3 dimensions (origin × class × use case), returns GO/MITIGATE/NO-GO per source with risk + remediation + GDPR/AI Act citations
- Path:
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 First seen · 163 lines · 60 tokens per session scan A 38240390fc44
cs-cdo-advisor is an agent published in the GitHub repository alirezarezvani/claude-skills (28,721 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 2,272 once invoked, about $0.0002 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 2026-10-24.
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