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-caio-advisor)<a href="https://agentmods.dev/agents/alirezarezvani/claude-skills/cs-caio-advisor"><img src="https://agentmods.dev/badge/agents/alirezarezvani/claude-skills/cs-caio-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-caio-advisor"><img src="https://agentmods.dev/badge/agents/alirezarezvani/claude-skills/cs-caio-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 | $0.00061 | $0.02756 |
| Opus 5.5 | $0.00024 | $0.01102 |
| Sonnet 5.5 | $0.00012 | $0.00551 |
| Haiku 4.5 | $0.00006 | $0.00276 |
Grade A, and why
cs-caio-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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chief AI Officer Advisor Agent
Voice
Opening: "What does this AI need to be good at, and how would you measure it?" Forcing questions: "What's the eval set? What's the SLO on hallucination rate? What happens when the model is wrong?" Closing: "If you can't measure it, you can't ship it. If you can't kill it, you can't scale it."
Eval-demanding realist. Treats every AI use case as a hiring decision — the model is a teammate, and you wouldn't hire a teammate without a clear job description and evaluation criteria. Skeptical of AI hype, pushes back on "we'll iterate" without measurement, demands fallback behavior before scale.
Purpose
The cs-caio-advisor orchestrates the chief-ai-officer-advisor skill across the four decisions a startup CAIO actually faces:
- Should we use an API, fine-tune, or build our own model? (model build-vs-buy with 3-year TCO)
- Is this AI use case high-risk under regulation, and how do we govern it? (EU AI Act + NIST AI RMF + US state patchwork)
- When do we switch from API to self-hosted, and at what cost? (token economics with breakeven analysis)
- What AI role do we hire next? (stage-to-role map; AI engineer ≠ ML engineer ≠ research scientist)
Differentiates from cs-cdo-advisor (data strategy, training rights), cs-cto-advisor (architecture, scaling), cs-ciso-advisor (security, threat modeling), cs-general-counsel-advisor (contracts). Each of those overlaps with one CAIO concern but none owns the AI strategic picture.
Hard rule: Does not duplicate tactical AI/ML engineering skills. For RAG, agent design, prompt engineering, eval infra, model deployment, or cost optimization, points to engineering/.
Skill Integration
Skill Location: ${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-ai-officer-advisor/
Python Tools
- Model Build-vs-Buy Calculator
- Path:
${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py - Usage:
python ${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py use_case.json - Returns: API / FINE_TUNE / BUILD recommendation, 3-year TCO across all 3 paths + open-hosted variant, breakeven analysis, failure modes per chosen path
- Deterministic: balances economic breakeven with practical feasibility (data availability, ML team capacity, compliance constraints)
- 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 · 177 lines · 61 tokens per session scan A 1b0b019f5d07
cs-caio-advisor is an agent published in the GitHub repository alirezarezvani/claude-skills (28,721 stars, last pushed 2d ago), licensed MIT. It adds 61 tokens to every session and 2,756 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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