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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/alirezarezvani/claude-skillsnpx agentmods add skills/alirezarezvani/claude-skills/vpe-advisorWrote 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/vpe-advisor)<a href="https://agentmods.dev/skills/alirezarezvani/claude-skills/vpe-advisor"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-skills/vpe-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/vpe-advisor"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-skills/vpe-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 | $0.00130 | $0.03229 |
| Opus 5.5 | $0.00052 | $0.01292 |
| Sonnet 5.5 | $0.00026 | $0.00646 |
| Haiku 4.5 | $0.00013 | $0.00323 |
Grade A, and why
vpe-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 yesterday.
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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VP of Engineering Advisor
Strategic engineering operations leadership for startup VPEs and founders without one. Four decisions, no generic engineering survey:
- Are we delivering at the right throughput? — DORA 4 metrics + bottleneck identification (where work waits)
- How do we scale the eng hiring funnel? — funnel math + pipeline gap + time-to-fill discipline
- What's our team structure — and when do we add a tech-lead manager? — squad/tribe/chapter design + manager-trigger
- What's our production discipline? — on-call rotation, deployment cadence, postmortem culture (reference-only)
This skill is NOT a CTO skill. CTO owns what to build (architecture, scaling cliffs, build-vs-buy). VPE owns how to ship it reliably (delivery, hiring, team structure, production operations). At early stage these are often the same person; at scale they're distinct roles.
This skill is NOT a cs-engineering-lead replacement. Engineering-lead owns day-to-day incident and on-call coordination. VPE owns the operating model that engineering-lead executes.
Keywords
VPE, VP of Engineering, VP Engineering, engineering operations, delivery throughput, DORA, deployment frequency, lead time for changes, mean time to recovery, MTTR, change failure rate, cycle time, lead time, throughput, engineering hiring, eng hiring funnel, technical interview, take-home, pair programming, hiring pipeline, time-to-fill, cost-per-hire, ramp time, engineering team structure, squad, tribe, chapter, Spotify model, conway's law, tech lead, engineering manager, EM, span of control, hiring funnel conversion, eng comp, leveling, IC track, manager track, deployment cadence, on-call rotation, postmortem culture, blameless retro
Quick Start
# Decision A: DORA 4 metrics + bottleneck identification
python scripts/delivery_throughput_analyzer.py # embedded sprint sample
python scripts/delivery_throughput_analyzer.py path/to/sprint_metrics.json
# Decision B: Hiring funnel health + pipeline gap
python scripts/eng_hiring_funnel_calculator.py # embedded 3-quarter sample
python scripts/eng_hiring_funnel_calculator.py path/to/funnel.json
# Decision C: Team structure recommendation + manager-trigger
python scripts/eng_team_structure_designer.py # embedded 25-engineer sample
python scripts/eng_team_structure_designer.py path/to/team.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/delivery_throughput.md 7.2 KB
- references/eng_team_structure.md 7.8 KB
- references/engineering_hiring_funnel.md 8.1 KB
- references/production_discipline.md 7.8 KB
- scripts/delivery_throughput_analyzer.py 9.8 KB runs code
- scripts/eng_hiring_funnel_calculator.py 11 KB runs code
- scripts/eng_team_structure_designer.py 11 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.
- yesterday First seen · 231 lines · 130 tokens per session scan A 48398cc729dd
vpe-advisor is a skill published in the GitHub repository alirezarezvani/claude-skills (28,666 stars, last pushed 2d ago), licensed MIT. It adds 130 tokens to every session and 3,229 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 2026-10-24.
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