Laporan audit skill
complete-ai-agent-stack-deployment-self-hosted-from-scratch Laporan audit.
Sequences a complete, end-to-end, fully self-hosted AI agent stack deployment from scratch — GPU procurement/sizing for open-weight model serving, self-hosted LLM serving (vLLM/TGI), agent control-flow architecture, a self-hosted vector database for RAG, self-hosted MCP servers for tool access, and an evaluation/guardrails harness — with no managed LLM API or managed vector database anywhere in the stack. An integration/orchestration skill that sequences existing tool-specific skills in the right order and flags handoff points, explicit about the added GPU-procurement and operational burden versus a cloud-managed agent stack. Use when a user asks to "build a self-hosted AI agent stack with open-weight models," "run our agent on our own GPUs with no managed LLM API," "stand up a self-hosted vector database and MCP servers for an agent platform," or "give me the end-to-end sequence for a fully self-hosted agent deployment from GPU procurement to production."
Trust Score OpenAgentSkill
Trust Score OpenAgentSkill
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
Adopsi GitHub
Peringatan48
38 star GitHub
Aktivitas star/fork
Peringatan48
38 star dan 18 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Pemeliharaan terbaru
Lulus88
3 bulan sejak push
Kejelasan lisensi
Lulus86
Apache-2.0
Kelengkapan README/SKILL.md
Lulus86
Metadata memuat konteks penggunaan dan alur kerja yang cukup
Risiko dependensi/runtime
Gagal38
command execution surface, credential or environment access
Ketersediaan pemasangan
Lulus92
npx skills add selvarajmurugesan90/ops-engineering-skills --skill complete-ai-agent-stack-deployment-self-hosted-from-scratch
Keamanan perintah pemasangan
Lulus92
Jalur pemasangan paket atau runtime standar
Cakupan izin
Gagal24
secrets or environment access, shell or command execution
Bukti repositori
Lulus86
https://github.com/selvarajmurugesan90/ops-engineering-skills/tree/main/plugins/ai-agent/skills/complete-ai-agent-stack-deployment-self-hosted-from-scratch
Status peninjauan
Peringatan46
Persetujuan tinjauan AI belum ada
Hasil terbukti Agent
Info54
Belum ada data hasil Agent
Pemeriksaan
Tinjauan pemasangan dan adopsi
Jalur pemasangan
92
npx skills add selvarajmurugesan90/ops-engineering-skills --skill complete-ai-agent-stack-deployment-self-hosted-from-scratch
Repositori
88
https://github.com/selvarajmurugesan90/ops-engineering-skills/tree/main/plugins/ai-agent/skills/complete-ai-agent-stack-deployment-self-hosted-from-scratch
Lisensi
86
Apache-2.0
Pemeliharaan
88
3 bulan sejak push
Tinjauan AI
55
Review approval is missing
Kelengkapan README/SKILL.md
86
Usable description available
Risiko dependensi
38
command execution surface, credential or environment access
Keamanan perintah pemasangan
92
Jalur pemasangan paket atau runtime standar
Cakupan izin
24
secrets or environment access, shell or command execution
Aktivitas star/fork
48
38 star dan 18 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Adopsi
42
38 star GitHub
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Peringatan
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- Low GitHub adoption signal
- Persetujuan tinjauan AI belum ada
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 38 GitHub stars
- Stars/forks activity: 38 stars, 18 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Metode
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
Bandingkan opsi sekitar
Skill terkait untuk diaudit berikutnya
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K Star · Laporan audit
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
64K Star · Laporan audit
Academic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38K Star · Laporan audit