GPU-Server-Management-MCP-Skill-: Skill for Claude Code

.agents/skills/gpu-server-management/SKILL.md

gpu-server-management is a skill for Claude Code, Codex from ixijxjgxidj-cmd/GPU-Server-Management-MCP-Skill-. It costs 226 tokens per session (5,809 once invoked), scanned A, original, MIT.

A management system for using shared remote GPU servers, which are machines designed for graphics-heavy computing and AI workloads.

In plain words
What is it for?
Use it to connect to, claim, release, and monitor GPU servers; run training or inference jobs; manage proxy subscriptions; and retrieve troubleshooting notes.
Why use it?
It helps coordinate server access, remember machine-specific problems, and avoid repeating setup and troubleshooting work.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is ixijxjgxidj-cmd/GPU-Server-Management-MCP-Skill-'s own configuration. It tells Claude Code and Codex how to work on GPU-Server-Management-MCP-Skill- itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything GPU-Server-Management-MCP-Skill- configures →

Reuse

Borrowing it

Nothing to install: this file belongs to ixijxjgxidj-cmd/GPU-Server-Management-MCP-Skill-. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/ixijxjgxidj-cmd/GPU-Server-Management-MCP-Skill-/main/.agents/skills/gpu-server-management/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/ixijxjgxidj-cmd/GPU-Server-Management-MCP-Skill-

Made for: Claude Code, Codex.

Wrote 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.

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README.md
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Your own site
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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.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/ixijxjgxidj-cmd/gpu-server-management-mcp-skill-/gpu-server-management"><img src="https://agentmods.dev/badge/skills/ixijxjgxidj-cmd/gpu-server-management-mcp-skill-/gpu-server-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 226 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,809 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00226 $0.05809
Opus 5.5 $0.00090 $0.02324
Sonnet 5.5 $0.00045 $0.01162
Haiku 4.5 $0.00023 $0.00581

Measured 2mo ago against content hash 367bcc173d42, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

gpu-server-management scanned grade A with 1 finding 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 2mo 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- 直连测速: curl -s -w "%{speed_download}" -o /dev/null --max-time 8 "<URL>"
.agents/skills/gpu-server-management/SKILL.md · 273 lines

How it starts

The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GPU Server Management & Next-Gen Proxy Orchestration

A shared registry and high-performance multi-proxy orchestration engine that lets you drive many remote GPU machines as one pool. The database is collective memory: what one agent configures, discovers, or encounters (including pitfalls, troubleshooting workarounds, and server notes), every later agent reads back — so setup work is never repeated and machines cooperate seamlessly.

What this unlocks — the reason to reach for these tools:

Goal Tool In one line
遇错优先查询·RAG 问题库 query_troubleshooting { query } 【遇到报错第一顺位调用】 秒级语义检索全集群所有踩坑经验 (pitfalls)、节点备忘 (notes) 与备份索引,返回已验证的解决方案与执行命令。
Dual Timers: Lease & Task get_servers / claim_server Tracks 2 distinct timers: 1. Server physical lifespan remaining (server_expires_at) vs. 2. Task countdown lease (duration_minutes).
Server Pitfalls & Caveats (踩坑与避坑记忆) record_pitfall / get_servers Records environment traps, PyTorch/CUDA conflicts, OOM mitigations, and network quirks per server, auto-returned on every get_servers call.
Clash & V2Ray Subscriptions import_proxy_subscription Auto-fetches and parses Clash YAML / Base64 subscriptions, batch-populating proxy nodes with region tags (HK/JP/US/SG).
Domain-Aware Routing & Racing plan_network_relay Domain profiling (HuggingFace / GitHub / S3) + Direct vs Multi-Proxy concurrent Range benchmark (哪个快选哪个).
Multi-Proxy Chunk Aggregator plan_network_relay For >500MB large weights/datasets, splits into 64MB chunks across multiple proxies in parallel with auto-failover and resume.
Intelligent Dual-Mode Backup plan_server_backup Physical remaining > 1h: only backup experiment outputs (exclude datasets to keep affinity); Physical remaining <= 1h: full asset evacuation.
RAG Vector Search for Backups query_backup_index Semantic natural-language / keyword RAG search across all historical checkpoints and backups (IP lifecycle-bound).
Dataset Affinity (数据就近计算) plan_task_allocation { preferred_datasets } Route jobs directly to nodes with pre-cached datasets (+100k score boost), avoiding huge network downloads.
Manage Datasets register_dataset / remove_dataset Record dataset paths & sizes so subsequent agents reuse local data paths without re-downloading.
Borrow disk plan_disk_share Mount a disk-rich machine onto one that ran out of space (turn a peer into a cloud disk).
Unified Proxy Environment plan_network_relay One-shot script (proxy_env.sh) wrapping Shell, Git, Pip, Python, and HuggingFace fast transfer variables.

Read the full file on GitHub · 273 lines

Changes

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.

  1. 2mo ago First seen · 273 lines · 226 tokens per session scan A 367bcc173d42

Subscribe to this mod's changes

gpu-server-management is a skill published in the GitHub repository ixijxjgxidj-cmd/GPU-Server-Management-MCP-Skill- (7 stars, last pushed 2mo ago), licensed MIT. It adds 226 tokens to every session and 5,809 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

gpu-server-management compared with skills from other repositories

gpu-server-management next to 6 skills from other repositories
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gpu-server-managementixijxjgxidj-cmd/GPU-Server-Management-MCP-Skill-Skill72mo agoMITA226Claude Code, Codex
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