container-management:gpu-bottleneck-analysis SKILL.md skillA
container-management:gpu-bottleneck-analysis from daocloud/daocloud-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add daocloud/daocloud-skills/container-management:gpu-bottleneck-analysis@git:20260918.2022c1ePins git:20260918.2022c1e in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- daocloud/daocloud-skills
- Path
skills/container-management-gpu-bottleneck-analysis/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260918.2022c1e, committed Sep 18, 2026
- Size
- 24,742 bytes
- Repository stars
- 13
- License
- none declared
Use when a user asks to analyze GPU pool or GPU cluster capacity, predict bottlenecks under traffic, QPS, or inference load growth, or choose between scaling, throttling, and routing changes. Also use for Chinese requests about GPU 池/集群容量、流量上涨、QPS 增长、推理负载增长、哪个 GPU 池先成为瓶颈、是否扩容/限流/改路由. Example prompts include "如果今晚流量上涨 30%，哪个 GPU 池会先成为瓶颈？", "建议扩容、限流还是改路由？", "which GPU pool will bottleneck first", and "what if traffic increases by X%".
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# GPU Pool Bottleneck Analysis Analyze GPU cluster capacity across multiple clusters, predict bottlenecks under increased inference QPS, and recommend concrete actions (scale, throttle, or reroute). **REQUIRED SUB-SKILL:** Use `dce` for all command execution, auth checks, and catalog discovery. **SCOPE:** Read-only analysis. This skill does NOT create, modify, or delete any resources. ## When to Use - "If traffic increases by 30%, which GPU pool will bottleneck first?" - "Should we scale, throttle, or change routing for GPU workloads?" - "GPU capacity planning across clusters" - "Which cluster has the most GPU headroom?" - "Predict GPU saturation under load increase" ## Prerequisites - `dce` auth is established. If not, stop and instruct the user to run `dce auth login --hostname <host>`. - User provides the inference QPS metric name if it is not a standard kpanda metric (see Step 4). ## Workflow ### Step 1 — Discover GPU Clusters Run `dce container-management cluster list-clusters -o json` to get all clusters. Then identify which clusters have GPU capacity. Use one of: - `dce container-management cluster list-all-cluster-gpu` (returns global GPU inventory) …
Read the whole file at its exact version.
How to install
mdr add daocloud/daocloud-skills/container-management:gpu-bottleneck-analysis@sha256:6d35207791fcc24cThe line at the top of this page pins the label git:20260918.2022c1e, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_osilbkmoqfjmql37)
3 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 1 version of this file, committed on Sep 18, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (24742 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
daocloud/daocloud-skills · 13 stars · license none declared · pushed 2026-11-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_osilbkmoqfjmql37 GET https://markdownregistry.com/api/v1/resolve?ref=daocloud/daocloud-skills/container-management:gpu-bottleneck-analysis GET https://markdownregistry.com/api/v1/blob/6d35207791fcc24cf4186c5f254f4e4392b1b1b8f8b0d5219c3fc784d6bea324
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.