Matplotlib
zLanqing/codex-claude-academic-skills
Low-level plotting library for full customization. An agent skill from zLanqing/codex-claude-academic-skills.
资源容量分析技能。当用户询问算力资源、GPU 节点、机房、IDC、传输链路、带宽、资源利用率、容量水位、扩容需求、资源规划时使用。
Install Resource Capacity Analysis
$ npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zj-unicom-ai/UniEmployee resource-capacity-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .claude/skills && cp -r skills-src/backend/skills/resource-capacity-analysis .claude/skills/resource-capacity-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "resource-capacity-analysis" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/resource-capacity-analysis into .claude/skills/resource-capacity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resource-capacity-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/resource-capacity-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zj-unicom-ai/UniEmployee resource-capacity-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .agents/skills && cp -r skills-src/backend/skills/resource-capacity-analysis .agents/skills/resource-capacity-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "resource-capacity-analysis" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/resource-capacity-analysis into .agents/skills/resource-capacity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resource-capacity-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zj-unicom-ai/UniEmployee resource-capacity-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/backend/skills/resource-capacity-analysis .cursor/skills/resource-capacity-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "resource-capacity-analysis" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/resource-capacity-analysis into .cursor/skills/resource-capacity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resource-capacity-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/zj-unicom-ai/UniEmployee.git --path backend/skills/resource-capacity-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zj-unicom-ai/UniEmployee resource-capacity-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/backend/skills/resource-capacity-analysis .gemini/skills/resource-capacity-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "resource-capacity-analysis" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/resource-capacity-analysis into .gemini/skills/resource-capacity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resource-capacity-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install zj-unicom-ai/UniEmployee resource-capacity-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .github/skills && cp -r skills-src/backend/skills/resource-capacity-analysis .github/skills/resource-capacity-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "resource-capacity-analysis" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/resource-capacity-analysis into .github/skills/resource-capacity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resource-capacity-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zj-unicom-ai/UniEmployee resource-capacity-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/backend/skills/resource-capacity-analysis .opencode/skills/resource-capacity-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "resource-capacity-analysis" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/resource-capacity-analysis into .opencode/skills/resource-capacity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resource-capacity-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
resource-capacity-analysis- GitHub stars
- 360
- Token cost
- ~456 tokens
- SKILL.md length
- 139 words
- Files
- 1
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
资源容量分析技能。当用户询问算力资源、GPU 节点、机房、IDC、传输链路、带宽、资源利用率、容量水位、扩容需求、资源规划时使用。
- Works in 4 steps: 全量台账按 category 分组，计算各分组平均利用率与资源数； → 按 utilization_pct 降序排行，列出全部 ≥ 80%… → 对 demand_forecast 非平稳的资源计算预测利用率， → …
- Data & Analytics work in your project
- SKILL.md covers 数据集, 阈值口径（写进结论）, 执行步骤（用 execute 跑 pandas，工作目录… and 注意事项, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Resource Capacity Analysis is an agent skill from zj-unicom-ai/UniEmployee. 资源容量分析技能。当用户询问算力资源、GPU 节点、机房、IDC、传输链路、带宽、资源利用率、容量水位、扩容需求、资源规划时使用。
Its SKILL.md is about 460 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics. The repository describes itself as: 面向企业的数字员工构建与运行平台：把专业员工的经验、流程与判断标准，固化为可随时上岗、可配置、可审批、可观测的 AI 数字员工。 The licence is MIT.
When your agent uses it
- Data & Analytics work in your project
Example prompts
- “/resource-capacity-analysis”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 全量台账按 category 分组，计算各分组平均利用率与资源数；
- 按 utilization_pct 降序排行，列出全部 ≥ 80% 的资源（预警清单）
- 对 demand_forecast 非平稳的资源计算预测利用率，
- 扩容缺口测算：对预警资源给出达到目标水位（70%）所需的
What it can do on your machine
Read from SKILL.md and the folder at commit c38a00a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Resource Capacity Analysis loads about 456 tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 139 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from zj-unicom-ai/UniEmployee at commit c38a00a, republished under its MIT licence (© zj-unicom-ai). 139 words, ~456 tokens.
Download SKILL.mdSave it as
.claude/skills/resource-capacity-analysis/SKILL.md (or your agent's skills folder).- name
- resource-capacity-analysis
- description
- 资源容量分析技能。当用户询问算力资源、GPU 节点、机房、IDC、传输链路、带宽、资源利用率、容量水位、扩容需求、资源规划时使用。
资源容量分析
你是算网资源运营专家，回答资源问题必须基于资源台账数据跑出的真实数据， 资源归属关系用企业本体核实，禁止编造容量或利用率数字。
数据集
/datasets/netops_resources.csv：算网资源台账，列： resource_id / category（机房/算力节点/传输链路/带宽）/ name / unit（机柜/卡/vCPU/Gbps）/ capacity / used / utilization_pct / location / status / demand_forecast
阈值口径（写进结论）
- 利用率 ≥ 80%：高水位预警，需扩容评估
- 利用率 ≥ 90%：紧急，需立即扩容或限流
- 利用率 < 50% 且需求平稳：低水位，可评估整合
- demand_forecast 含 "+N%" 时，用 预测利用率 = 当前利用率 × (1 + N%) 做前瞻判断
执行步骤（用 execute 跑 pandas，工作目录 /data）
步骤1：明确分析范围
确认用户问的资源类别（算力/网络/IDC 或全部）与目的（日常水位巡检 / 扩容决策 / 单资源深查）。
步骤2：跑数
- 全量台账按 category 分组，计算各分组平均利用率与资源数；
- 按 utilization_pct 降序排行，列出全部 ≥ 80% 的资源（预警清单） 与 < 50% 的资源（低水位清单）；
- 对 demand_forecast 非平稳的资源计算预测利用率， 标出"当前未超限但半年内将超 80%"的前瞻预警；
- 扩容缺口测算：对预警资源给出达到目标水位（70%）所需的 capacity 增量 = used / 0.7 - capacity（按 unit 取整）。
步骤3：本体核实归属与关联（涉及具体资源时）
- ontology_find_entities 按 datacenter/compute_node/link 实体类型查资源实体， 核对台账与本体两边的名称与状态是否一致；
- 本体多跳：compute_node → deploy_in → datacenter（节点在哪个机房）、 station → backhaul → link（基站走哪条回传链路）—— 由此回答"某基站/某机房受哪条链路高水位影响"这类关联问题；
- 台账与本体不一致时（如状态或名称对不上），以提示核实的方式输出，不擅自裁决。
步骤4：输出报告
结构：「资源水位总览 → 预警清单 → 前瞻预警 → 扩容建议」：
- 总览：各类资源平均利用率一句话；
- 预警清单表格：资源/类别/当前利用率/预测利用率/建议动作与时限；
- 扩容建议给出量化缺口（含单位），并注明影响的基站/机房范围（本体查得）；
- 涉及采购/立项的表述只给测算依据，不替用户拍板。
结尾标注数据来源：「以上来自资源台账（N 条）+ 企业本体（M 个实体 / K 条关系）」。
注意事项
- 利用率判定必须基于 used/capacity 复核，不能只看 utilization_pct 单列
- "带宽池"是逻辑资源，无本体实体对应时如实说明
- 扩容缺口测算要写明公式与假设（目标水位 70%），便于复核
- 用户要图表时用 matplotlib 出图并用 write_file 落到 /data/ 下
最终输出铁律
- 必须输出完整中文报告，格式固定为： 「资源水位总览 → 预警清单 → 前瞻预警 → 扩容建议」。
- 必须至少写明：
- 当前达到或超过 80% 的资源名称和利用率；
- 达到 70% 目标水位所需的扩容缺口及计算过程；
- 至少一个资源的本体归属或关联业务关系。
- 不要只输出"我先查阅资源容量分析技能规程"或"跑台账数据"。
- 分析完成后立即给结论，不要停留在工具执行过程。
© zj-unicom-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in backend/skills/resource-capacity-analysis of zj-unicom-ai/UniEmployee.
Open the folder on GitHubat commit c38a00a
Compare with similar skills
Resource Capacity Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Resource Capacity Analysis this skillzj-unicom-ai/UniEmployee | 360 | — | ~456 | Automated safety check: Pass | MIT | |
| MatplotlibzLanqing/codex-claude-academic-skills | 4.7k | 17 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.7k | 16 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Chart Visualizationbytedance/deer-flow | 84k | 1 repos | ~840 | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Resource Capacity Analysis
What does Resource Capacity Analysis do?
资源容量分析技能。当用户询问算力资源、GPU 节点、机房、IDC、传输链路、带宽、资源利用率、容量水位、扩容需求、资源规划时使用。. Resource Capacity Analysis is an agent skill from zj-unicom-ai/UniEmployee.
When should I use Resource Capacity Analysis?
Resource Capacity Analysis fits situations like: data & Analytics work in your project.
How do I install Resource Capacity Analysis in Claude Code?
Run `npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a claude-code`. Or copy the skill folder (backend/skills/resource-capacity-analysis in zj-unicom-ai/UniEmployee) into .claude/skills/resource-capacity-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Resource Capacity Analysis in Codex?
Run `npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a codex`. Or copy the skill folder (backend/skills/resource-capacity-analysis in zj-unicom-ai/UniEmployee) into .agents/skills/resource-capacity-analysis in your project. Codex loads it when a task matches its description.
Can I use Resource Capacity Analysis in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/resource-capacity-analysis, .gemini/skills/resource-capacity-analysis, .github/skills/resource-capacity-analysis and .opencode/skills/resource-capacity-analysis in your project.
What does Resource Capacity Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Resource Capacity Analysis is instructions for the agent only.
Does Resource Capacity Analysis access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Resource Capacity Analysis safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Resource Capacity Analysis use?
Resource Capacity Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Resource Capacity Analysis use?
About 456 tokens (SKILL.md is roughly 1.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Resource Capacity Analysis?
Skills that share tags, products or a category with Resource Capacity Analysis: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Chart Visualization (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Resource Capacity Analysis?
zj-unicom-ai (a GitHub organization) maintains it in zj-unicom-ai/UniEmployee, which has 360 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.
Source: zj-unicom-ai/UniEmployee on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.