Wjs Publishing Hugo Skill for Claude
当用户想给自己的 Hugo 静态博客（如 maggiacito.com）新增或编辑帖子、管理类目、上传图片并发布上线时使用——对话式后台，说一句就改文件、commit、推送、自动部署，不需要任何 CMS/服务器。触发词：「发一篇博客」「给 Hugo 加文章」「写篇帖子到博客」「管理博客类目」「上传图片到博客」「博客后台」「/wjs-publishing-hugo」。
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/jianshuo/claude-skills --skill wjs-publishing-hugoWhat comes with it
12 837 bytes besides the instructionThe instruction itself
8 sections, as written by the authorwjs-publishing-hugo
Hugo 博客的对话式后台。我就是后台——没有 CMS、没有 /admin 页面、没有 PAT、没有 Cloudflare。
你说要发什么，我按仓库约定改 content/、放图、commit、push，走仓库现有部署流自动上线。
适用：maggiacito.com 这类 Hugo 站（content/posts/*.md + categories/tags taxonomy + GitHub Pages
Actions 部署）。其它 Hugo 仓库也能用，先按下面「前置检测」对齐约定。
前置检测（每次开工先做）
- 确认在 Hugo 仓库根：有
hugo.toml（或config.toml）和content/。不在就让用户给路径。 - 读
hugo.toml：拿timeZone、[taxonomies]（确认categories/tags）。 - 看一篇现有帖子的 front matter（
content/posts/里随便挑一篇），确认字段与下面「权威格式」一致；
不一致就以仓库现状为准，别套用本文档的默认。
帖子的权威格式（maggiacito 现状）
文件：content/posts/<slug>.md。front matter 严格如下，日期用 YYYY-MM-DD HH:MM:SS（Asia/Shanghai）：
---
title: "标题"
date: 2026-06-04 21:30:00
lastmod: 2026-06-04 21:30:00
categories: ["知天命"]
tags: []
url: /知天命/标题/
---
正文 markdown……
url显式写/<主类目>/<标题或slug>/（沿用 WordPress 迁来的链接风格；类目和标题可含中文）。categories/tags是字符串数组，可空[]。- 不要手写 front matter——用
scripts/new-post.py生成，格式才不会漂。
工作流
1. 新增帖子
拿到标题 + 正文（用户给草稿/链接/零散思路都行；要润色按需润），选好类目，然后：
echo "<正文 markdown>" | python3 <SKILL_DIR>/scripts/new-post.py \
--repo <仓库根> --title "标题" --category "知天命" --slug 可选英文slug
脚本生成 content/posts/<slug>.md 并打印路径。不传 --slug 时按标题派生文件名（中文也行，文件名读者看不到）。
不传 --url 时默认 /<首个类目>/<标题或slug>/。需要别的链接用 --url 覆盖。
2. 编辑帖子
直接改对应 content/posts/*.md：改正文随意；改了内容把 lastmod 更新成当前时间，date 不动。
3. 管理类目
- 先列现有类目给用户挑（避免造重复）：
bash <SKILL_DIR>/scripts/categories.sh <仓库根> - 新类目：现场加进帖子的
categories[]即可，Hugo taxonomy 自动建/categories/<名>/页，无需别的配置。 - 改名/合并类目：扫所有
content/posts/*.md的categories[]批量替换（这是 CMS 做不到、我能做的）。改完提醒用户旧的/categories/<旧名>/链接会失效。
4. 上传图片
新图放 static/uploads/<年>/（served 为 /uploads/...）；老的 static/wp-content/uploads/ 不动。
python3 <SKILL_DIR>/scripts/add-image.py <本地图片> --repo <仓库根> --alt "说明"
打印一行 markdown 图片链接，插进正文。超 2000px 宽的图会用 macOS sips 自动缩小。
5. 发布上线
本地预览（可选）：hugo server -D → 看 http://localhost:1313 。
确认后发布：
bash <SKILL_DIR>/scripts/publish.sh "post: <标题>" <仓库根>
普通用户的 git push 会触发 deploy.yml（on: push）构建上线——**只有反馈机器人那种 GITHUB_TOKEN
推送**才会被 GitHub 防递归挡掉，你自己推不受影响。push 完可 gh run watch 看部署。
范围（YAGNI）
只做：新增/编辑帖子、管理类目、上传图片、发布。不做评论、用户管理、草稿工作流、定时发布。
常见错误
- ❌ 手写 front matter 导致日期格式/字段漂移 → ✅ 一律走
new-post.py。 - ❌ 图片塞进
static/wp-content/uploads/（那是迁移来的历史目录）→ ✅ 新图进static/uploads/。 - ❌ 改了帖子忘了更
lastmod→ ✅ 编辑后同步lastmod。 - ❌ 造了和现有同名/近义的类目 → ✅ 先
categories.sh看一遍再定。 - ❌ 以为 push 不会自动部署（被反馈闭环那条
GITHUB_TOKEN限制误导）→ ✅ 你本人 push 会触发deploy.yml。 - ❌ 设了会撞车的
url（和已有帖子重复）→ ✅ 用categories.sh/grep 查重，必要时--url指定唯一路径。
Other skills for the same job
different authors, same section of the catalogue
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
How to use it
Take jianshuo/wjs-publishing-hugo from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.