Wp Performance Review
elvismdev/claude-wordpress-skills
WordPress performance code review and optimization analysis.
分析发布日志数据，从发布时间、标签效果、内容类型、粉丝增长四个维度归因内容表现，输出可执行的优化建议. An agent skill from ZJU-REAL/Easel.
Install Skill Publish Analytics
$ npx skills add ZJU-REAL/Easel --skill skill-publish-analytics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ZJU-REAL/Easel skill-publish-analytics --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/ZJU-REAL/Easel.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/openclaw/skill-publish-analytics .claude/skills/skill-publish-analytics && 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 "skill-publish-analytics" agent skill from https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-publish-analytics into .claude/skills/skill-publish-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-publish-analytics", 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/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-publish-analyticsType 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 ZJU-REAL/Easel --skill skill-publish-analytics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ZJU-REAL/Easel skill-publish-analytics --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJU-REAL/Easel.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/openclaw/skill-publish-analytics .agents/skills/skill-publish-analytics && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "skill-publish-analytics" agent skill from https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-publish-analytics into .agents/skills/skill-publish-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-publish-analytics", 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 ZJU-REAL/Easel --skill skill-publish-analytics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ZJU-REAL/Easel skill-publish-analytics --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJU-REAL/Easel.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/openclaw/skill-publish-analytics .cursor/skills/skill-publish-analytics && 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 "skill-publish-analytics" agent skill from https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-publish-analytics into .cursor/skills/skill-publish-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-publish-analytics", 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/ZJU-REAL/Easel.git --path skills/openclaw/skill-publish-analytics--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 ZJU-REAL/Easel --skill skill-publish-analytics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ZJU-REAL/Easel skill-publish-analytics --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJU-REAL/Easel.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/openclaw/skill-publish-analytics .gemini/skills/skill-publish-analytics && 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 "skill-publish-analytics" agent skill from https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-publish-analytics into .gemini/skills/skill-publish-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-publish-analytics", 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 ZJU-REAL/Easel skill-publish-analyticsInstalls 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 ZJU-REAL/Easel --skill skill-publish-analytics -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ZJU-REAL/Easel.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/openclaw/skill-publish-analytics .github/skills/skill-publish-analytics && 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 "skill-publish-analytics" agent skill from https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-publish-analytics into .github/skills/skill-publish-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-publish-analytics", 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 ZJU-REAL/Easel --skill skill-publish-analytics -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ZJU-REAL/Easel skill-publish-analytics --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJU-REAL/Easel.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/openclaw/skill-publish-analytics .opencode/skills/skill-publish-analytics && 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 "skill-publish-analytics" agent skill from https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-publish-analytics into .opencode/skills/skill-publish-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-publish-analytics", 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
skill-publish-analytics- GitHub stars
- 3.4k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 324 words
- Files
- 4 (incl. scripts, references)
- Skills in repo
- 114
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
分析发布日志数据，从发布时间、标签效果、内容类型、粉丝增长四个维度归因内容表现，输出可执行的优化建议. An agent skill from ZJU-REAL/Easel.
- Works in 4 steps: 检查 Profile 上下文（=== EASEL ACCOUNT PROFILE… → 调用脚本（--profile 须放在子命令前；publish-log.json… → 脚本输出结构化… → …
- Tasks that involve Performance reviews
- SKILL.md covers 数据层定位, 输入, 数据源 and 执行步骤, plus 8 more sections
- Runs Python scripts from its folder; calls python3
What it does
Skill Publish Analytics is an agent skill from ZJU-REAL/Easel. 分析发布日志数据，从发布时间、标签效果、内容类型、粉丝增长四个维度归因内容表现，输出可执行的优化建议。 当用户说"发布数据分析""归因分析""什么时间发好""标签效果""内容表现分析""发布日志分析"时使用。 和 skill-social-performance-review 的区别：本 SKILL 从发布日志做四维归因，review 做跨平台月度组合复盘。
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `EASEL-META.md`, `references/follower-log-schema.md` and `scripts/analyze.py`).
It sits in Business, Finance & HR, covering Performance reviews. The repository describes itself as: An open-source AI agent for social media — discover trends, create content, publish everywhere, and learn what works across Xiaohongshu, Douyin, Zhihu, Bilibili, and more.🎨一个开源的… The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Performance reviews
Example prompts
- “发布数据分析”
- “什么时间发好”
- “内容表现分析”
- “/skill-publish-analytics”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 检查 Profile 上下文（=== EASEL ACCOUNT PROFILE === 标记），有则取 profile 名。
- 调用脚本（--profile 须放在子命令前；publish-log.json 不存在时脚本友好报错）
- 脚本输出结构化 JSON：summary（总数/日期范围/平台/各指标覆盖率/全量样本警告）+
- 解读输出：按下方各模式说明和「输出格式」把 JSON 转成 Markdown 表格 →
What it can do on your machine
Read from SKILL.md and the folder at commit ede33b8. 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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
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
Skill Publish Analytics loads about 1.2k tokens when it runs, and up to ~1.9k if it reads all its reference files. Until then it costs about 51 tokens; SKILL.md has 324 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); the scripts in this folder are not scanned.
SKILL.md
The full file from ZJU-REAL/Easel at commit ede33b8, republished under its Apache-2.0 licence (© ZJU-REAL). 324 words, ~1,240 tokens.
Download SKILL.mdSave it as
.claude/skills/skill-publish-analytics/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- skill-publish-analytics
- description
- 分析发布日志数据，从发布时间、标签效果、内容类型、粉丝增长四个维度归因内容表现，输出可执行的优化建议。 当用户说"发布数据分析""归因分析""什么时间发好""标签效果""内容表现分析""发布日志分析"时使用。 和 skill-social-performance-review 的区别：本 SKILL 从发布日志做四维归因，review 做跨平台月度组合复盘。
- layer
- attribute
发布数据归因分析
读取 publish-log.json，从时间、标签、类型、增长四个维度分析内容表现，输出结构化归因报告。
数据层定位
本 SKILL 是归因链的消费层，只读底座、不新建存储、不回写：
| 数据 | 权威底座 | 维护方 | 本 SKILL 用途 |
|---|---|---|---|
| 发布事件 | outputs/_analytics/publish-log.json | skill-publish-log | 模式 A/B/C |
| 粉丝 / 时序快照 | outputs/_analytics/snapshots/{profile}/{platform}/{date}.json | skill-data-tracker | 模式 D 增长归因 |
粉丝时序的权威来源是 skill-data-tracker 的快照底座。 模式 D 读取的 outputs/_analytics/follower-log.json 由 track.py export-followers 确定性导出，不应手工维护。字段映射见 references/follower-log-schema.md。
输入
用户指定分析模式（可组合）：
- 模式 A — 最佳发布时间：分析发布时段与互动数据的关系
- 模式 B — 标签效果分析：评估标签对内容表现的影响
- 模式 C — 内容类型对比：按内容类型对比各项指标
- 模式 D — 增长归因：关联发布事件与粉丝增长
未指定模式时默认执行 A + B + C。模式 D 前先运行 python3 skills/openclaw/skill-data-tracker/scripts/track.py export-followers。
数据源
publish-log.json 结构
{
"version": "1.0",
"entries": [{
"id": "唯一标识",
"platform": "xiaohongshu|douyin|bilibili|weibo",
"title": "标题",
"url": "发布链接",
"type": "图文|视频|直播|文章",
"published_at": "ISO 8601 时间戳",
"logged_at": "记录时间",
"initial_metrics": {
"views": null | number,
"likes": null | number,
"comments": null | number,
"shares": null | number
},
"skill_source": "生成该内容的 SKILL",
"profile": "账号画像名",
"tags": ["标签列表"],
"notes": "备注"
}]
}数据处理规则
- null 指标：排除出该指标的平均值计算，报告覆盖率百分比
- 样本量警告：单桶 < 5 条时标注
⚠ 样本不足；全量 < 10 条时在报告头部警告结果可能不具统计意义 - 时区：有 Profile 时使用 Profile 中的时区，无 Profile 时默认 Asia/Shanghai
执行步骤
四种分析模式的全部计算（时段分桶、标签聚合、类型对比、增长归因、样本量警告、
覆盖率）由 scripts/analyze.py 确定性完成。不要用内联 Python 心算，直接调脚本。
LLM 只负责选模式、按 Profile 过滤、解读 JSON、写关键发现/方法论/局限性。
- 检查 Profile 上下文（
=== EASEL ACCOUNT PROFILE ===标记），有则取 profile 名。 - 调用脚本（
--profile须放在子命令前；publish-log.json 不存在时脚本友好报错）：
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py all
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py --profile 画像名 time
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py --profile 画像名 tags
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py --profile 画像名 types
python3 skills/openclaw/skill-publish-analytics/scripts/analyze.py --profile 画像名 growth- 脚本输出结构化 JSON：
summary（总数/日期范围/平台/各指标覆盖率/全量样本警告）+ 各模式结果。每个分析桶含count和warning（单桶 <5 条标注样本不足）。 - 解读输出：按下方各模式说明和「输出格式」把 JSON 转成 Markdown 表格 → 关键发现（3 条）→ 方法论 → 局限性。局限性须含"关联性不等于因果性"。
模式 A — 最佳发布时间
步骤：
- 解析
published_at提取小时和星期几 - 将时段分为 6 个桶：早晨(6-9) / 上午(9-12) / 午间(12-14) / 下午(14-18) / 晚间(18-22) / 深夜(22-6)
- 交叉 initial_metrics 计算每个桶的平均互动量（views、likes、comments、shares）
- 按平台分别统计
输出：
- 热力图表格（星期 × 时段），单元格为平均互动综合分
- 推荐 Top 3 发布时段（含具体星期和时间段）
- 各平台分别的最佳时段
互动综合分计算：
engagement_score = views × 0.1 + likes × 1.0 + comments × 2.0 + shares × 3.0
模式 B — 标签效果分析
步骤：
- 提取每条记录的
tags[]，统计各标签使用频次 - 对每个标签计算其关联条目的平均 views、likes、comments、shares
- 构建标签共现矩阵：标签 A 与标签 B 同时出现的次数
- 识别高效标签（平均互动高于全局均值）和低效标签
输出：
- 标签效果排名表：标签 / 使用次数 / 平均浏览 / 平均点赞 / 平均评论 / 平均转发
- Top 5 与 Bottom 5 标签对比
- 标签共现矩阵（仅展示共现 >= 2 次的组合）
模式 C — 内容类型对比
步骤：
- 按
type字段分组（图文 / 视频 / 直播 / 文章） - 计算每类的发布数量、平均 views、likes、comments、shares
- 交叉平台与类型生成二维表
输出：
- 类型对比表：类型 / 数量 / 平均浏览 / 平均点赞 / 平均评论 / 平均转发
- 每个指标标注 "winner"（最高值类型）
- 平台 × 类型 交叉表
模式 D — 增长归因
前置条件： outputs/_analytics/follower-log.json（从 skill-data-tracker 快照底座导出的派生视图，schema 与字段映射见 references/follower-log-schema.md）
步骤：
- 读取 follower-log.json，若不存在则输出提示并跳过本模式
- 将每条发布事件与发布后 24h / 48h / 7d 的粉丝变化关联
- 计算每条发布的粉丝增量（发布后快照 - 发布前最近快照）
- 按粉丝影响力排名
输出：
- 发布事件粉丝影响排名表：标题 / 平台 / 发布时间 / 24h增量 / 48h增量 / 7d增量
- 高增长条目的共性分析（类型、标签、时段）
- 若 follower-log.json 不存在，输出：
"模式 D 需要粉丝数据。请在 outputs/_analytics/follower-log.json 中记录粉丝快照数据，schema 见 skill-publish-analytics/references/follower-log-schema.md"
输出格式
每个模式的输出均包含以下部分：
## 数据摘要
- 总条目数 / 日期范围 / 涉及平台
- 各指标覆盖率（非 null 比例）
## 分析结果
（模式对应的表格和图表）
## 关键发现
1. （最重要的可执行洞察）
2. （第二重要）
3. （第三重要）
## 方法论
- 互动综合分计算公式
- 聚合方式（均值 / 中位数）
- 时段划分标准
## 局限性
- 样本量说明
- null 数据覆盖率
- "关联性不等于因果性 — 时段/标签分析反映相关关系，不能直接推导因果"Profile 感知
- 有 Profile：
- 按
profile字段过滤，只分析当前账号的数据 - 使用 Profile 中的 platform 和 timezone 字段
- 输出标注 "当前分析范围：{profile_name} @ {platform}"
- 按
- 无 Profile：
- 分析全部条目，按平台分组展示
- 报告附注："提供 Profile 可按账号过滤数据并使用平台特定时区"
规则
- 不捏造数据 — 所有数字必须来自 publish-log.json，不得推测或补全
- 报告样本量 — 每个分析桶必须标注条目数
- 关联非因果 — 时段和标签分析中必须注明"关联性不等于因果性"
- 样本不足警告 — 全量 < 10 条时在报告头部加粗警告
- 计算透明 — 展示每个指标的计算公式和聚合方式
自研备注
参考产品：Metricool analytics、Iconosquare insights、SocialBee performance reports、Later analytics。 本 SKILL 侧重中文社媒平台（小红书、抖音、B站、微博）的发布节奏与互动规律分析。
© ZJU-REAL, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (scripts, references) in skills/openclaw/skill-publish-analytics of ZJU-REAL/Easel.
- SKILL.md
- EASEL-META.md
- references/follower-log-schema.md
- scripts/analyze.py
Open the folder on GitHubat commit ede33b8
Compare with similar skills
Skill Publish Analytics 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 |
|---|---|---|---|---|---|---|
| Skill Publish Analytics this skillZJU-REAL/Easel | 3.4k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
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| Run Mv Hoi Reconstructionnvidia-isaac/video_to_data | 861 | — | ~1.5k | Automated safety check: Pass | Custom licence | |
| Company Analysiszhu1090093659/dsh-trading | 238 | — | ~4.2k | Automated safety check: Pass | Custom licence | |
| Windbg Diagnostic Methodmicrosoft/win-dev-skills | 466 | — | ~1.9k | Automated safety check: Pass | MIT |
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Method and tooling for measuring how AI-generated a piece of prose reads, in Japanese or English.
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微信公众号文章排版引擎：把 Markdown / Word(.docx) / PDF / 纯文本转成可直接粘贴进公众号编辑器的 HTML，自动章节编号、关键词标记、引言卡、目录、代码块、图片/GIF、作者签名；主题从 references/theme-index.md…
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微信公众号文章自动创作与发布工具。给定参考文章、文字或文档，自动搜索整理全网相关信息、生成图文并茂的公众号文章，并发布到微信公众号草稿箱。特别强调反 AI 检测写作。
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社媒卡片视觉设计系统：提供配色、中文字体层级、满画幅布局、品类骨架和死空白/密度质检，避免模板化 PPT 与廉价 AI 感。
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生成电商商品视觉方案：主图概念、场景图、详情页视觉方向和 AI 生图 Prompt. An agent skill from ZJU-REAL/Easel.
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将数据或文字内容转化为可视化信息图，支持静态（AntV）和动画 GIF 两种模式。当用户需要制作信息图、数据可视化、流程图、对比图、动画图表、GIF 图表、思维导图、SWOT 分析图时调用。本地渲染信息图/GIF 动画；要单张静态图片 URL 用 chart-visualization，要 CSV/JSON→整页报告用 data-report
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长篇小说/网文连载创作：从世界观、人设和三级大纲写到逐章正文，并用文件化状态维护伏笔、前情和跨章一致性. An agent skill from ZJU-REAL/Easel.
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Questions about Skill Publish Analytics
What does Skill Publish Analytics do?
分析发布日志数据，从发布时间、标签效果、内容类型、粉丝增长四个维度归因内容表现，输出可执行的优化建议. An agent skill from ZJU-REAL/Easel. Skill Publish Analytics is an agent skill from ZJU-REAL/Easel.
When should I use Skill Publish Analytics?
Skill Publish Analytics fits situations like: tasks that involve Performance reviews.
How do I install Skill Publish Analytics in Claude Code?
Run `npx skills add ZJU-REAL/Easel --skill skill-publish-analytics -a claude-code`. Or copy the skill folder (skills/openclaw/skill-publish-analytics in ZJU-REAL/Easel) into .claude/skills/skill-publish-analytics in your project. Claude Code loads it when a task matches its description.
How do I install Skill Publish Analytics in Codex?
Run `npx skills add ZJU-REAL/Easel --skill skill-publish-analytics -a codex`. Or copy the skill folder (skills/openclaw/skill-publish-analytics in ZJU-REAL/Easel) into .agents/skills/skill-publish-analytics in your project. Codex loads it when a task matches its description.
Can I use Skill Publish Analytics 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 ZJU-REAL/Easel --skill skill-publish-analytics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-publish-analytics, .gemini/skills/skill-publish-analytics, .github/skills/skill-publish-analytics and .opencode/skills/skill-publish-analytics in your project.
What does Skill Publish Analytics need to run?
Going by SKILL.md and its folder, Skill Publish Analytics needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Skill Publish Analytics 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 Skill Publish Analytics 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Skill Publish Analytics use?
Skill Publish Analytics is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Skill Publish Analytics use?
About 1.2k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 631 tokens, read only when the agent opens those files.
What are the alternatives to Skill Publish Analytics?
Skills that share tags, products or a category with Skill Publish Analytics: Wp Performance Review (elvismdev/claude-wordpress-skills, 235 stars), Align Human (agentscope-ai/OpenJudge, 871 stars), Run Mv Hoi Reconstruction (nvidia-isaac/video_to_data, 861 stars) and Company Analysis (zhu1090093659/dsh-trading, 238 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Skill Publish Analytics?
ZJU-REAL (a GitHub organization) maintains it in ZJU-REAL/Easel, which has 3,441 GitHub stars. The repository holds 114 skills in this directory. The repository was last updated on October 11, 2026.
Source: ZJU-REAL/Easel on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.