Agent skill

Skill Ugc Discovery

by ZJU-REAL in ZJU-REAL/Easel

发现用户生成内容(UGC)。搜索与创作者品牌/账号相关的粉丝内容、测评、提及和社区讨论, 输出高价值 UGC 列表与互动建议。当用户说"谁提到了我"、"粉丝内容"、"品牌提及"、 "UGC 发现"、"用户口碑"、"测评搜索"、"社区讨论"时触发。

Apache-2.0Auto-check passedMarketing & SEO

Install Skill Ugc Discovery

skills CLI
$ npx skills add ZJU-REAL/Easel --skill skill-ugc-discovery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ZJU-REAL/Easel skill-ugc-discovery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ZJU-REAL/Easel.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/openclaw/skill-ugc-discovery .claude/skills/skill-ugc-discovery && rm -rf skills-src

Use ~/.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/

Facts

Skill name
skill-ugc-discovery
GitHub stars
3.4k
Token cost
~674 tokens
SKILL.md length
192 words
Files
2
Skills in repo
114
Repo updated
First seen
Licence
Apache-2.0

At a glance

发现用户生成内容(UGC)。搜索与创作者品牌/账号相关的粉丝内容、测评、提及和社区讨论, 输出高价值 UGC 列表与互动建议。当用户说"谁提到了我"、"粉丝内容"、"品牌提及"、 "UGC 发现"、"用户口碑"、"测评搜索"、"社区讨论"时触发。

  • Works in 10 steps: 确认关键词:从用户输入获取 brand_keywords。若有… → 确定搜索范围:根据 platforms 参数确定目标平台;根据… → 执行三条路径 → …
  • Tasks that involve Influencer and creator marketing
  • SKILL.md covers 输入, 输出, 三条发现路径 and 执行步骤, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Skill Ugc Discovery is an agent skill from ZJU-REAL/Easel. 发现用户生成内容(UGC)。搜索与创作者品牌/账号相关的粉丝内容、测评、提及和社区讨论, 输出高价值 UGC 列表与互动建议。当用户说"谁提到了我"、"粉丝内容"、"品牌提及"、 "UGC 发现"、"用户口碑"、"测评搜索"、"社区讨论"时触发。

Its SKILL.md is about 670 tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `EASEL-META.md`).

It sits in Marketing & SEO, covering Influencer and creator marketing. 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 Influencer and creator marketing

Example prompts

  • “UGC 发现”
  • “/skill-ugc-discovery”

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  1. 确认关键词:从用户输入获取 brand_keywords。若有 Profile,从 identity.md 补充账号名、品牌名、产品名等关键词。
  2. 确定搜索范围:根据 platforms 参数确定目标平台;根据 content_type 调整搜索关键词侧重(reviews 侧重"测评/推荐",complaints 侧重"吐槽/避坑")。
  3. 执行三条路径
  4. 内容采集:对搜索结果中高相关的链接,用 WebFetch 获取内容摘要和互动数据。
  5. 去重与分类:去除重复结果,按情感倾向分类(正面/中性/负面)。
  6. 情感判断:基于内容文本判断情感倾向,区分事实性提及和评价性内容。
  7. 价值排序:按互动量和内容质量排序,筛选出高价值 UGC(值得互动/转发的内容)。
  8. 负面标注:单独列出负面反馈,按严重程度排序,给出建议回应策略。
  9. 互动建议:针对高价值 UGC 给出具体互动建议(转发并感谢 / 评论区互动 / 联动合作邀约)。
  10. 输出报告:按输出模板生成完整报告,保存到 outputs/。

What it can do on your machine

Read from SKILL.md and the folder at commit 278f420. 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 (its code samples are markdown).

    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 Ugc Discovery loads about 674 tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 192 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~674

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 ZJU-REAL/Easel at commit 278f420, republished under its Apache-2.0 licence (© ZJU-REAL). 192 words, ~674 tokens.

Download SKILL.mdSave it as .claude/skills/skill-ugc-discovery/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
skill-ugc-discovery
description
发现用户生成内容(UGC)。搜索与创作者品牌/账号相关的粉丝内容、测评、提及和社区讨论, 输出高价值 UGC 列表与互动建议。当用户说"谁提到了我"、"粉丝内容"、"品牌提及"、 "UGC 发现"、"用户口碑"、"测评搜索"、"社区讨论"时触发。
layer
discover

UGC 内容发现

搜索与创作者品牌/账号相关的用户生成内容,发现粉丝内容、测评、提及和社区讨论,输出可互动的 UGC 列表。

输入

字段必填说明
brand_keywords是品牌名/账号名,可多个(逗号分隔)
platforms否聚焦平台(小红书/B站/微博/知乎/抖音),默认全平台
content_type否reviews / mentions / fan_art / complaints / all(默认 all)
time_range否recent / this_month / this_quarter(默认 recent)

输出

markdown
# UGC 内容发现报告

## 发现概览
- 搜索关键词: {keywords}
- 搜索平台: {platforms}
- 发现 UGC 内容: {count} 条
- 正面/中性/负面: {positive}/{neutral}/{negative}

## 高价值 UGC(推荐互动/转发)
| # | 平台 | 内容摘要 | 来源链接 | 互动量估计 | 情感 | 建议动作 |
|---|------|---------|---------|-----------|------|---------|

## 负面反馈(需关注)
| # | 平台 | 内容摘要 | 来源链接 | 严重程度 | 建议回应 |
|---|------|---------|---------|---------|---------|

## 互动建议
- {具体建议: 转发/评论/联动/感谢}

三条发现路径

路径 1 — 平台搜索(主路径)

用 WebSearch 搜索创作者/品牌名相关内容:

搜索关键词组合:

  • "{账号名} 推荐" / "{账号名} 测评" / "{品牌名} 体验"
  • "{账号名} site:xiaohongshu.com" / "site:bilibili.com"
  • "{账号名} 好物" / "{账号名} 同款"

按 platforms 参数聚焦平台,未指定时逐平台搜索。

路径 2 — 话题/标签监控

搜索创作者品牌话题和关联标签:

  • "#{账号名}# site:weibo.com" — 微博话题
  • "#{品牌话题}#" — 跨平台品牌话题
  • "{账号名} 话题" / "{品牌名} 标签"
路径 3 — 社区讨论扫描

搜索论坛、问答社区、讨论帖:

  • "{品牌名} 怎么样" / "{产品名} 好用吗" — 知乎/贴吧
  • "{品牌名} 值得买吗" / "{账号名} 靠谱吗" — 购买决策讨论
  • "{品牌名} 吐槽" / "{品牌名} 避坑" — 负面反馈定向搜索

执行步骤

  1. 确认关键词:从用户输入获取 brand_keywords。若有 Profile,从 identity.md 补充账号名、品牌名、产品名等关键词。
  2. 确定搜索范围:根据 platforms 参数确定目标平台;根据 content_type 调整搜索关键词侧重(reviews 侧重"测评/推荐",complaints 侧重"吐槽/避坑")。
  3. 执行三条路径:
    • 路径 1:对每个平台执行 2-3 组关键词搜索(WebSearch)
    • 路径 2:搜索品牌话题和标签
    • 路径 3:搜索社区讨论
  4. 内容采集:对搜索结果中高相关的链接,用 WebFetch 获取内容摘要和互动数据。
  5. 去重与分类:去除重复结果,按情感倾向分类(正面/中性/负面)。
  6. 情感判断:基于内容文本判断情感倾向,区分事实性提及和评价性内容。
  7. 价值排序:按互动量和内容质量排序,筛选出高价值 UGC(值得互动/转发的内容)。
  8. 负面标注:单独列出负面反馈,按严重程度排序,给出建议回应策略。
  9. 互动建议:针对高价值 UGC 给出具体互动建议(转发并感谢 / 评论区互动 / 联动合作邀约)。
  10. 输出报告:按输出模板生成完整报告,保存到 outputs/。

Profile 感知

有 Profile 时:

  • 读取 identity.md 获取账号名、品牌名、产品线关键词,自动扩展搜索关键词
  • 读取 platforms.md 确定活跃平台,优先搜索这些平台
  • 读取 audience.md 判断哪些 UGC 来自目标受众群体(更具互动价值)
  • 读取 style.md 匹配互动建议的语气和方式

无 Profile 时:

  • 用户必须提供 brand_keywords,否则提示用户补充
  • 全平台搜索,不做平台优先级排序
  • 互动建议给出通用策略
  • 附注"提供 Profile 可获得更精准的 UGC 发现和互动建议"

规则

  1. 区分事实与分析 — 搜索到的内容是事实,情感判断是分析,两者分开标注
  2. 必须附来源链接 — 每条发现的 UGC 必须附上来源 URL
  3. 不编造 UGC — 搜索无结果时如实报告"未发现相关 UGC",不捏造内容
  4. 优先可操作内容 — 排序时优先展示高互动量、值得回应的 UGC
  5. 负面内容不回避 — 负面反馈单独列出,给出建设性的回应建议
  6. 标注数据局限 — WebSearch 结果有时效和覆盖限制,明确标注搜索范围和局限性

自研溯源与参考项目见同目录 EASEL-META.md。

© 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 1 other file in skills/openclaw/skill-ugc-discovery of ZJU-REAL/Easel.

  • SKILL.md
  • EASEL-META.md

Open the folder on GitHubat commit 278f420

Compare with similar skills

Skill Ugc Discovery 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 Ugc Discovery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skill Ugc Discovery this skillZJU-REAL/Easel3.4k—~674Automated safety check: PassApache-2.0
Audience ResearchScrapeCreators/social-media-research-skills3.4k—~635Automated safety check: NotesMIT
Influencer Discoverytigerless-labs/influencer-discovery212—~2.5kAutomated safety check: NotesNone
Opencloneteam-attention/openclone130—~2.6kAutomated safety check: NotesMIT
Reelclaw Adsdansugc/reelclaw145—~3.9kAutomated safety check: NotesMIT
Affiliate CheckAffitor/affiliate-skills701—~808Automated safety check: NotesMIT

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Categories

Questions about Skill Ugc Discovery

What does Skill Ugc Discovery do?

发现用户生成内容(UGC)。搜索与创作者品牌/账号相关的粉丝内容、测评、提及和社区讨论, 输出高价值 UGC 列表与互动建议。当用户说"谁提到了我"、"粉丝内容"、"品牌提及"、 "UGC 发现"、"用户口碑"、"测评搜索"、"社区讨论"时触发。. Skill Ugc Discovery is an agent skill from ZJU-REAL/Easel.

When should I use Skill Ugc Discovery?

Skill Ugc Discovery fits situations like: tasks that involve Influencer and creator marketing.

How do I install Skill Ugc Discovery in Claude Code?

Run `npx skills add ZJU-REAL/Easel --skill skill-ugc-discovery -a claude-code`. Or copy the skill folder (skills/openclaw/skill-ugc-discovery in ZJU-REAL/Easel) into .claude/skills/skill-ugc-discovery in your project. Claude Code loads it when a task matches its description.

How do I install Skill Ugc Discovery in Codex?

Run `npx skills add ZJU-REAL/Easel --skill skill-ugc-discovery -a codex`. Or copy the skill folder (skills/openclaw/skill-ugc-discovery in ZJU-REAL/Easel) into .agents/skills/skill-ugc-discovery in your project. Codex loads it when a task matches its description.

Can I use Skill Ugc Discovery 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-ugc-discovery -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-ugc-discovery, .gemini/skills/skill-ugc-discovery, .github/skills/skill-ugc-discovery and .opencode/skills/skill-ugc-discovery in your project.

What does Skill Ugc Discovery need to run?

SKILL.md names no scripts, command-line tools or credentials: Skill Ugc Discovery is instructions for the agent only.

Does Skill Ugc Discovery 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 Ugc Discovery 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 Skill Ugc Discovery use?

Skill Ugc Discovery 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 Ugc Discovery use?

About 674 tokens (SKILL.md is roughly 2.7k 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 Skill Ugc Discovery?

Skills that share tags, products or a category with Skill Ugc Discovery: Audience Research (ScrapeCreators/social-media-research-skills, 3.4k stars), Influencer Discovery (tigerless-labs/influencer-discovery, 212 stars), Openclone (team-attention/openclone, 130 stars) and Reelclaw Ads (dansugc/reelclaw, 145 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Ugc Discovery?

ZJU-REAL (a GitHub organization) maintains it in ZJU-REAL/Easel, which has 3,376 GitHub stars. The repository holds 114 skills in this directory. The repository was last updated on October 9, 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.