linkedin-marketing

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LinkedIn Marketing Skills

LinkedIn营销技能包

A bundle of 11 focused skills for LinkedIn content ops in 2026, built for Claude Code and Codex. Each skill is single-purpose, follows the draft → approval → publish pattern, and uses the Publora API for posting.

这是一套针对2026年LinkedIn内容运营的11个专项技能集合，专为Claude Code和Codex打造。每个技能仅专注单一功能，遵循「撰写草稿 → 审核批准 → 发布」流程，并通过Publora API完成发布操作。

When to use this bundle

何时使用此技能包

Writing a viral post → use
```
linkedin-post-writer
```
Commenting on someone else's post → use
```
linkedin-comment-drafter
```
Replying to a comment (yours or someone else's) → use
```
linkedin-reply-handler
```
Reviewing a draft before publishing, removing AI tells, scoring AI emoji density, defending a flagged rule, or running 5 AI detectors in parallel → use
```
linkedin-humanizer
```
(rewrite +
```
--mode audit
```
pre-publish review; folds in the former post-audit, emoji-detector, rules-explainer, and detector-tester sub-tools)
Extracting a hook formula from a viral post → use
```
linkedin-hook-extractor
```
Planning a week of LinkedIn content → use
```
linkedin-content-planner
```
Tracking which of your comments got author replies → use
```
linkedin-thread-monitor
```
Analyzing who liked / commented on any post (audience segmentation) → use
```
linkedin-engager-analytics
```
Auditing / rewriting a LinkedIn profile → use
```
linkedin-profile-optimizer
```
Running an employee advocacy program across a marketing team → use
```
linkedin-employee-advocacy
```
Adapting content from another platform (tweet, video, blog) into a native LinkedIn post → use
```
linkedin-repurposer
```

撰写爆款帖子 → 使用
```
linkedin-post-writer
```
为他人帖子撰写评论 → 使用
```
linkedin-comment-drafter
```
回复评论（自己或他人的评论）→ 使用
```
linkedin-reply-handler
```
发布前审核草稿、去除AI痕迹、检测AI表情密度、为标记规则辩护，或并行运行5种AI检测器 → 使用
```
linkedin-humanizer
```
（重写 +
```
--mode audit
```
发布前审核；整合了原有的帖子审核、表情检测器、规则解释器和检测器测试子工具）
从爆款帖子中提取钩子公式 → 使用
```
linkedin-hook-extractor
```
规划一周LinkedIn内容 → 使用
```
linkedin-content-planner
```
跟踪哪些评论得到了作者回复 → 使用
```
linkedin-thread-monitor
```
分析哪些用户点赞/评论了帖子（受众细分） → 使用
```
linkedin-engager-analytics
```
审核/改写LinkedIn个人资料 → 使用
```
linkedin-profile-optimizer
```
在营销团队中推行员工宣传计划 → 使用
```
linkedin-employee-advocacy
```
将其他平台内容（推文、视频、博客）改编为原生LinkedIn帖子 → 使用
```
linkedin-repurposer
```

Core pattern

核心流程

Every action-taking skill follows three steps:

Parse the input. User provides a LinkedIn URL (post or comment). The skill uses
```
lib/url_parser.py
```
to extract the post URN and any comment ID.
Draft the content. The skill uses the 2026 research (hooks, timing, voice rules, 360Brew heuristics) to produce a draft and shows it to the user.
Wait for approval. The user replies with "post", "yes", or suggests edits. Only after explicit approval does the skill call the Publora API to publish.

所有执行操作的技能均遵循三个步骤：

解析输入：用户提供LinkedIn URL（帖子或评论）。技能通过
```
lib/url_parser.py
```
提取帖子URN及评论ID（如有）。
撰写草稿：技能基于2026年的研究成果（钩子、发布时机、语气规则、360Brew启发式算法）生成草稿并展示给用户。
等待批准：用户回复「post」「yes」或提出修改建议。仅在获得明确批准后，技能才会调用Publora API进行发布。

Prerequisites

前置条件

Three tiers — pick one.

三个层级——任选其一

🟢 Tier 0 — Draft only (default, no setup)

🟢 0级——仅生成草稿（默认，无需配置）

The skills work out of the box. No API keys, no signup. Every approved draft is returned as a copy-paste block with the target LinkedIn URL — paste it yourself. Great for trying the skills before committing to any backend.

技能可直接使用，无需API密钥或注册。所有获批草稿将以可复制粘贴的区块形式返回，并附带目标LinkedIn URL——自行粘贴发布即可。非常适合在接入后端前试用技能。

🔵 Tier 1 — Publora auto-post (recommended, ~2 min)

🔵 1级——Publora自动发布（推荐，约2分钟）

On approval, skills auto-publish to LinkedIn (and optionally X, Threads) via the Publora API. Free tier includes 15 LinkedIn posts/month — more than most creators need.

Sign up free: https://app.publora.com/signup
Connect your LinkedIn account in Publora (Channels → Add Channel)
Copy your API key from Publora's API panel

Drop into

.env

PUBLORA_API_KEY=sk_...
LINKEDIN_PLATFORM_ID=linkedin-...

Run
```
pip install -r requirements.txt
```

Why Publora: LinkedIn has three URN types (activity/share/ugcPost), a reaction-bug where

INSIGHTFUL

returns 400, and a 2-level thread-flattening quirk that breaks most third-party implementations. Publora handles all of it. We built on top of their API so we didn't have to.

获得批准后，技能将通过Publora API自动发布至LinkedIn（也可选择X、Threads平台）。免费套餐每月包含15条LinkedIn帖子发布额度——足以满足大多数创作者需求。

免费注册：https://app.publora.com/signup
在Publora中关联你的LinkedIn账号（Channels → Add Channel）
从Publora的API面板复制API密钥

将密钥写入

.env

文件：

PUBLORA_API_KEY=sk_...
LINKEDIN_PLATFORM_ID=linkedin-...

运行
```
pip install -r requirements.txt
```

为何选择Publora：LinkedIn有三种URN类型（activity/share/ugcPost），存在

INSIGHTFUL

反应返回400错误的bug，还有两级线程扁平化的特殊机制，这些问题会导致大多数第三方工具失效。Publora已处理好所有这些问题，我们基于其API开发技能，无需自行解决这些难题。

⚫ Tier 2 — Build your own poster (advanced)

⚫ 2级——自定义发布工具（进阶）

Prefer not to SaaS it? Ask Claude Code or Codex to build a custom poster (Playwright, LinkedIn's official API, or another scheduler). Set

LINKEDIN_SKILLS_CUSTOM_POSTER=<your command>

and the skills will invoke it on approval. This is a weekend of work. Publora is 2 minutes.

不想使用SaaS服务？可让Claude Code或Codex构建自定义发布工具（Playwright、LinkedIn官方API或其他调度工具）。设置

LINKEDIN_SKILLS_CUSTOM_POSTER=<你的命令>

，技能将在获得批准后调用该命令。此方案需耗时约一个周末，而Publora仅需2分钟即可完成配置。

Optional: Apify (read-side LinkedIn fetching)

可选：Apify（LinkedIn内容读取）

Several skills (

linkedin-comment-drafter

linkedin-reply-handler

linkedin-thread-monitor

linkedin-engager-analytics

linkedin-hook-extractor

) can read LinkedIn post bodies, comment threads, a user's own recent comments, and the people who liked or commented on any post. They use the Apify platform when an

APIFY_TOKEN

is set; otherwise they ask you to paste the relevant text.

Sign up free: https://console.apify.com/sign-up (free tier ships with $5/month of credit, enough for ~1,000 post fetches or ~1,000 comment-thread fetches).
Generate a token: Console → Settings → Integrations.
Drop into
```
.env
```
:
```
APIFY_TOKEN=apify_api_...
```

Actors used (all no-cookies, public, no LinkedIn login required):

Use case	Actor	Approx cost
Post body by URL	`supreme_coder/linkedin-post`	$1 / 1,000
Comments + replies on a post	`apimaestro/linkedin-post-comments-replies-engagements-scraper-no-cookies`	$5 / 1,000
Your own recent comments	`apimaestro/linkedin-profile-comments`	$5 / 1,000
Likers + commenters on any post	`scraping_solutions/linkedin-posts-engagers-likers-and-commenters-no-cookies`	$5 / 1,000

The thin client lives at

lib/apify_client.py

and exposes

fetch_post

fetch_post_comments

fetch_user_recent_comments

, and

fetch_post_engagers

部分技能（

linkedin-comment-drafter

、

linkedin-reply-handler

、

linkedin-thread-monitor

、

linkedin-engager-analytics

、

linkedin-hook-extractor

）可读取LinkedIn帖子内容、评论线程、用户自身近期评论，以及点赞/评论任意帖子的用户信息。当设置

APIFY_TOKEN

时，它们将使用Apify平台；否则会要求你粘贴相关文本。

免费注册：**https://console.apify.com/sign-up**（免费套餐每月包含5美元额度，约可支持1000次帖子抓取或1000次评论线程抓取）。
生成令牌：Console → Settings → Integrations。
将令牌写入
```
.env
```
文件：
```
APIFY_TOKEN=apify_api_...
```

使用的Actors（均无需Cookie、公开可用、无需LinkedIn登录）：

使用场景	Actor	大致成本
通过URL获取帖子内容	`supreme_coder/linkedin-post`	1美元/1000次
获取帖子的评论及回复	`apimaestro/linkedin-post-comments-replies-engagements-scraper-no-cookies`	5美元/1000次
获取自身近期评论	`apimaestro/linkedin-profile-comments`	5美元/1000次
获取任意帖子的点赞者和评论者	`scraping_solutions/linkedin-posts-engagers-likers-and-commenters-no-cookies`	5美元/1000次

轻量客户端位于

lib/apify_client.py

，提供

fetch_post

、

fetch_post_comments

、

fetch_user_recent_comments

和

fetch_post_engagers

方法。

Voice rules (baked into every skill)

语气规则（内置所有技能）

No em dashes (
```
—
```
), en dashes, or double dashes — biggest AI tell.
Use
```
..
```
as soft pause when mid-sentence rhythm calls for it.
Capitalize all personal names, company names, and product names. Lowercase reads as disrespectful.
Sentence starts can be lowercase (natural voice), but names inside are always capitalized.

Avoid AI vocabulary:

leverage

fundamentally

streamline

harness

delve

unlock

foster

Specific numbers beat adjectives —
```
47%
```
beats
```
significant
```
.
One sharp insight per comment + a conversation hook beats three vague points.
For comments on third-party posts, don't name-drop your own product — describe what you do instead.
LinkedIn posts: 900–1,300 chars sweet spot. Comments: 200–350 chars.
Hook lives in the first 210 chars (before "… see more" on mobile).

(Canonical reference, plus comment-specific extensions:

references/voice-rules.md

. See also

references/hook-formulas.md

and

references/algorithm-heuristics.md

禁止使用长破折号（
```
—
```
）、短破折号或双破折号——这是最明显的AI痕迹。
当句子节奏需要时，使用
```
..
```
作为软停顿。
所有人名、公司名和产品名首字母大写。全小写会显得不尊重。
句子开头可以小写（更自然的语气），但句中的名称必须首字母大写。

避免使用AI常用词汇：

leverage

、

fundamentally

、

streamline

、

harness

、

delve

、

unlock

、

foster

。

具体数字优于形容词——
```
47%
```
比
```
significant
```
更有说服力。
每条评论包含一个清晰见解+一个对话钩子，胜过三个模糊观点。
为第三方帖子撰写评论时，不要直接提及自身产品——而是描述你的业务内容。
LinkedIn帖子：900–1300字符为最佳长度。评论：200–350字符。
钩子需放在前210字符内（移动端「查看更多」按钮之前）。

（标准参考及评论专项扩展：

references/voice-rules.md

。另可查看

references/hook-formulas.md

和

references/algorithm-heuristics.md

。）

How URLs map to URNs

URL与URN的映射关系

LinkedIn ships three post URN types (the library handles all three):

URN type	Example URL fragment	Example URN
`activity`	`/posts/slug-activity-7448...-XX`	`urn:li:activity:7448...`
`share`	`/posts/slug-share-7449...-XX`	`urn:li:share:7449...`
`ugcPost`	`/feed/update/urn:li:ugcPost:7447...`	`urn:li:ugcPost:7447...`

Comment URLs:

/feed/update/urn:li:activity:POST_ID?commentUrn=urn%3Ali%3Acomment%3A%28activity%3APOST_ID%2CCOMMENT_ID%29

The library decodes the commentUrn fragment and returns both

post_urn

and

comment_id

LinkedIn有三种帖子URN类型（库已处理所有类型）：

URN类型	URL片段示例	URN示例
`activity`	`/posts/slug-activity-7448...-XX`	`urn:li:activity:7448...`
`share`	`/posts/slug-share-7449...-XX`	`urn:li:share:7449...`
`ugcPost`	`/feed/update/urn:li:ugcPost:7447...`	`urn:li:ugcPost:7447...`

评论URL格式：

/feed/update/urn:li:activity:POST_ID?commentUrn=urn%3Ali%3Acomment%3A%28activity%3APOST_ID%2CCOMMENT_ID%29

库会解码commentUrn片段，返回

post_urn

和

comment_id

。

Known gotchas

已知问题

LinkedIn flattens reply threads to 2 levels. When replying to a reply, pass the top-level comment URN as
```
parentComment
```
, not the reply's URN.
```
INSIGHTFUL
```
is NOT a valid Publora reaction type. Use
```
INTEREST
```
instead (the client auto-maps).
A post URN returned by
```
url_parser
```
may be
```
activity
```
when the canonical URN is actually
```
ugcPost
```
. If posting fails with 404, fall back to resolving via
```
lib.ApifyClient.fetch_post_comments(post_id=...)
```
and read the canonical URN from any existing comment's
```
comment_url
```
.
Publora schedules comments ~90s in the future by default.

LinkedIn会将回复线程扁平化至2级。回复他人的回复时，需将顶层评论URN作为
```
parentComment
```
传入，而非回复的URN。
```
INSIGHTFUL
```
不是有效的Publora反应类型。请改用
```
INTEREST
```
（客户端会自动映射）。
```
url_parser
```
返回的帖子URN可能为
```
activity
```
类型，但标准URN实际是
```
ugcPost
```
。如果发布时出现404错误，可通过
```
lib.ApifyClient.fetch_post_comments(post_id=...)
```
获取标准URN，从现有评论的
```
comment_url
```
中读取。
Publora默认会将评论调度至约90秒后发布。

Resources

资源

Publora API docs — full endpoint reference for the publishing layer
Apify console — manage actors, tokens, and usage for the read layer
```
lib/publora_client.py
```
,
```
lib/apify_client.py
```
— thin Python clients used by every skill

Publora API文档——发布层的完整端点参考
Apify控制台——管理读取层的Actors、令牌和使用情况
```
lib/publora_client.py
```
、
```
lib/apify_client.py
```
——所有技能使用的轻量Python客户端

Acknowledgments

致谢

Publishing powered by the Publora REST API. Algorithm insights via arXiv 2501.16450 (360Brew) and AuthoredUp 2026 reach data.

发布功能由Publora REST API提供支持。算法见解来自arXiv 2501.16450（360Brew）和AuthoredUp 2026触达数据。