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linkedin-post-auditLinkedIn 后期审核

Agent Skill

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

总安装

2,521

周安装

103

GitHub Stars

公开资料未说明

下载量

816
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:linkedin-post-audit(LinkedIn 后期审核)
来源仓库:https://github.com/sergebulaev/linkedin-post-audit
安装命令:
openclaw skills install linkedin-post-audit
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install linkedin-post-audit

简介

审核 LinkedIn 帖子草稿的算法合规性、人工智能线索、结构和长度;提供通过/失败结果以及详细的修复和发布建议。

SKILL.md

name
linkedin-post-audit
description
Audit a LinkedIn post draft against 2026 algorithm heuristics and voice rules before publishing. Use when the user has a draft and wants to catch AI tells, algorithm penalties, or structural issues before shipping. Returns a pass/fail report with specific fixes and optional auto-rewrites. Keywords: post audit, linkedin review, algorithm check, 360Brew, humanizer, AI detection, pre-publish check.

LinkedIn Post Audit

Run any post draft through the 2026 heuristic checklist. Catches AI tells, timing/format issues, length violations, and structural weaknesses before publishing.

When to use

  • Before publishing a hand-written or AI-drafted post
  • When linkedin-post-writer finishes a draft (auto-invoked)
  • When a recent post didn't land and the user wants a post-mortem

Input

  • A post draft (plain text)
  • Optional: target audience, scheduled time, format (text / carousel / video / image)

Output

  • Pass/Fail header
  • Blockers (must fix before publishing): em dashes, AI vocab, external links in body
  • Warnings (ship-risky): uniform sentence rhythm, missing numbers, generic close
  • Score estimates: OriginalityAI AI-likelihood, approximate first-hour reach fit
  • Suggested fixes: inline rewrites for each issue
  • Timing recommendation: best window given audience

Checks

Blockers (auto-fail)

  1. Em dash / en dash / double dash present
  2. External link in body (not in first comment)
  3. Post exceeds 3,000 chars (LinkedIn hard limit)
  4. Opens with "In today's fast-paced world..." or similar
  5. Ends with "What do you think?" or "Thoughts?"
  6. Contains AI vocabulary blacklist words (see references/ai-tells.md)
  7. Frames LinkedIn as inferior in a LinkedIn post (algo penalty)

Warnings (flag with suggested fix)

  1. Hook doesn't fit in first 210 chars (mobile …see more cutoff)
  2. Length outside 900-1,300 sweet spot (or 1,500-1,900 for long-form with breaks)
  3. Uniform sentence length (all 15-22 words)
  4. No specific number per 100 words
  5. No named entity
  6. No first-person sensory detail
  7. Rule-of-three list without receipts
  8. More than 2 hashtags
  9. User's own product named more than once
  10. Missing reaction-prompting moment (vulnerability, stakes, question)
  11. Passive voice >10%

Info (neutral notes)

  1. Suggested posting time given audience
  2. Format recommendation (text / carousel / video) given topic
  3. Similar-hook detection: if this post's first 100 chars match a recent post

Steps

  1. Parse draft into sentences, paragraphs, first-210-char hook.
  2. Run each blocker check; collect failures.
  3. If any blockers, return FAIL with specific fix suggestions; optionally offer auto-rewrite.
  4. If no blockers, run warnings.
  5. Estimate OriginalityAI score (heuristic proxy: avg sentence length variance, unique 3-gram ratio, passive voice ratio).
  6. Return structured report.

Example

Input: "In today's fast-paced world, businesses are fundamentally leveraging AI to unlock massive ROI — here's what I learned..."
Output: - FAIL (3 blockers) - L1 "In today's fast-paced world" (filler opener) - L1 "fundamentally" (AI vocab) - L1 "leveraging" (AI vocab) - L1 em dash - Suggested rewrite: "Businesses are using AI to cut costs 40%. Here's what I learned."

Files

  • SKILL.md — this file
  • references/ai-tells.md — complete blacklist + regex patterns
  • references/audit-checklist.md — full 20-point checklist with thresholds

Related skills

  • linkedin-humanizer — aggressive rewrite if audit fails
  • linkedin-post-writer — regenerate draft using a proven formula

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

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按下载量换算624

安全审计

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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