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Linkedin Post Audit

Name: Linkedin Post Audit
Author: sergebulaev

作者 Sergey Bulaev · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ✓ 安全检测通过

总下载

当前安装

版本数

在 OpenClaw 中安装

/install linkedin-post-audit

功能描述

Audit LinkedIn post drafts for algorithm compliance, AI cues, structure, and length; provides pass/fail results with detailed fixes and posting advice.

使用说明 (SKILL.md)

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)

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

Warnings (flag with suggested fix)

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

Info (neutral notes)

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

Steps

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

安全使用建议

This skill is an instruction-only audit tool and appears coherent, but before installing you should: (1) confirm how 'recent posts' would be supplied if you expect similarity checks — avoid granting any LinkedIn API credentials unless you trust the skill's implementation; (2) ensure the agent is not given automatic post-to-LinkedIn permission (review any auto-rewrite before publishing); (3) recognize the 'OriginalityAI' score is an internal heuristic here (no data is sent to OriginalityAI unless you wire that up yourself); and (4) if you plan to integrate it with other skills (e.g., linkedin-post-writer), review that integration's permissions so your account tokens or past-post data are not exposed unexpectedly.

功能分析

Type: OpenClaw Skill Name: linkedin-post-audit Version: 1.0.0 The `linkedin-post-audit` skill bundle is a text-analysis tool designed to optimize LinkedIn posts by checking for AI-generated markers and algorithm penalties. The logic is entirely contained within markdown instructions and reference files (`SKILL.md`, `references/ai-tells.md`, and `references/audit-checklist.md`), focusing on heuristic checks like vocabulary blacklists and structural formatting. There is no evidence of malicious intent, data exfiltration, or unauthorized command execution.

能力评估

✓ Purpose & Capability

Name, description, and all declared requirements align: this is an instruction-only auditing tool that checks text against an internal checklist and blacklist. It does not request unrelated binaries, environment variables, or credentials.

ℹ Instruction Scope

All runtime instructions operate on the provided draft and internal reference files (ai-tells.md, audit-checklist.md). One minor ambiguity: 'Similar-hook detection: if this post's first 100 chars match a recent post' implies comparing to 'recent posts' but does not specify how those posts are provided (user-supplied, cached, or fetched from LinkedIn). If the skill were implemented to fetch user posts, that would require additional permissions/API access not described here; as provided, the instructions stay within scope.

✓ Install Mechanism

No install spec and no code files beyond instruction/reference docs — nothing is written to disk or downloaded by the skill itself.

✓ Credentials

The skill declares no environment variables, credentials, or config paths. The checks described don't require external secrets or unrelated access.

✓ Persistence & Privilege

always is false and the skill is user-invocable. It does not request persistent or platform-wide privileges, nor does it instruct modifying other skills or system configuration.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install linkedin-post-audit
安装完成后，直接呼叫该 Skill 的名称或使用 /linkedin-post-audit 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.0

Initial release

元数据

Slug linkedin-post-audit

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题