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Linkedin Post Audit
by
Sergey Bulaev
· GitHub ↗
· v1.0.0
· MIT-0
82
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Install in OpenClaw
/install linkedin-post-audit
Description
Audit LinkedIn post drafts for algorithm compliance, AI cues, structure, and length; provides pass/fail results with detailed fixes and posting advice.
README (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-writerfinishes 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 morecutoff) - 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 filereferences/ai-tells.md— complete blacklist + regex patternsreferences/audit-checklist.md— full 20-point checklist with thresholds
Related skills
linkedin-humanizer— aggressive rewrite if audit failslinkedin-post-writer— regenerate draft using a proven formula
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install linkedin-post-audit - After installation, invoke the skill by name or use
/linkedin-post-audit - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release
Metadata
Frequently Asked Questions
What is Linkedin Post Audit?
Audit LinkedIn post drafts for algorithm compliance, AI cues, structure, and length; provides pass/fail results with detailed fixes and posting advice. It is an AI Agent Skill for Claude Code / OpenClaw, with 82 downloads so far.
How do I install Linkedin Post Audit?
Run "/install linkedin-post-audit" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Linkedin Post Audit free?
Yes, Linkedin Post Audit is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Linkedin Post Audit support?
Linkedin Post Audit is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Linkedin Post Audit?
It is built and maintained by Sergey Bulaev (@sergebulaev); the current version is v1.0.0.
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