li-audit
Skill2 starsChanged 43 days agoMITView on GitHub
Post-mortem on the user's published LinkedIn posts: rank by engagement rate and reach multiple, not impressions, and turn the results into next week's plan inputs. Use when the user asks "audit my posts", "what worked", pastes post stats, or wants a performance review. Reads the log file; diagnoses; prescribes.
7 other projects use a version of this.
Adopted by
Most recently taken up by
- furkanbilalakyuz/linkedin-agent-skill-trtheir own version, first seen Nov 8, 2026
- mubaid/dsh-linkedin-agenttheir own version, first seen Oct 25, 2026
- robbiehaynes/job-searchtheir own version, first seen Oct 20, 2026
- postzen-dev/linkedin-agent-skilltheir own version, first seen Oct 15, 2026
- avi691/linkedin-agent-skilltheir own version, first seen Oct 8, 2026
- nessalazne/linkedin-agent-skilltheir own version, first seen Sep 19, 2026
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 7
- Jakeschincariol/linkedin-agent-skill★ 514same name
- avi691/linkedin-agent-skill★ 5same name
- nessalazne/linkedin-agent-skill★ 3same name
- postzen-dev/linkedin-agent-skill★ 3same name
- furkanbilalakyuz/linkedin-agent-skill-tr★ 0same name
- mubaid/dsh-linkedin-agent★ 0same name
- robbiehaynes/job-search★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository12 other files it gives its agents.
- AGENTS.mdAGENTS.md
- li-carouselSkill
- li-commentSkill
- li-dmSkill
- li-humanSkill
- li-inboxSkill
- li-planSkill
- li-postSkill
- li-profileSkill
- li-replySkill
- li-repurposeSkill
- li-voiceSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 7 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 18
Read the history: ~/.agents/linkedin/log.md (then the Claude/Copilot fallbacks), which li-post appends to on every approved post.
- Distinctive: line 69
Do not recommend posting more as the fix for low engagement.
- Distinctive: line 71
If a post the user loved flopped, say so kindly and say why.
- Very specific: line 73
Write the prescription into the next li-plan run.
“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
li-audit
What already went out, judged honestly. Ranks by the metrics that matter,
finds the pattern, and feeds the result straight into next week's li-plan.
Before you audit
- Read the history:
~/.agents/linkedin/log.md(then the Claude/Copilot fallbacks), whichli-postappends to on every approved post. If the log is empty or missing, ask the user to paste their recent posts with stats. - Get the numbers per post: impressions, reactions, comments, reposts, and ideally profile follows or DMs attributed. Work with what exists; mark missing metrics as unknown rather than inferring them.
The metrics that matter
Compute per post, and rank by these, not by raw impressions:
- Engagement rate = (reactions + comments + reposts) / impressions. The headline metric. Above 3% is strong for most niches.
- Reach multiple = impressions / follower count. Above 3x means the algorithm carried it beyond the audience.
- Comment quality. Count substantive comments separately from congratulations. Ten "great post"s lose to two real questions.
- Conversion. Profile visits, follows, DMs, leads. Self-reported; take the user's word.
Show the ranking table with all four columns. Then say what the table means in two sentences. Tables do not interpret themselves.
The diagnosis
Find the pattern across the top 3 and bottom 3:
- Hook family: which of the 21 formulas won? (Cross-check with
hooks.json.) - Format: text vs carousel vs repurposed.
- Pillar: which content pillar is carrying the account?
- Topic: what subject, specifically, not "AI content".
- Length and structure: did the winners share a shape?
Then the prescription, as inputs to li-plan:
AUDIT
posts reviewed: 12 (4 weeks)
top: "The proposal story" - 4.1% engagement, 6.2x reach (hook #17, story)
bottom: "AI trends 2026" - 0.8% engagement, 1.1x reach (generic take)
pattern: stories with numbers travel; generic takes die
prescribe: 2 story posts next week, kill the trends pillar, test carousel
for the framework postRules
- Judge the post, not the person. "This post failed" is useful; "your writing is weak" is not.
- Small samples lie. Under 8 posts, present findings as hypotheses with "test:" labels, not conclusions.
- Do not recommend posting more as the fix for low engagement. The fix is almost always sharper ideas or better hooks, not volume.
- If a post the user loved flopped, say so kindly and say why. The audit is the one skill allowed to disagree with the user's taste.
- Write the prescription into the next
li-planrun. An audit nobody acts on is entertainment.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

