# Linkedin Post Report

> LinkedIn Post Report

- Skill: `kshyam/linkedin-post-report` (Agent Skill)
- Install (CLI): `npx skillmds@latest add kshyam/linkedin-post-report`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kshyam/linkedin-post-report/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: kshyam (https://skillmd.com/u/kshyam)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/kshyam/linkedin-post-report

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# LinkedIn Post Report

Take an Apify "LinkedIn profile posts" export — a CSV or XLSX scrape of one person's LinkedIn posts, anywhere from 50 to 5,000+ rows — and produce a decision-ready report on what's actually working, so the user can tell this person what to double down on and what to drop. Treat this as a serious analytics deliverable, not a quick summary.

If no file is attached, ask for it before doing anything else.

## Handle these quirks of the export format

The obvious columns lie. Read them correctly:

- The `type` column says "post" for every row — ignore it. Derive each post's FORMAT from which media columns are populated: if `document/*` (e.g. `document/title`, `document/totalPageCount`) is filled → carousel/document; else if `postVideo/*` → video; else if `article/*` → shared article/link; else if `postImages/0/url` → image; otherwise → text-only.
- Engagement per post = `engagement/likes` + `engagement/comments` + `engagement/shares`. Reaction mix lives in `engagement/reactions/N/type` and `/count` (like, empathy, praise, funny…) — use it to read emotional register. Post text is in `content`; the author's handle and the post link are in `linkedinUrl`.
- There's no date column, but the timestamp is encoded in the activity ID (`engagement/id`, also the number after "activity-" in the URL): take it as a 64-bit integer and shift right 22 bits for Unix milliseconds (date = id >> 22, then ÷1000). Use this to build the timeline for cadence and best-day/time.

## Quantitative pass

Compute the quantitative stats across ALL posts in code so the numbers are exact. Define an outlier as a post whose engagement is some multiple of its baseline, and state the multiple you used.

## Qualitative pass

Do the qualitative read on the standouts — top and bottom performers, every outlier, and a representative sample of the middle. Don't infer what a post said from its numbers, and for visual posts don't judge from the caption alone: actually OPEN THE MEDIA and look at it.

- Download and view the image (`postImages/N/url`) and the video thumbnail (`postVideo/thumbnailUrl`).
- For carousels, pull the on-slide wording from `document/transcribedDocumentUrl` (or `document/manifest/transcribedDocumentUrl`) and view the cover/slide images (`document/coverPages/.../imageUrls`, `document/manifest/perResolutions/N/imageManifestUrl`).
- For every winning visual post, describe what's literally on it — the on-image or first-slide text and hook, the visual style (candid photo, selfie, screenshot, data chart, quote/text card, diagram, meme), the layout — and tie those visual choices to why it performed.
- Quote the hook and link the post every time.

## What the report must deliver

With specifics and real examples:

1. **Bottom line first** — in 3-4 sentences, what's working and what should change.
2. **What FORMAT wins** (text / image / carousel / video / article) — average and median engagement and sample size per format, flag any format that looks strong but rests on only a few posts, and for the winning visual formats spell out what the strong images/slides actually look like.
3. **What ANGLE / hook / topic wins** — cluster posts into the angles this person actually uses (personal story, contrarian take, how-to, news reaction, list/framework, hot take, etc.), rank them by engagement, and name the opening-line and first-slide patterns that track with high engagement.
4. **The biggest outliers** — the posts that massively over- and under-performed their baseline, each with numbers, the hook, the link, what was on the image/slides, and the best read on WHY.
5. **Stop / Continue / Start** — what to stop (formats, angles, habits that reliably underperform), what to keep, and what to test next — concrete enough to act on this week.
6. **Whatever else the data clearly supports** and a sharp strategist would want: posting cadence and consistency, best day/time, ideal length, comment-to-reaction ratio (conversation vs passive likes), reaction-mix tells (controversy vs warmth), recurring themes, signs of fatigue or decline, and the single highest-leverage change.

## Output: save two new files (don't modify the upload)

1. A written report the user can read top to bottom — clear hierarchy, bottom line up front, no filler, every claim tied to a number or a quoted/linked post.
2. An SOP for the next post, reverse-engineered from the highest outliers: the repeatable recipe spelled out step by step — the hook formula, the winning format and angle, and the visual template (what slide 1 / the image should contain and look like), with real outlier posts as worked examples.

## Before calling it done

Ground every finding in the data. If the export lacks what a section needs (e.g. too few videos to judge, or media URLs that won't open), say so plainly instead of guessing. Re-check the headline numbers against the file and confirm every format, angle, and visual claim matches what's actually in the export.