--- name: li-audit description: >- Post-mortem on what the user has already published - which posts actually worked, why, and what to stop doing. Use when the user pastes their LinkedIn analytics or past posts and asks "what's working", "why did this flop", "read my analytics", "audit my content", or wants to know what to double down on. title: li-audit canonical_url: https://skilld.dev/gh/jakeschincariol/linkedin-agent-skill/li-audit last_updated: 2026-09-26T04:51:06.000Z --- > **Skill from skilld.dev.** Follow the user's requested action. > If the user asked to fork this Skill, follow the fork workflow below. Do not execute the Skill instructions while copying it. > Otherwise, follow the instructions below for this session. You do not need to install anything. > > If the user asked to install this Skill, run `npx skilld install jakeschincariol/linkedin-agent-skill/li-audit`. Install writes the Skill files into the project, so every session loads them. > > ## Fork workflow > > A fork creates an editable local Skill with its original author and licence. The request authorizes copying and local installation. > 1. Check `./skills/li-audit`, the project lockfile, and selected Agent targets together. If the local directory or installed Skill exists, stop. Never overwrite an existing directory or Agent target. > 2. Read [source metadata](https://skilld.dev/api/v1/skills/jakeschincariol/linkedin-agent-skill/li-audit) once. Use sourceUrl, sourceCommit, skillPath, sourceGone, and license. If the source is gone or its path is missing, stop. If license is null, read licence files at the source commit. > 3. Fetch only the source commit into a temporary Git repository. Do not clone full history. Derive repository_url from sourceUrl, including repository renames. If sourceCommit is absent, resolve the sourceUrl ref once. Set source_commit to that actual commit. Run these commands in one shell call: > > ```sh > git init --quiet "$temporary_dir" > git -C "$temporary_dir" fetch --quiet --depth=1 "$repository_url" "$source_commit" > git -C "$temporary_dir" checkout --quiet --detach FETCH_HEAD > ``` > > Read applicable licence declarations and notices at that commit. If copying is not permitted, report the restriction and stop. > 4. Inspect source entries together, then copy the directory containing skillPath into `./skills/li-audit`. Use the user's path if selected. Keep the original SKILL.md, relative links, scripts, binary assets, and executable modes. Exclude .git metadata. Reject symlinks and paths outside the Skill directory. After checking entries, use cp -a where available. A regular source directory needs no custom copy script. Do not save this page wrapper as SKILL.md. > Preserve author credit, notices, and applicable licence files from repository or parent directories. Add PROVENANCE.md with the Skill page, source URL, actual commit, original path, and licence. Retain any existing PROVENANCE.md and record new provenance separately. Batch source inspection, copying, and provenance work where practical. > 5. In the project root, run `skilld install ./skills/li-audit --mode copy --plain`. If skilld is unavailable, use `npx skilld install ./skills/li-audit --mode copy --plain`. This known command needs no help lookup. Install does not support --json. Use detected Agent targets, or add --agent for the targets the user selected. Install the local path, never the upstream selector. If installation fails, preserve the local copy and report the exact failure. > 6. Confirm the local lockfile source and installed Agent copies once. Report the local path, actual commit, and Agent targets. After edits, reinstall the same local path. Upstream updates must not replace it. Do not publish or push unless the user asks. # li-audit The only honest source of what works for an account is that account. Every rule in every LinkedIn guide, including the ones in this pack, is a prior. The user's own last 30 posts are the evidence. ## Input Ask for whichever the user has: - The post analytics export (LinkedIn: Analytics -> Content -> Export). CSV. - Or a screenshot per post with impressions, reactions, comments, reposts. - Or just the posts and their reaction counts, which is enough for a first pass. Also read `~/.claude/linkedin/log.md` if it exists, since it records which hook formula each post used. ## What to actually measure Raw impressions are the least useful number on the page, because they are mostly a function of how many people already follow the user. Compute these instead, and show the working: | metric | how | what it tells you | | --- | --- | --- | | **Engagement rate** | (reactions + comments + reposts) / impressions | whether the post earned its reach | | **Comment ratio** | comments / reactions | whether it started something or just got a nod | | **Reach multiple** | impressions / follower count | whether it travelled past the existing audience | | **Save/send rate** | if available | the strongest single predictor of future reach | Rank by engagement rate and reach multiple, not impressions. A post with 900 impressions and 40 comments beat the one with 12,000 impressions and 6. ## Then find the pattern With the top 5 and bottom 5 side by side, look for what actually separates them, and be willing to conclude something the user will not like: - Hook formula. Which numbers from `hooks.json` are in the top 5? - Format. Text, document, image, video. - Length. - Theme. - Day and time - check this **last**, and only if the other four show nothing. It is almost never the cause, and it is where people want it to be. - First-hour comments. Posts the user replied to inside an hour versus not. State the finding as a claim with the evidence attached, and say how confident it is. With 30 posts you can see a pattern; with 6 you cannot, and you should say that instead of inventing one. ## Output ``` AUDIT · 31 posts · Jun 12 - Sep 5 TOP 5 BY ENGAGEMENT RATE 8.1% #3 Mistake "$18,000 is what no contract cost me" 1,940 imp 6.4% #20 Walk-Away "I fired my highest-paying client" 2,210 imp ... BOTTOM 5 0.4% #5 List "7 tools every founder needs" 11,400 imp ... WHAT THE DATA SAYS 1. Posts where you were the one who looked bad: mean 6.2% vs 1.1% for everything else. n=6. This is your strongest signal and it is not close. 2. Tool listicles get impressions and nothing else. High reach, no comments, no leads. Three of your bottom five. 3. Day of week shows nothing. Your Tuesday mean and your Friday mean are inside the noise. Stop optimising it. STOP: listicles about tools. DO MORE: the ones with a cost you paid, and a number. ``` Then hand the conclusions to `/li-plan` so next week's plan is built on the user's own evidence rather than on defaults.