Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add styfinity/linkedin-engine --skill linkedin-post-autopsygit clone --depth 1 https://github.com/styfinity/linkedin-engineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/styfinity/linkedin-engine/linkedin-post-autopsy)<a href="https://agentmods.dev/skills/styfinity/linkedin-engine/linkedin-post-autopsy"><img src="https://agentmods.dev/badge/skills/styfinity/linkedin-engine/linkedin-post-autopsy.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00524 |
| Opus 5 | $0.00022 | $0.00262 |
| Sonnet 5 | $0.00009 | $0.00105 |
| Haiku 4.5 | $0.00004 | $0.00052 |
Grade A, and why
linkedin-post-autopsy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
LinkedIn Post Autopsy
Every post is data. This skill reads the post against the moves that drive distribution, reads the engagement ratios, and tells you the one thing to change next time.
Inputs
- The post text and its stats (impressions, comments, reactions, saves, profile views): $ARGUMENTS
- The brief (persona, pillars, offer, pains) loads automatically.
Do this
- Score the post against the seven moves, one line each, present or missing:
- Value-lever hook (first line earns the second)
- Status reframe (shifts how the reader sees themselves or the problem)
- System, not tips (a repeatable mechanism, not a listicle)
- Quantified before/after (real numbers, attributed to a post / a client / an operator)
- Contrarian line (one claim the feed disagrees with)
- Comment-keyword CTA (asks for a specific word, not "thoughts?")
- Saveable asset (something worth keeping: a checklist, a teardown, a swipe)
- Read the ratios. Comments are the heaviest-weighted signal. An inverted ratio (more comments than reactions) is the viral fingerprint. Note reactions-to-comments, and saves and profile views as intent signals.
- Tie the ratio read back to the moves. Which present move drove the signal, which missing move capped it.
- Name the single weakest move: the one that, fixed, would have moved this post most.
Output
Return labelled: a move-by-move scorecard (seven rows, present/missing + one-line read), the ratio read (what the comments-to-reactions ratio says about distribution), and THE ONE CHANGE that would have moved it most. Hand the rewrite to /linkedin-humanizer if you redraft the hook.
Rules
- End on one fix, not a list of five. If you can't reduce to one, you haven't finished reading the stats.
- Ground every claim in the numbers given. No verdict the stats don't support.
- Attribute any quoted number to a post / a client / an operator, never a named person or company.
- No em-dashes.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 36 lines · 44 tokens per session scan A fe92645086aa
linkedin-post-autopsy is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 524 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
LinkedIn Automation
Automate LinkedIn marketing, lead generation, content publishing, and professional networking.
reddit-leads
Discover B2B leads from Reddit using AI-powered lead scoring via reddapi.dev Leads API. Finds high-intent signals, scores them 0-100, and classifies by lead type (painpoint, solutionrequest, complaint, featurerequest, comparison). Perfect for competitor poaching, pain point discovery, and sales prospecting.
deepline-ads-audiences
Use this skill when building, enriching, auditing, or uploading B2B paid ads audiences to Google Customer Match, Meta/Facebook Custom Audiences, or LinkedIn Matched Audiences. Triggers on phrases like '/deepline-ads-audience', '/deepline-ads-audiences', 'upload this audience', 'create custom audiences', 'personal…
deepline-gtm
GTM prospecting, enrichment, outreach, and Deepline Play work/audits. Providers…
linkedin-carousels
Use when building a LinkedIn carousel / document post — the swipeable multi-page PDF — slide by slide: cover hook, narrative arc, one idea per slide, CTA closer, PDF export spec. NOT the account/format plan (that is linkedin-strategy), NOT a text or single-image feed post (that is linkedin-content), NOT a 16:9 talk…
b2b-marketing-playbook
Complete B2B marketing pipeline combining LinkedIn content, cold email sequences, and webinar funnels. Designed for SaaS founders doing $0–$1M ARR who need predictable lead generation. By @WeiYipei.