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 matteotitta/genesys-skills --skill linkedin-content-auditgit clone --depth 1 https://github.com/matteotitta/genesys-skillsWrote 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/matteotitta/genesys-skills/linkedin-content-audit)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-content-audit"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-content-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-content-audit"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-content-audit.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00025 | $0.01769 |
| Opus 5 | $0.00013 | $0.00885 |
| Sonnet 5 | $0.00005 | $0.00354 |
| Haiku 4.5 | $0.00003 | $0.00177 |
Grade A, and why
linkedin-content-audit 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Content Audit
Audit LinkedIn creator profiles by scraping posts (Apify) and classifying them across 6 dimensions: hook patterns, post types, media types, media content, CTAs, engagement. Diagnostic only — does not write posts or check algo compliance. Over-fetches ~100 posts/profile (6-month window), deep-analyzes the top 25 by engagement.
When to run
Run when: auditing a creator's LinkedIn, benchmarking 2+ profiles, extracting hook/CTA patterns, or sourcing competitive intel for content strategy. Skip when writing posts (linkedin-content), checking a single post's algo fit (linkedin-algo-audit), building an ICP guide (linkedin-content-guide), or running a multi-channel audit (content-audit). Full trigger/anti-trigger list → the premium reference.
Inputs
Required: 1-10 LinkedIn profile URLs (linkedin.com/in/... format).
Optional (defaults): scrape depth 100, time window 6months (alt: month/3months/year), top-N for analysis 25, include quote posts true, focus areas all 6, client context none. Validate URLs before scraping; confirm parameters with user. Full input table → the premium reference.
Steps
- Validate inputs — All URLs are
linkedin.com/in/...; 1-10 profiles; confirm depth + window with user. - Phase 1.1 — Apify scrape (single batch). Call
mcp__apify__call-actorwith actorharvestapi/linkedin-profile-posts, input:targetUrls: [all],maxPosts: 100,postedLimit: "6months",includeReposts: false,includeQuotePosts: true,scrapeReactions: false,scrapeComments: false. One run = one start fee + per-post cost (cheaper than N runs). Reposts excluded so patterns reflect creator's own voice. - Phase 1.2 — Retrieve + validate. Use
mcp__apify__get-actor-outputwith returneddatasetId. Confirm 50-100 posts/profile, text + engagement + timestamps present. Flag profiles with <10 posts as "insufficient data." Fallback if Apify fails: ask user for manual posts (copy-paste, Shield/Taplio/AuthoredUp CSV, or screenshots). - Phase 2.1 — Group by author (URL or name).
- Phase 2.2-2.4 — Engagement filter. Compute
total_engagement = likes + comments + sharesper post. Sort each profile descending. Take top 25 for deep analysis. Retain full dataset for volume/cadence metrics (total posts, posts/week, consistency stdev). - Phase 3.1 — Hook classification. Read first line / first sentence. Map to 14-category taxonomy in the premium reference. Output: count, %, avg engagement per hook type.
- Phase 3.2 — Post type (pillars). Map to Educational / Personal / Promotional / Organizational / Engagement. Compare mix to 40/25/25/10 target. Definitions → the premium reference.
- Phase 3.3 — Media type. Classify from Apify attachment data: text-only, carousel/document, single image, multi-image, video, poll, article/newsletter, external link. Output: format mix + avg engagement per format.
- Phase 3.4 — Media content. For posts with visuals, classify what media depicts using post-text context only (screenshots, charts, selfies, memes, infographics, text-on-image, BTS, professional photo, AI-generated, undetermined). NEVER guess from URL.
- Phase 3.5 — CTA classification. Analyze final 1-3 lines. Bucket into 8 types: comment prompt, DM invite, link/resource, follow/connect, save, repost, no CTA, multiple CTAs. Output: distribution + avg engagement per type. Pattern examples → the premium reference.
- Phase 3.6 — Engagement analysis. Per-post likes/comments/shares; aggregated avg/median/max; top 5 with excerpts; cross-tabs (engagement × hook, × media, × pillar, × CTA); volume metrics from full dataset.
- Phase 3 checkpoint — All 6 dimensions classified; percentages sum to 100% within each category; cross-tabs computed; volume metrics from full dataset.
- Phase 4 — Cross-profile comparison (only if 2+ profiles). Build profile overview matrix; format mix table; hook style heat map; engagement benchmarks; CTA distribution; top patterns to emulate (evidence-cited); anti-patterns to avoid (evidence-cited). Detail → the premium reference.
- Self-evaluation. Completeness (all phases + profiles + comparison if applicable); accuracy (3 random hook spot-checks, engagement avg sanity, format consistency vs attachments); honesty (zero invented numbers, no guessed media content, gaps marked). Full protocol → the premium reference.
- Write outputs. Per-profile:
linkedin-audit-{username}.md. Cross-profile:linkedin-audit-comparison.md. Templates → the premium reference.
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.
- 9d ago First seen · 91 lines · 150 tokens per session scan A b4b29939f30a
linkedin-content-audit is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 1,769 once invoked, about $0.0001 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 2026-09-03.
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