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 Casper-Studios/casper-marketplace --skill linkedin-engagement-scrapergit clone --depth 1 https://github.com/Casper-Studios/casper-marketplaceWrote 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/casper-studios/casper-marketplace/linkedin-engagement-scraper)<a href="https://agentmods.dev/skills/casper-studios/casper-marketplace/linkedin-engagement-scraper"><img src="https://agentmods.dev/badge/skills/casper-studios/casper-marketplace/linkedin-engagement-scraper/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/casper-studios/casper-marketplace/linkedin-engagement-scraper"><img src="https://agentmods.dev/badge/skills/casper-studios/casper-marketplace/linkedin-engagement-scraper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00124 | $0.01382 |
| Opus 5 | $0.00062 | $0.00691 |
| Sonnet 5 | $0.00025 | $0.00276 |
| Haiku 4.5 | $0.00012 | $0.00138 |
Grade A, and why
linkedin-engagement-scraper 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 11d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Engagement Scraper
Scrape everyone who engaged with a LinkedIn post and produce two Lemlist-ready outputs:
- All engagers — everyone who commented, ready for Lemlist import
- ICP matches — only the people who match your Ideal Customer Profile
Pipeline
LinkedIn post URL
↓
scrape_engagers.py (PhantomBuster API)
↓
┌───┴───┐
│ │
▼ ▼
All CSV engagers.json
(Lemlist) │
▼
filter_icp.py (define ICP → score → filter)
│
▼
ICP Matches CSV
(Lemlist)
Prerequisites
| Requirement | Details |
|---|---|
PHANTOMBUSTER_API_KEY |
Settings → API |
PHANTOMBUSTER_AGENT_ID |
From phantom URL: phantombuster.com/phantoms/<ID>/... |
| Python packages | pip install requests python-dotenv |
Set the env vars in a .env file in the working directory.
See references/phantombuster-setup.md for full PhantomBuster setup instructions.
Step 1: Scrape Engagers
python <skill-path>/scripts/scrape_engagers.py "<linkedin_post_url>"
This launches PhantomBuster, waits for results, and outputs:
| File | Purpose |
|---|---|
output/all_engagers_YYYY-MM-DD_HHMMSS.csv |
All contacts, Lemlist-ready. Import directly. |
output/engagers.json |
Full contact data with seniority tagging. Feed to Step 2. |
Step 2: Filter by ICP (Optional)
python <skill-path>/scripts/filter_icp.py output/engagers.json
The script prompts you to define your ICP interactively:
--- Define your Ideal Customer Profile ---
Segment 1:
Industries (comma-separated, or blank for any): saas, fintech
Target titles/keywords (comma-separated, or blank for any): vp, head of, director
Minimum seniority [c_suite / vp / director / manager / senior_ic / any]: director
Company name keywords (comma-separated, or blank for any):
Add another segment? (y/n): n
Or reuse a saved ICP:
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 161 lines · 124 tokens per session scan A 7aae6024f688
linkedin-engagement-scraper is a skill published in the GitHub repository Casper-Studios/casper-marketplace (12 stars, last pushed 6d ago), licensed MPL-2.0. It adds 124 tokens to every session and 1,382 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…