linkedin-comment-scraper

linkedin-comment-scraper is a skill for Claude Code, Codex from akiotanaka847/qaio-desktop. It costs 76 tokens per session (1,301 once invoked), scanned A, original, MIT.

A tool that collects the people who commented on a LinkedIn post and saves their details in a cleaned list. It removes duplicate profiles and can include names, job headlines, profile links, comment text, and reaction counts.

In plain words
What is it for?
It helps build a list of LinkedIn commenters from a post when you need the list itself rather than a complete email campaign.
Why use it?
It avoids manually opening a post and copying each commenter into a separate list. The result can be used for outreach or other follow-up work.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit It helps build a list of LinkedIn commenters from a post when you need the list itself rather than a complete email campaign.

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Install with agentmods
npx agentmods add skills/akiotanaka847/qaio-desktop/linkedin-comment-scraper
Install

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.

Any agent
npx skills add akiotanaka847/qaio-desktop --skill linkedin-comment-scraper
Clone the repo
git clone --depth 1 https://github.com/akiotanaka847/qaio-desktop

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for linkedin-comment-scraper

README.md
[![agentmods](https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/linkedin-comment-scraper/github.svg)](https://agentmods.dev/skills/akiotanaka847/qaio-desktop/linkedin-comment-scraper)
Your own site
<a href="https://agentmods.dev/skills/akiotanaka847/qaio-desktop/linkedin-comment-scraper"><img src="https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/linkedin-comment-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.

agentmods 80×15 button for linkedin-comment-scraper

Your own site · 80×15
<a href="https://agentmods.dev/skills/akiotanaka847/qaio-desktop/linkedin-comment-scraper"><img src="https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/linkedin-comment-scraper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,301 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00076 $0.01301
Opus 5 $0.00038 $0.00651
Sonnet 5 $0.00015 $0.00260
Haiku 4.5 $0.00008 $0.00130

Measured 6d ago against content hash 5a10332ca054, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

linkedin-comment-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 6d 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.

store/agents/outbound/.agents/skills/linkedin-comment-scraper/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn Comment Scraper

Pull every commenter from a LinkedIn post into a clean, deduped list. Phase 1 of the comment-to-outreach pipeline, but you can run it standalone if you only need the list (e.g. as input to a different downstream tool).

When to use

  • "Scrape commenters from this LinkedIn post: ".
  • "Pull a list of who commented on this post".
  • You want a clean, deduped commenter list for any downstream use - not necessarily cold outreach.

When NOT to use

  • You want to react to a post (not comment) - use linkedin-reaction-scraper.
  • You want the full end-to-end pipeline through to Instantly - use linkedin-comment-to-outreach.

Connections I need

  • Apify (scraping) - Required. I use the harvestapi/linkedin-post-comments actor.

If Apify isn't connected I stop and ask you to connect it from the Integrations tab.

Information I need

  • The LinkedIn post URL - Required. If missing I ask: "Which LinkedIn post should I scrape?"
  • A target item count - Optional. Defaults to defaultMaxItems from your outbound context (500). Override per call if you only want a quick test pull.

Steps

  1. Validate URL. Confirm the URL is a LinkedIn post (linkedin.com/posts/... or linkedin.com/feed/update/...). Reject profile URLs, article URLs, company URLs. If the input is a short link or a redirect, follow it once to resolve the canonical post URL before scraping.

  2. Test pull. First call to the actor with maxItems: 20 to confirm the post is reachable and the actor returns the expected shape. If the test pull returns 0 items, stop and surface why (post deleted, comments disabled, geo-blocked, actor cold-start).

  3. Full pull. Call the actor with maxItems: {target} (default 500). Wait for the run to finish. Apify typically takes 2-5 minutes for the full pull.

  4. Dedupe. Group raw items by profileUrl. For duplicates within one scrape (same person commented multiple times), keep the row with the longest comment text. Drop rows where profileUrl is null or where fullName is null - these are scrape misses, not real leads.

Read the full file on GitHub · 87 lines

Changes

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.

  1. 6d ago First seen · 87 lines · 76 tokens per session scan A 5a10332ca054

Subscribe to this mod's changes

linkedin-comment-scraper is a skill published in the GitHub repository akiotanaka847/qaio-desktop (2 stars, last pushed 8d ago), licensed MIT. It adds 76 tokens to every session and 1,301 once invoked, about $0.0004 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 2026-09-05.

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