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 akiotanaka847/qaio-desktop --skill linkedin-reaction-to-outreachgit clone --depth 1 https://github.com/akiotanaka847/qaio-desktopWrote 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/akiotanaka847/qaio-desktop/linkedin-reaction-to-outreach)<a href="https://agentmods.dev/skills/akiotanaka847/qaio-desktop/linkedin-reaction-to-outreach"><img src="https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/linkedin-reaction-to-outreach/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/akiotanaka847/qaio-desktop/linkedin-reaction-to-outreach"><img src="https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/linkedin-reaction-to-outreach.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 | $0.00089 | $0.01980 |
| Opus 5 | $0.00044 | $0.00990 |
| Sonnet 5 | $0.00018 | $0.00396 |
| Haiku 4.5 | $0.00009 | $0.00198 |
Grade A, and why
linkedin-reaction-to-outreach 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.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Reaction to Outreach
End-to-end orchestrator: LinkedIn post URL in, paused Instantly campaign out. Same five-phase chain as linkedin-comment-to-outreach, but I scrape reactors instead of commenters.
Why reactors? Two reasons:
- Volume - reactors typically outnumber commenters 5-10x. A post with 30 commenters often has 200-500 reactors.
- Richer profiles - the reaction scrape returns full LinkedIn profiles per person (experience history, education, skills, certifications, location, connections count) directly in one Apify call. The commenter scrape only returns surface fields. This makes the personalization ceiling much higher.
Trade-off: reacting is a lower-effort signal than commenting. You're trading per-lead intent for volume + data depth.
When to use
- "Run the LinkedIn reaction pipeline on this post: ".
- "Scrape and email everyone who reacted to this post".
- A post is hitting your ideal customer profile broadly and you want maximum coverage.
- You want full LinkedIn profile data attached to each lead (for personalization in the email body, not just the subject).
- Niche-audience outreach: "CPAs who reacted to a tax planning post", "founders who reacted to a fundraising thread".
When NOT to use
- You only want commenters (higher per-lead intent) - use
linkedin-comment-to-outreach. - Just need the reactor list, no outreach - use
linkedin-reaction-scraperdirectly. - Just need to enrich an existing list - use
apollo-enrichmentdirectly. - Already have a verified list and copy ready - use
instantly-campaigndirectly.
Connections I need
I run external work through Composio. Before this skill runs I check that every category below is linked. Missing → I name the category, ask you to connect it from the Integrations tab, stop.
- Apify (scraping) - for the LinkedIn reaction actor (with
profileScraperMode: "main"). Required. - Airtable (database) - for the lead-tracking table. Required.
- Apollo (enrichment) - for verified emails + company / title / location. Required.
- Instantly (sending platform) - for campaign creation and lead loading. Required.
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 · 129 lines · 89 tokens per session scan A 2a74e4d5fd90
linkedin-reaction-to-outreach is a skill published in the GitHub repository akiotanaka847/qaio-desktop (2 stars, last pushed 8d ago), licensed MIT. It adds 89 tokens to every session and 1,980 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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