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-comment-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-comment-to-outreach)<a href="https://agentmods.dev/skills/akiotanaka847/qaio-desktop/linkedin-comment-to-outreach"><img src="https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/linkedin-comment-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-comment-to-outreach"><img src="https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/linkedin-comment-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 | $0.00102 | $0.02071 |
| Opus 5 | $0.00051 | $0.01035 |
| Sonnet 5 | $0.00020 | $0.00414 |
| Haiku 4.5 | $0.00010 | $0.00207 |
Grade A, and why
linkedin-comment-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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Comment to Outreach
End-to-end orchestrator: LinkedIn post URL in, paused Instantly campaign out. I chain the five sub-skills with a checkpoint between each phase so you stay in control while the heavy lifting happens automatically.
Use this for commenters (higher intent, lower volume). For reactors (5-10x more leads, full LinkedIn profiles attached), use linkedin-reaction-to-outreach instead.
When to use
- "Run the LinkedIn pipeline on this post: ".
- "Scrape and email these commenters".
- "Outreach from this LinkedIn post".
- A speaker / competitor / thought-leader posted something that hits your ideal customer profile dead center, and you want to reach every qualified commenter in one motion.
When NOT to use
- Targeting people who reacted to a post - use
linkedin-reaction-to-outreach. Reactors are 5-10x more numerous and come with richer profile data. - Just need the commenter list, no outreach - use
linkedin-comment-scraperdirectly. - Just need to enrich an existing list - use
apollo-enrichmentdirectly. - Just need cold email copy without a lead source - use
cold-email-sequencedirectly. - 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 comment actor. 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.
If any of the four are missing I stop on the first missing one and ask you to connect it. The pipeline does not partially run.
Information I need
I read your outbound context first. For every required field that's missing I ask ONE plain-language question (best modality: connected app > URL > paste) and wait.
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 · 126 lines · 102 tokens per session scan A cf1adc18a85f
linkedin-comment-to-outreach is a skill published in the GitHub repository akiotanaka847/qaio-desktop (2 stars, last pushed 8d ago), licensed MIT. It adds 102 tokens to every session and 2,071 once invoked, about $0.0005 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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