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 WingedGuardian/GENesis-AGI --skill linkedin-dm-outreachgit clone --depth 1 https://github.com/WingedGuardian/GENesis-AGIWrote 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/wingedguardian/genesis-agi/linkedin-dm-outreach)<a href="https://agentmods.dev/skills/wingedguardian/genesis-agi/linkedin-dm-outreach"><img src="https://agentmods.dev/badge/skills/wingedguardian/genesis-agi/linkedin-dm-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/wingedguardian/genesis-agi/linkedin-dm-outreach"><img src="https://agentmods.dev/badge/skills/wingedguardian/genesis-agi/linkedin-dm-outreach.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 | $0.00087 | $0.01238 |
| Opus 5 | $0.00044 | $0.00619 |
| Sonnet 5 | $0.00017 | $0.00248 |
| Haiku 4.5 | $0.00009 | $0.00124 |
Grade A, and why
linkedin-dm-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 12d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn DM Outreach
Purpose
Write personalized LinkedIn messages — connection requests, InMails, and follow-up messages — that get responses instead of being ignored. Every message must demonstrate genuine relevance: why THIS person, why NOW, why the user is worth responding to.
Voice Loading
Load the user's voice via voice-master's overlay resolution before drafting:
- Read
../voice-master/SKILL.mdand follow its User Calibration Overlay section to load exemplars and voice-dimensions from the out-of-repo overlay (or template fallback with warning if no overlay). - This skill's medium is
professional. Select professional-medium exemplars from whatever voice-master loads. - Read
../voice-master/references/anti-slop.md— apply the Universal and Professional / LinkedIn sections.
If no overlay is present, voice-master falls back to generic voice guidance and warns — note this in your output.
DMs are the most personal LinkedIn format — sounding templated here is fatal. One bad outreach message can permanently close a door.
Message Types
Connection Request (300 characters max)
The hardest format. 300 characters to establish relevance and earn a click.
Rules:
- Name a specific reason for connecting (shared interest, their content, mutual connection, specific role/company)
- No generic "I'd love to add you to my network"
- No pitching in the connection request — ever
- If there's no genuine reason to connect, don't send the request
Structure: "[Specific reference to them or their work]. [Why connecting makes sense for both sides — in one sentence]."
First Message (after connecting)
Sent after a connection request is accepted, or to an existing connection.
Rules:
- Thank them for connecting (brief, not gushing)
- Reference something specific about them or their work
- State your reason for reaching out clearly
- Ask one specific question or make one specific offer
- Keep under 150 words — respect their time
- No "I hope this message finds you well"
- No walls of text about yourself
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.
- 12d ago First seen · 154 lines · 87 tokens per session scan A be1ac536d8a0
linkedin-dm-outreach is a skill published in the GitHub repository WingedGuardian/GENesis-AGI (96 stars, last pushed yesterday), licensed MIT. It adds 87 tokens to every session and 1,238 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 2026-08-30.
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