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 swan-gtm/gtm-skills --skill linkedin-dm-signal-classifiergit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/swan-gtm/gtm-skills/linkedin-dm-signal-classifier)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-dm-signal-classifier"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-dm-signal-classifier/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/swan-gtm/gtm-skills/linkedin-dm-signal-classifier"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-dm-signal-classifier.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 | $0.00118 | $0.05977 |
| Opus 5 | $0.00059 | $0.02988 |
| Sonnet 5 | $0.00024 | $0.01195 |
| Haiku 4.5 | $0.00012 | $0.00598 |
Grade A, and why
linkedin-dm-signal-classifier 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 13d 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 — 612 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Template placeholders
Replace every {{...}} before enabling. See the setup checklist reference for the full setup list.
{{TRIGGER_OWNER}}— Team member whose LinkedIn DMs are scanned (one skill instance per person){{CRM}}— Your CRM (e.g. HubSpot, Attio){{SIGNALS_CHANNEL}}— Slack channel for all DM signal posts + daily digest{{EXPANSION_CHANNEL}}— Slack channel for expansion/churn-risk cross-posts (optional){{MQL_CHANNEL}}— Slack channel where your lead-scoring alerts post qualified accounts{{PARTNERSHIPS_OWNER}}— Person to flag partner/agency signals to{{FUNDRAISING_OWNER}}— Person to flag investor inquiries to (usually a founder){{LEAD_SCORING_SKILL}}— Sub-skill reference: your lead scoring & qualification methodology{{OUTREACH_SKILL}}— Sub-skill reference: your outreach/sequence-building methodology{{HIGH_ACV_THRESHOLD}}— Company profile that suggests medium-to-high ACV (default: >30 employees, funded, or VP+/C-suite/Founder contact){{SELF_SERVE_THRESHOLD}}— Company profile that should route to your self-serve / low-touch motion instead of being scored (default: <50 employees AND ≤$10M total funding)
⚠️ Optional EVAL mode (recommended for the first 1–2 weeks)
While you evaluate output quality, you can run with {{CRM}} writes disabled: skip every "create or update in {{CRM}}" step below and instead include the exact note text that WOULD have been written in the Slack post. Workspace account-memory writes stay ON either way. Once you trust the output, follow the steps as written — that is the live configuration.
Overview
Daily workflow for processing LinkedIn DM conversations. Runs once per person per day via a personal scheduled trigger. The trigger owner's LinkedIn account is used automatically.
Step 1 — Determine the Cursor
Check the trigger owner's user memory for a key like
LI DM last run: [ISO timestamp].
- If found: this is a subsequent run. You will skip chats with no activity since that timestamp.
- If not found: this is the first run. Process chats up to the limit specified in the trigger instructions.
What ships with it
1 file 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.
- 13d ago First seen · 612 lines · 118 tokens per session scan A 968e98bc0832
linkedin-dm-signal-classifier is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 118 tokens to every session and 5,977 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.
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