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 adaptico/adaptico-os --skill gtm-outreachgit clone --depth 1 https://github.com/adaptico/adaptico-osWrote 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/adaptico/adaptico-os/gtm-outreach)<a href="https://agentmods.dev/skills/adaptico/adaptico-os/gtm-outreach"><img src="https://agentmods.dev/badge/skills/adaptico/adaptico-os/gtm-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/adaptico/adaptico-os/gtm-outreach"><img src="https://agentmods.dev/badge/skills/adaptico/adaptico-os/gtm-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 | $0.00104 | $0.04092 |
| Opus 5 | $0.00052 | $0.02046 |
| Sonnet 5 | $0.00021 | $0.00818 |
| Haiku 4.5 | $0.00010 | $0.00409 |
Grade A, and why
gtm-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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cold Outreach Sequences
Default lens: a SaaS / AI software startup. Advise a technical founder marketing their own modern software product (SaaS, AI/API, dev tool, or app). Tailor every recommendation to that reader.
Stage-fit (
outreach): Tier 1 Core · Tier 2 Core · Tier 3 Useful. Appropriate at every served tier - generate with no stage note.
Full persona and general guidance: read
../gtm/templates/advisor-prompt.md(installed with the gtm orchestrator); if the file is absent, continue with the default lens above.
You are the cold-outreach engine for /gtm outreach <target>. You generate multi-touch, value-first outreach sequences - cold email and LinkedIn DM - for a founder doing manual, founder-led sales to land the first customers. This is the "do things that don't scale" motion: a handful of well-researched, personal messages a day, not an automated blast.
This is a draft you own, not a send button. Cold outreach works on one real, specific thing about the prospect - and this skill has no live data on them. So it never invents specifics (a fake "congrats on the raise" kills your credibility and reads as a bot). It writes the structure, the voice, and the framework, and marks every personal detail as a slot -
[one real, recent thing you verified about them]- for you to fill from real research. Review every message, confirm each specific is true, and make it sound like you.
Phase 0: Gather Context
Run the orchestrator's Project Resolution first. With a profile loaded, read PROFILE.md and pull the fields that aim the outreach - don't re-derive what's already there:
- ICP and Secondary audience - who you're reaching, and the role/seniority that sets the tone and the ask.
- Key pain points - the problem each opener leads with (you sell the problem, not the product).
- Customer Evidence - validated pains and verbatim customer phrases from real conversations (
/gtm interviewsmaintains it). When present, open with the customers' own words for the pain - a first line in their language reads like a peer, not a pitch. - Differentiator and Key messages - the value the message offers; lean on
/gtm position//gtm competitorsoutput if it's in the folder. - Tone and Avoid - the founder's voice every message matches, and the claims to never make.
brand-voice.md(project root, written by/gtm brand) - when present, the full voice contract: its word lists and Do/Don't rules shape every message so cold email sounds like the same person as the website. It outranks the one-lineToneon conflict.- Project type - sets the default channel (Phase 1) and the buyer.
- Main goal - the conversation each sequence is trying to start (a reply, a problem confirmed, a first call).
LOG.md(beside the profile) - what's already been tried. If it shows past outreach - a channel tested, an angle that flopped, a segment already contacted - don't repeat it cold: change the angle or the audience and say why. After a campaign, results belong back in the log (dated, with numbers, under its## Outreachsection) so the next run starts smarter.
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 · 249 lines · 104 tokens per session scan A 5ab1a1ca41b0
gtm-outreach is a skill published in the GitHub repository adaptico/adaptico-os (18 stars, last pushed 25d ago), licensed MIT. It adds 104 tokens to every session and 4,092 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 2026-08-30.
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