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 media-pr-outreachgit 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/media-pr-outreach)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/media-pr-outreach"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/media-pr-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/swan-gtm/gtm-skills/media-pr-outreach"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/media-pr-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 | $0.00109 | $0.01239 |
| Opus 5.5 | $0.00044 | $0.00496 |
| Sonnet 5.5 | $0.00022 | $0.00248 |
| Haiku 4.5 | $0.00011 | $0.00124 |
Grade A, and why
media-pr-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 1mo 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Applies to any media or PR outreach to a journalist for coverage. Produces a short pitch anchored to the reporter's recent work, weighted toward their readers, and closed on a specific give and one clear ask.
Most cold pitches to reporters fail the same three ways: they lead with the company, they are not anchored to anything the reporter actually wrote, and they open with "happy to be a source" and give no reason to reply. This skill fixes all three, then routes the draft through a review gate before it sends.
The four pitch types
Pick the type before drafting - each has a different structure and length, detailed in references/pitch-types.md.
- First contact - get on the reporter's radar before there is news. No pitch, no ask beyond "useful to be a resource".
- News pitch - a specific announcement worth their readers' attention.
- Byline pitch - a contributed article from an exec, backed by a real existing point of view.
- Reactive pitch - a story just broke on the beat and there is a fast, specific expert take to offer. Speed is the whole point.
The play
- Qualify the reporter against a recent, on-beat byline. Find a specific piece they published recently that the company can genuinely comment on, and confirm it is actually recent at the source - a contact list goes stale, and a byline from last quarter is not an anchor. Record the anchor's title, date, and link. No live anchor means the reporter is not ready for first contact or a pitch yet. (A reactive pitch is the exception: its anchor is the story that just broke, so speed replaces the byline check.)
- Pick the type and draft to its structure. Use the matching structure from
references/pitch-types.md. Keep it short - first contact under five sentences, a news pitch under 150 words, a reactive pitch under 100. - Lead with their readers, not the company. Open on the news value or the beat. One line of company relevance, not three. The anchor rule, the them:us ratio, and the give-plus-ask close - the three levers that decide reply rate - are worked through with examples in
references/anchor-ratio-and-close.md. - Run the copy pass. Strip AI tells, then add enough plain human voice that it reads as one person emailing another, not a template. No jargon, no "circling back", no press-release voice.
- Clear the review gate. Before it sends, one independent read confirms: anchored (to the reporter's recent byline, or for a reactive pitch to the breaking story), passes the them:us ratio, closes on a real give-plus-ask (first contact excepted - it makes no ask), and reads clean. One revision round.
- Human approves, then send, log, and follow up once. A person reviews and approves the final email and sends it from the right inbox - nothing goes out automatically. Log the reporter, outlet, date, type, and anchor. Follow up once after five to seven business days, one sentence, no pressure. Log every response, including a decline - a "not now" is a relationship.
What ships with it
2 files 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.
- 1mo ago First seen · 46 lines · 109 tokens per session scan A 58c40a0e802c
media-pr-outreach is a skill published in the GitHub repository swan-gtm/gtm-skills (221 stars, last pushed 7d ago), licensed MIT. It adds 109 tokens to every session and 1,239 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-03.
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