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 campaign-challengergit 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/campaign-challenger)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/campaign-challenger"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/campaign-challenger/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/campaign-challenger"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/campaign-challenger.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 | $0.00129 | $0.01225 |
| Opus 5 | $0.00064 | $0.00613 |
| Sonnet 5 | $0.00026 | $0.00245 |
| Haiku 4.5 | $0.00013 | $0.00122 |
Grade A, and why
campaign-challenger 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 9d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Applies to a sequence that's written and not yet launched. Produces a comparison against the team's own campaign history, an absolute score, and a prioritised fix list.
Judge against their history, not against best practice
Generic copywriting advice is the weakest available benchmark. It averages across every offer, market and level of competence, so it can tell you a draft is catastrophically broken and nothing finer than that.
The campaigns that team has already run are a far better instrument. They hold the offer constant, the market constant, and the sender's own credibility constant — so a difference between the draft and a past winner is a difference that means something. Run both reads and report both: comparative against their history, absolute against the quality bar. They fail differently, and a draft that passes one and fails the other is telling you something specific.
Where there's no history at all, don't error out. Fall back to the absolute bar, and say plainly that's what you're doing.
Rank on meetings, not on replies
Reply rate is the number everyone has and the number that misleads most. A campaign can triple its replies by asking a question anyone can answer and book nothing.
Rank the existing campaigns on meetings booked first, reply rate second. When the two disagree — and they often do — that disagreement is usually the most useful finding available, because the campaign with the best reply rate and no meetings is generating conversations with the wrong people or at the wrong moment.
The copy is the explanation
Stats alone tell you which campaign won. Only the copy tells you why, and why is the entire deliverable — a benchmark that can't be acted on is a scoreboard.
So gather both. When someone offers past performance without the messages, ask for the messages; without them the comparison degrades into "your draft is longer than your best campaign", which is an observation, not a fix.
Compare on things you can point at: sequence structure and length, how each opener works, the CTA shape per touch, angle variety across the sequence, and cadence.
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.
- 9d ago First seen · 80 lines · 129 tokens per session scan A 1e9e796252c4
campaign-challenger is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 129 tokens to every session and 1,225 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 2026-09-03.
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