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 sequence-writergit 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/sequence-writer)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/sequence-writer"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/sequence-writer/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/sequence-writer"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/sequence-writer.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 | $0.00081 | $0.01122 |
| Opus 5 | $0.00041 | $0.00561 |
| Sonnet 5 | $0.00016 | $0.00224 |
| Haiku 4.5 | $0.00008 | $0.00112 |
Grade A, and why
sequence-writer 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write a complete cold outreach sequence grounded in a specific trigger event. Verify the contact first. Build every step around what actually happened at the account. Stop when there's nothing left worth saying.
Input
The user will provide via $ARGUMENTS:
- Trigger event: what happened at the account (e.g. "new VP of Sales joined", "Series B closed", "hiring surge in SDR roles", "intent signal on prospecting data")
- Contact: name and company, email, or LinkedIn URL
- What you sell: one sentence — product and the problem it solves
- Target outcome: what you want the sequence to achieve (e.g. "book a 20-minute discovery call")
- Tone: [optional] — direct / conversational / formal
- Steps: [optional] — number of steps, default is 5
If trigger event or contact is missing, ask once. If what you sell is missing, ask once. Never write the sequence before verifying the contact.
Workflow
-
CONTACT VERIFICATION Resolve and verify the contact via Lusha before writing anything:
- Current title and company confirmed
- Tenure in current role
- Verified email address
- Flag if contact has departed — do not write sequence until user confirms new contact
- Flag if title has changed since signal fired
-
ACCOUNT SIGNAL CONFIRMATION Confirm the trigger event via Lusha and check for additional signals at the account:
- Verify the primary trigger is current and accurate
- Surface any stacked signals — additional events at the same account in the last 90 days
- Note signal recency — older signals get less prominence in the sequence
-
SEQUENCE ARCHITECTURE Before writing, define the arc:
- Step 1: Lead with the trigger — specific, observed, no product pitch
- Step 2: Connect signal to a problem your product solves — relevance, not features
- Step 3: Social proof — a customer in a similar position who had the same problem
- Step 4: Stacked signal or urgency — why now matters, not just why you
- Step 5: Final close — direct ask, easy to say yes to
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 · 188 lines · 81 tokens per session scan A cead273196f8
sequence-writer is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 81 tokens to every session and 1,122 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 2026-09-03.
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