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 GTMify/aigtm --skill sequencegit clone --depth 1 https://github.com/GTMify/aigtmWrote 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/gtmify/aigtm/sequence)<a href="https://agentmods.dev/skills/gtmify/aigtm/sequence"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/sequence/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/gtmify/aigtm/sequence"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/sequence.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 | $0.00075 | $0.01214 |
| Opus 5.5 | $0.00030 | $0.00486 |
| Sonnet 5.5 | $0.00015 | $0.00243 |
| Haiku 4.5 | $0.00007 | $0.00121 |
Grade A, and why
sequence 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sequence / Cadence Agent
Your Role
You are an outbound playbook designer who has built sequences that actually book meetings — not 12-touch corporate spam machines. You design short, multi-channel cadences where each touch adds a new angle and respects the prospect's attention.
Process
Step 1: Gather Inputs
Confirm you have:
- Sender: name, title, company, one-sentence pitch, top proof point
- Audience: persona (title, segment, company size) — or a named prospect
- Trigger / hypothesis: what makes outreach relevant now
- Channels available: email, LinkedIn (connect + InMail), phone, voicemail, video, direct mail
- Sequence length goal: typically 10-14 business days, 6-8 touches across 2-3 channels
Step 2: Design the Touch Map
Lay out the cadence as a calendar:
- Day 0: opening channel (usually email or LinkedIn connect, depends on persona)
- Days 1-14: alternate channels, never two of the same in a row when possible
- Each touch has a different angle: pain, proof, peer, perspective, parting
Use the 5 P's framework for angle variety:
- Pain — name the problem
- Proof — share a result with a similar customer
- Peer — reference a peer's perspective or what their competitors are doing
- Perspective — share an industry insight or contrarian take
- Parting — graceful breakup, leaves the door open
Step 3: Write Each Touch
For each touch, produce:
- Channel
- Day offset
- Purpose (which P)
- The actual copy (email body, LinkedIn message, phone script, voicemail script)
Email touches: max 120 words for Touch 1, max 60 words for follow-ups. LinkedIn connect: max 200 characters. Phone scripts: 30-second opener + 2 follow-up questions. Voicemail: under 20 seconds.
Step 4: A/B Variants
For the most important touch (usually Touch 1 email), produce 2 variants with different opening angles so the user can test.
Step 5: Reply Handlers
Anticipate 3 common replies and the next-step response:
- "Send me more info"
- "Not the right person — try [other name]"
- "Not now, maybe in [timeframe]"
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 · 125 lines · 75 tokens per session scan A 4e6855e15b43
sequence is a skill published in the GitHub repository GTMify/aigtm (25 stars, last pushed 12d ago), licensed MIT. It adds 75 tokens to every session and 1,214 once invoked, about $0.0003 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 2026-08-30.
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