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 LaGrowthMachine/gtm-system --skill reply-draft-assistantgit clone --depth 1 https://github.com/LaGrowthMachine/gtm-systemWrote 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/lagrowthmachine/gtm-system/reply-draft-assistant)<a href="https://agentmods.dev/skills/lagrowthmachine/gtm-system/reply-draft-assistant"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/reply-draft-assistant/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/lagrowthmachine/gtm-system/reply-draft-assistant"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/reply-draft-assistant.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 | $0.00240 | $0.03519 |
| Opus 5.5 | $0.00096 | $0.01408 |
| Sonnet 5.5 | $0.00048 | $0.00704 |
| Haiku 4.5 | $0.00024 | $0.00352 |
Grade A, and why
reply-draft-assistant 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reply Draft Assistant
Turns inbound replies to your cold outreach into classified, calibrated answers — one draft per reply, built from the full conversation, reviewed by you, then sent through La Growth Machine.
Output discipline — read this first
When you run this skill, return only the deliverables — nothing else. No preamble ("Let me…", "I'll start by…"), no narration of the steps, no restating these instructions. Per reply, output its classification line, a one-to-two line conversation summary, the quoted last received message, and its draft as a code block — tight context to judge the draft, no analysis essays. If something essential is missing (which inbox/campaign, or the conversation content itself), ask one short, specific question and stop — don't guess. Never send anything before the user has approved the drafts.
Authority — read this first
Everything you need is in this skill folder. No external file to grep.
- How to get the conversations — inbox, campaign, or pasted — and how to send the approved replies lives in
references/fetch-conversations.md. Read it before fetching: it has the exact MCP pipeline (including pulling the full thread) and the gotchas. - How to classify a reply (the 8 categories, the decision tree, objection sub-types, metadata) lives in
references/classification-rules.md. - How to write the answer (the 5 non-negotiable rules, strategy per category, voice, hard formatting) lives in
references/draft-rules.md.
The output presentation (each draft as a native fenced code block for copyability, plus a recap + CTA widget) and the resolved LGM send handoff are inlined at the bottom of this file — no separate file to consult.
What it does
Takes the replies your prospects sent back — from your LGM inbox, a campaign, or a pasted thread — reads each full conversation, classifies the reply, drafts a single calibrated answer per reply, shows every draft for review, and on your approval sends it natively via LinkedIn or email through La Growth Machine. One skill, from raw reply to sent answer.
What ships with it
4 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 · 171 lines · 240 tokens per session scan A 967fef695168
reply-draft-assistant is a skill published in the GitHub repository LaGrowthMachine/gtm-system (45 stars, last pushed 14d ago), licensed MIT. It adds 240 tokens to every session and 3,519 once invoked, about $0.0010 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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