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 outreach-icp-findergit 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/outreach-icp-finder)<a href="https://agentmods.dev/skills/lagrowthmachine/gtm-system/outreach-icp-finder"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/outreach-icp-finder/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/outreach-icp-finder"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/outreach-icp-finder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00234 | $0.06847 |
| Opus 5 | $0.00117 | $0.03424 |
| Sonnet 5 | $0.00047 | $0.01369 |
| Haiku 4.5 | $0.00023 | $0.00685 |
Grade A, and why
outreach-icp-finder 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 today.
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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outreach ICP Finder
Turns the outreach you already ran into a proven ideal customer profile — which job titles, seniorities, industries, company sizes and countries actually reply and show interest, which ones waste your touches — then helps find more of the good ones.
Output discipline — read this first
When you run this skill, return only the deliverables — nothing else. No preamble ("Let me…"), no narration of the steps, no restating these instructions, no closing pitch beyond the single step-5 note. Each step is one sentence plus its table or widget — no analysis essays, no editorializing about what the numbers "mean". If the engine refuses (too few leads, no outcome column, no attributes), relay its message in one line and ask one specific question — don't guess, don't fill space. Otherwise: output the five deliverables and stop.
Authority — read this first
Everything you need is inline in this file.
- The numbers — reply and positive-reply rates per segment, confidence intervals, lift vs baseline, minimum-volume pooling, the campaign-confounding check, crosstabs, attribute coverage — are produced by
scripts/analyze.py. Never compute these yourself. Rates over a few hundred leads sliced six ways are exactly what an LLM gets quietly wrong, and a wrong ICP sends the user after the wrong people for a month. Run the script; reason over its JSON. - The labeling — sorting reply texts into the five fixed labels — is your job when the data has reply text but no labels (see Labeling replies). You label; the script counts.
- The judgment — clustering the significant segments into 2–3 named archetypes, reading confounds, deciding what to flag — is your job, using the rules below.
references/title-taxonomy.jsonholds the job-title → seniority/function rules and the reply-label vocabulary. The script loads it; you don't need to read it unless a title family is systematically misclassified and you want to extend a rule.examples/sample-outreach.csvis a fictional 620-row dataset for a worked run.scripts/analyze.py --testis the self-test.
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.
- today First seen · 232 lines · 234 tokens per session scan A 0d2bdc7a1906
outreach-icp-finder is a skill published in the GitHub repository LaGrowthMachine/gtm-system (37 stars, last pushed yesterday), licensed MIT. It adds 234 tokens to every session and 6,847 once invoked, about $0.0012 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 2026-09-12.
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