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 AlexisMarasigan/coldoutboundskills --skill auto-research-publicgit clone --depth 1 https://github.com/AlexisMarasigan/coldoutboundskillsWrote 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/alexismarasigan/coldoutboundskills/auto-research-public)<a href="https://agentmods.dev/skills/alexismarasigan/coldoutboundskills/auto-research-public"><img src="https://agentmods.dev/badge/skills/alexismarasigan/coldoutboundskills/auto-research-public/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/alexismarasigan/coldoutboundskills/auto-research-public"><img src="https://agentmods.dev/badge/skills/alexismarasigan/coldoutboundskills/auto-research-public.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 | $0.00129 | $0.03037 |
| Opus 5 | $0.00064 | $0.01519 |
| Sonnet 5 | $0.00026 | $0.00607 |
| Haiku 4.5 | $0.00013 | $0.00304 |
Grade A, and why
auto-research-public 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 11d 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.
This is a copy
100% identical to auto-research-public — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Research (Public)
Automated end-to-end campaign launcher. Feed it one target company domain, get back a live Smartlead campaign with per-lead personalization — in about 20 minutes.
This is the beginner-friendly version of the GEX internal auto-research-v2. All state lives in local JSON files; no Supabase, no Trigger.dev.
What you get
- Input: one target company domain + your
client-profile.yaml - Output: a running Smartlead campaign with:
- 200-1,000 leads (depending on targeting tightness)
- Per-lead personalization: 9 custom variables (situation, value, CTA × 3 variants)
- A/B/C subject + body variants tested in parallel
- Campaign assigned to your available inboxes
- Schedule: Mon-Fri 8am-5pm your timezone
Prerequisites
Before running:
-
client-profile.yamlexists (run/icp-onboardingif not) -
SMARTLEAD_API_KEYin env -
PROSPEO_API_KEYin env -
MILLIONVERIFIER_API_KEYin env (for email validation) - At least 20 Smartlead inboxes tagged "active" (run
/smartlead-inbox-managerfirst) - At least 1 campaign template in Smartlead (or the script creates a fresh one)
The orchestration (Claude Code runs this)
Unlike the other skills, this skill orchestrates through the Claude Code conversation itself — Claude does the reasoning (ICP generation, copy writing, personalization), and phase scripts do the heavy API I/O. This is the pattern from the GEX v2 internal.
Phase 1: Scrape the target company
npx tsx scripts/phase-scrape.ts --domain=<target.com> --out=/tmp/auto/scrape.json
Output: JSON with domain + text content from homepage, /about, /product, /pricing, /customers.
Claude reads the output and writes a short analysis to /tmp/auto/company-analysis.md:
- What the company does
- Who their likely customers are
- Social proof signals
- Potential angles for outreach
Phase 2: Claude generates ICP filters
Reading /tmp/auto/scrape.json + ~/cold-email-ai-skills/profiles/<slug>/client-profile.yaml, Claude writes Prospeo filters to /tmp/auto/filters.json:
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.
- 11d ago First seen · 288 lines · 129 tokens per session scan A 548ec7410a01
auto-research-public is a skill published in the GitHub repository AlexisMarasigan/coldoutboundskills (4 stars, last pushed 4mo ago), licensed MIT. It adds 129 tokens to every session and 3,037 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to auto-research-public, differing in 0 lines, and is treated as a copy.
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