auto-research-public

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: auto-research-public
description: Autonomous cold email campaign launcher. Takes one target company domain, scrapes their website, generates an ICP with Claude, pulls matching leads via Prospeo, enriches emails + company descriptions, personalizes each lead with parallel Claude Code Task sub-agents (A/B/C variants), and uploads as a live Smartlead campaign. Uses local JSON files for state (no database required). Use for automated daily campaign launches after you have an initial `client-profile.yaml` from /icp-onboarding. Triggers on "auto-research", "launch an automated campaign", "daily campaign", "run the research loop".
---

# 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.yaml` exists (run `/icp-onboarding` if not)
- [ ] `SMARTLEAD_API_KEY` in env
- [ ] `PROSPEO_API_KEY` in env
- [ ] `MILLIONVERIFIER_API_KEY` in env (for email validation)
- [ ] At least 20 Smartlead inboxes tagged "active" (run `/smartlead-inbox-manager` first)
- [ ] 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

```bash
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`:

```json
{
  "job_titles": ["VP Marketing", "Head of Marketing", ...],
  "seniorities": ["Vice President", "Head", "Director"],
  "industries": ["Software Development", "Financial Services"],
  "company_size_min": 50,
  "company_size_max": 500,
  "countries": ["US"],
  "excluded_industries": ["Religious Institutions", "Government Administration"]
}
```

Claude MUST use exact Prospeo industry names from `~/cold-email-ai-skills/skills/icp-onboarding/references/prospeo-industries.md`.

### Phase 3: Prospeo search

```bash
npx tsx scripts/phase-prospeo.ts --filters-file=/tmp/auto/filters.json --max-leads=1000 --out=/tmp/auto/leads.json
```

Output: JSON with `leads` array. Each lead has: `first_name`, `last_name`, `email` (may be empty), `linkedin_url`, `job_title`, `company_name`, `company_domain`, `company_industry`, `company_headcount`, `company_description`.

### Phase 4: Email waterfall + description enrichment

```bash
npx tsx scripts/phase-enrich.ts --leads-file=/tmp/auto/leads.json --out=/tmp/auto/enriched.json
```

The script:
1. Checks each lead for email; if missing, hits Prospeo's `enrich-person` endpoint
2. If company_description is thin (<50 chars), scrapes company_domain homepage
3. Runs MillionVerifier on every candidate email
4