# Leads

> >

- **Type:** Skill
- **Install:** `agentstack add skill-jayden3455-collab-claude-leads-skill-claude-leads-skill`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [jayden3455-collab](https://agentstack.voostack.com/s/jayden3455-collab)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [jayden3455-collab](https://github.com/jayden3455-collab)
- **Source:** https://github.com/jayden3455-collab/Claude-leads-skill

## Install

```sh
agentstack add skill-jayden3455-collab-claude-leads-skill-claude-leads-skill
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Lead Pipeline Skill

Full automated pipeline: **scrape → ICP filter → email verify → personalize → CSV**

Pipeline lives at `pipeline.py`. All leads export to `output/`.

---

## Setup (one-time per machine)

```bash
git clone https://github.com/jayden3455-collab/Claude-leads-skill.git
cd Claude-leads-skill
pip3 install -r requirements.txt
cp .env.example .env
# Fill in the three required keys (see below)
```

### Required API keys (fill into `.env`)

| Key | Where to get it | Purpose |
|-----|----------------|---------|
| `ANTHROPIC_API_KEY` | console.anthropic.com → API Keys | ICP filter + personalization |
| `AI_ARK_API_KEY` | ai-ark.com → Developer Portal | Lead scraping database |
| `MILLION_VERIFIER_API_KEY` | millionverifier.com → API | Email verification |

**Never hardcode keys — always use the `.env` file.**

---

## Running the pipeline

```bash
python3 pipeline.py ""  [personalization_style]
```

### Arguments

| Arg | Required | Description |
|-----|----------|-------------|
| `` | Yes | Plain-English description of who to target |
| `` | Yes | Hard cap on leads to process. **= AI Ark credits spent.** |
| `[personalization_style]` | No | `case_study` (default), `top_service`, `top_competitor`, `none` |

### Credit math
- Each lead processed through email export = **1 AI Ark credit**
- Set `max_leads` to stay under your credit budget
- Safe default: `9000` (leaves buffer in a 10k-credit account)
- Typical email hit rate: 8–13% depending on niche

### Example invocations

```bash
# Boutique M&A advisory firms, US, decision makers
python3 pipeline.py "Buy-side M&A advisory firms in the US, company size 5-100 employees, decision makers: Managing Partner, Managing Director, Partner, Principal, CEO, Founder" 9000

# PR agencies, broader company size
python3 pipeline.py "PR agencies and public relations firms in the US, company size 5-200 employees, decision makers: CEO, Founder, Owner, Partner, Managing Director, Director, VP, President" 9000

# Podcasting agencies
python3 pipeline.py "Podcasting agencies and podcast production companies in the US, company size 5-100 employees, decision makers: CEO, Founder, Owner, Director" 5000 case_study

# No personalization (faster, cheaper on Claude tokens)
python3 pipeline.py "SaaS companies, US, 10-200 employees, founders and CEOs" 9000 none
```

---

## Pipeline stages

| Stage | What happens | Output |
|-------|-------------|--------|
| **1. Scrape** | Claude converts ICP → AI Ark filters, fetches profiles + emails | `output/checkpoints/01_scraped_*.csv` |
| **2. ICP filter** | Claude Haiku scores each lead PASS/FAIL against the ICP | `output/checkpoints/02_icp_filtered_*.csv` |
| **3. Email verify** | Million Verifier bulk-verifies all emails, keeps `ok` + `catch_all` | `output/checkpoints/03_verified_*.csv` |
| **4. Personalize** | Visits each company website, extracts top case study or service | Final CSV |
| **5. Export** | Final CSV lands in `output/pipeline_.csv` | `output/pipeline_*.csv` |

---

## Output CSV columns

`First Name`, `Last Name`, `Full Name`, `Email`, `Email Status`, `Title`, `Seniority`, `Department`, `LinkedIn URL`, `Person Location`, `Person City`, `Person State`, `Person Country`, `Company`, `Company Domain`, `Company LinkedIn`, `Company Size`, `Company Industry`, `Company HQ City`, `Company HQ Country`, `ICP Match`, `Personalization`, `Top Competitor`

---

## How to pick `max_leads` and ICP

**Company size guidance:**
- Too small to have budget: under 5 employees
- Sweet spot for cold email clients: 5–100 employees
- Has in-house teams: 200+ employees

**Niche pool sizes (AI Ark, approximate):**
- Hyper-niche (M&A advisory, podcasting agencies): 3,000–5,000 people
- Mid-size niche (PR agencies): 10,000–20,000 people
- Broad (marketing agencies, SaaS): 50,000+ people

**Expected yield per 9,000 credits:**
- 8–13% email hit rate → 720–1,170 with emails
- ICP filter removes ~40–60% → 288–700 pass
- MV verification removes ~1–5% → **250–680 verified leads**

To hit 2,000+ verified leads, either:
1. Run multiple niches and merge the CSVs
2. Target a broad niche with a large pool

---

## When a run finishes

Report:
- Niche targeted
- Credits used (= `max_leads` capped at pool size)
- Final verified lead count
- Output file path

If the pool is smaller than `max_leads`, credits used = pool size (AI Ark stops at last page).

---

## Gotchas

- **Email hit rate is not 100%** — AI Ark only has emails for 8–13% of people in most niches. This is normal. The rest are scraped as profiles but dropped before ICP filtering.
- **Million Verifier can queue-delay** — small batches (<200 emails) sometimes sit at 0% for 20–30 minutes before jumping to done. Don't kill the process.
- **`catch_all` emails are kept** — MV pipeline keeps both `ok` and `catch_all` statuses. Only `ok` emails should go into cold email campaigns. Filter by `Email Status = ok` before uploading to Smartlead/PlusVibe.
- **AI Ark credits are spent on export, not search** — browsing profile pages is free; the `/people/export/single` call (which fetches the email) costs 1 credit each.
- **Never kill a running pipeline** — credits are already spent at the scrape stage. Killing mid-run orphans leads that are in the checkpoint but won't make it to the final CSV. Always let it finish.
- **Checkpoints are your safety net** — if the pipeline crashes after step 2, the ICP-filtered leads are saved. You can re-run verification manually against `02_icp_filtered_*.csv`.
- **python3, not python** — the system command is `python3`. `python` is not aliased on macOS by default.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [jayden3455-collab](https://github.com/jayden3455-collab)
- **Source:** [jayden3455-collab/Claude-leads-skill](https://github.com/jayden3455-collab/Claude-leads-skill)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-jayden3455-collab-claude-leads-skill-claude-leads-skill
- Seller: https://agentstack.voostack.com/s/jayden3455-collab
- Browse the marketplace: https://agentstack.voostack.com/browse

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