niche-signal-discovery

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---
name: niche-signal-discovery
disable-model-invocation: false
description: 'Discover niche first-party signals that differentiate Closed Won vs Closed Lost accounts for ICP analysis. Use when the user provides won/lost customer domain lists and wants differential signals (website content, job listings, tech stack, maturity markers) to build account scoring models and prospecting criteria. Triggers: ICP analysis, niche signals, won vs lost analysis, differential signals, signal discovery, ICP signal report, account scoring signals, lead scoring, first-party signals, buyer signals. Before reading this file, first read deepline-gtm to understand the Deepline CLI tool and how to use it. Then read this file for guidance on the task.'
---

# Niche Signal Discovery

## Quick Start

```bash
npm install -g deepline
# Fallback for secure sandboxes: mkdir -p "$HOME/.local" && npm config set prefix "$HOME/.local" && export PATH="$HOME/.local/bin:$PATH" && npm install -g deepline --registry https://code.deepline.com/api/v2/npm/
deepline auth register --wait auto
deepline auth wait --timeout 120 # completes Cowork/browser approval; no-op if already connected
deepline auth status
deepline -h
```

## CLI resolution

Run `deepline` when it is available. If the shell reports that command is missing, use `<workspace-root>/.deepline/runtime/bin/deepline` (or the npm-created `.cmd` shim on Windows). If neither exists, follow `https://code.deepline.com/INSTALL.md` to set up Deepline.

Discover differential signals between Closed Won and Closed Lost accounts by extracting multi-page website content and job listings, then computing Laplace-smoothed lift scores to identify what distinguishes buyers from non-buyers.

## Prerequisites

- **Deepline CLI** — All enrichment runs through `deepline enrich`; route through prebuilt plays and customer-configured provider connections rather than hardcoding provider-specific prospecting tools.
- **Python 3** stdlib only — no pip dependencies for any shipped script.
- **Credits** - paid web extraction plus CrustData job search. Run a small sample or `deepline tools describe crustdata_v2_job_search --json` for current Deepline-facing pricing before scaling. Step 7 contact discovery is additional. **Always get user approval before paid steps.**

## Deepline-First Principle

Use `deepline enrich` for all enrichment and `deepline tools execute` for one-offs. Inspect CSV shape and samples with `deepline csv show`; inspect run state with the run/play URL or `deepline runs get` when a run id is available. Reruns are idempotent. Refer to `deepline-gtm` for command patterns and provider playbooks.

## Input requirements

- Won and lost customer domain lists (≥20 won + ≥10 lost for statistical significance)
- **Lookalikes can supplement Won** if Closed Won < 15. Add a Dataset Caveat to the report.
- **Target company context** from Step 0 — what they sell, who they sell to, key personas.

## Pipeline

```
0.    Discover target company (what they sell, who they sell to)
0.5.  Discover ecosystem (competitors, tech stack, buyer personas)
1.    Prepare input CSV (deduplicate within won/lost groups)
1.0.5 Build "do not re-contact" index from user's existing list (scripts/dedupe_utils.py)
1.5.  Generate vertical-specific configs (keywords, tools, job roles)
2.    Multi-page website + job extraction (deepline enrich)
3.    Quality gate — verify file completeness + coverage (>80%)
3.5.  Review configs against enriched data
4.    Differential analysis (scripts/analyze_signals.py)
5.    Generate report — every top signal must include cited evidence
6.    Signal interpretation review
7.    Top 10 net-new prospects [REQUIRED] + contacts/emails [optional, costs credits]
```

**Step 7 is required.** A signal report without 10 actionable companies forces the reader to do their own prospecting pass — exactly the expensive thing they wanted to skip. Contacts/emails are optional only because they cost extra credits; always offer them.

##