/niche-signal-discovery

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

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.

Signal reliability hierarchy

Highest → lowest confidence:

1. **Job listings** — active budget + acknowledged pain. Highest-intent. 2. **Analyst validation** (Gartner/Forrester) — typically 4-7x lift, rare in lost. 3. **Compliance infrastructure** (SOC2/GDPR/ISO) — procurement maturity. 4. **Buyer pain language** on careers/blog — operational awareness. 5. **Tech stack tools** (niche SaaS) — infrastructure readiness. 6. **Website product/marketing content** — variable; can be buyer OR competitor.

**When website signals fail:** For B2B back-office tools (AR, billing, compliance), buyers don't publish their pain on marketing pages. Prioritize jobs + tech stack + firmographics for these verticals.

What NOT to use for scoring

CRM fields populated by AE activity — catalyst note count, OCR-derived counts (`number_of_champions_c`, `number_of_decision_makers_c`), MEDDPICC picklists, any "did the AE do X on this opp" field — correlate with win-rate as **engagement artifacts, not causal signals**. They get filled in _after_ the AE decides an opp is worth working. **Never use them as scoring inputs.** On one real run, catalyst notes showed "109x lift" — almost made the TL;DR before we caught the direction of causality.

Rule of thumb: every scoring input must be observable BEFORE the AE touches the account. Read `references/scoring-pitfalls.md` for the full list and the "safer alternative read" for loss-reason data.

Step 0: Target company discovery

**Do this FIRST.** The entire pipeline (exa query, keywords, tech stack, job roles) adapts based on this discovery; skipping it produces generic/irrelevant signals.

deeplineagent: "Research {{company-domain}}. Summarize what the company sells, who they sell to, what makes them different, and any example customers."

Document: (1) product category, (2) target buyer persona, (3) key differentiation, (4) example customers.

Step 0.5: Ecosystem discovery

Three parallel `deeplineagent` queries:

• **Competito