Niche Signal Discovery

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:

• Competitors — "{product category} software alternatives competitors" → 3-5 names
• Tech stack — "{buyer persona} software stack" → 10-15 tools by category
• Job roles — "{buyer persona} job titles" → 10-15 title variations

These feed Step 1.5 config generation.

Step 1: Prepare input CSV

domain,status
customer1.com,won
non-customer1.com,lost

Deduplicate within the input. If a domain appears in BOTH won and lost (same company, multiple deals), Deepline only fetches job listings once — silently undercounting won_with_jobs. Remove ALL rows for cross-group domains:

from collections import Counter
counts = Counter(r['domain'] for r in rows)
duplicate_domains = {d for d, c in counts.items() if c > 1}
# Drop every row in duplicate_domains, not just one copy.

Step 1.0.5: Build "do not re-contact" index

Before any prospects ship in Step 7, dedupe candidates against whatever "already known" list the user provides — customers, CRM export, past outbound, a previous run's output. Always ask explicitly; if the user has no list, note it as a caveat in the final report rather than silently skipping.

Order: apex domain first, fuzzy company name as fallback. Use the shipped helper — it handles public-suffix multi-label TLDs (co.uk, co.jp, com.au) and corporate-suffix stripping:

python3 scripts/dedupe_utils.py --selftest   # one-time sanity check
python3 scripts/dedupe_utils.py \
    --existing customers.csv --candidates prospects_raw.csv \
    --out-actionable prospects_actionable.csv --out-matched already_known.csv

Don't silently drop CRM matches — categorize them: Net-new / Account-only / Re-engage / Active-open / Current-customer.

Read references/dedupe.md for the failure modes (raw-string match missing amsynergy.nikon.com → nikon.com cost 24 of 50 prospects in one run), category definitions, and library usage.

Step 1.5: Generate vertical-specific configs

Create three JSON files in output/{{company}}/:

{{company}}-keywords.json    # product category, pain language, competitor names, maturity terms
{{company}}-tools.json       # niche SaaS tools by category
{{company}}-job-roles.json   # buyer persona job titles

Read references/keyword-catalog.md for the JSON schema, generation patterns, and multi-vertical examples (creative ops, AR automation, sales engagement, developer tools).

Validation: Do the configs match the target's vertical and buyer persona? If not, refine based on Step 0/0.5 findings.

Step 2: Deepline enrichment

Never scrape just the homepage. Use Serper to discover relevant pages, Firecrawl to extract content.

Step 2a - Discover pages with Serper (0.02 credits/company):

deepline enrich \
  --input output/{{company}}-icp-input.csv \
  --output output/{{company}}-discovered.csv \
  --name niche-pages-discovery \
  --with '{"alias":"pages","tool":"serper_google_search","payload":{"query":"site:{{domain}} product OR features OR integrations OR customers OR security OR pricing OR careers OR about"}}' \
  --json

Adapt the query by vertical: add compliance OR audit for back-office, documentation OR api for developer tools, portfolio OR workflow for creative tools.

Step 2b - Scrape top 5 pages with Firecrawl (0.05 credits/company):

Extract URLs from Serper results, then scrape each:

deepline enrich \
  --input output/{{company}}-urls.csv \
  --output output/{{company}}-scraped.csv \
  --name niche-page-scrape \
  --with '{"alias":"content","tool":"firecrawl_scrape","payload":{"url":"{{url}}"}}' --json

Aggregate scraped pages back into one row per domain, formatted as {"data":{"results":[{url, title, text}]}} for the analysis script.

Step 2c - Job listings with Crustdata:

deepline enrich \
  --input output/{{company}}-aggregated.csv \
  --output output/{{company}}-enriched.csv \
  --name niche-job-listings \
  --with '{"alias":"jobs","tool":"crustdata_v2_job_search","payload":{"filters":[{"filter_type":"company.basic_info.primary_domain","type":"=","value":"{{domain}}"}],"limit":100}}' --json

Estimate the paid-step total from current tool pricing before scaling. Get user approval first.

Step 3: Quality gate

deepline enrich returns to terminal before OS buffers fully flush. Running the analysis script immediately can read a partially-written file and produce won_with_jobs: 0 even when data is fine. Always verify:

INPUT_ROWS=$(wc -l < output/{{company}}-icp-input.csv)
OUTPUT_ROWS=$(wc -l < output/{{company}}-enriched.csv)
echo "Input: $INPUT_ROWS, Output: $OUTPUT_ROWS"  # should match

Then spot-check that won rows have job data, that website coverage is >80%, and that average content depth is 6-8 pages / 12-20K chars per company.

Read references/quality-gate.md for the full verification script, the buffer-flush retry pattern, and the "auto-extracted domain validation" check that has caught up to 53% false-positive rates in CRM-exported customer lists.

Step 3.5: Review configs against enriched data

Inspect the enriched CSV before analysis:

deepline csv show --csv output/{{company}}-enriched.csv --summary
deepline csv show --csv output/{{company}}-enriched.csv --rows 0:5

Red flags:

• Keyword in <10% of enriched companies → too niche, broaden
• Keyword in >90% → too generic, refine
• Product-category keywords appear frequently in Won → wrong product category, those companies are competitors not buyers
• Job roles missing from actual listings → wrong buyer persona

Fix and regenerate configs if needed.

Step 4: Differential analysis

python3 scripts/analyze_signals.py \
  --input output/{{company}}-enriched.csv \
  --keywords output/{{company}}-keywords.json \
  --tools output/{{company}}-tools.json \
  --job-roles output/{{company}}-job-roles.json \
  --output output/{{company}}-analysis.json

The script computes substring-match presence, Laplace-smoothed lift, source breakdown (website/jobs/both), tech-stack mentions, job-role prevalence, anti-fit signals, and per-keyword evidence quotes (±40 chars with URLs) — the evidence array is what Step 5 renders.

Step 5: Report generation

Read references/report-template.md for the full report structure (Quick Reference Dashboard at the top, then detail sections), the signal-strength visual scale, prospecting-link format, and all quality rules. Critical rules in brief:

• Raw counts always (15% (6), not just 15%); sample sizes in headers (Won (n=37))
• Bold only signals with lift > 2x AND count ≥ 3 companies
• Flag n=1 signals — they're statistically meaningless
• Source evidence is mandatory for every top signal (lift ≥ 1.5 AND won ≥ 3) — 3-5 cited quotes per signal with source type, company, page/job title, ±40-char quote, and live URL. The analysis script outputs this; render it, don't decide whether to. Signals without 3+ citations get demoted and flagged *(insufficient evidence)*.
• Annotate each evidence quote with ✅ (clear buyer signal) or ⚠️ (vendor-adjacent — the company sells something similar, so the keyword on their product page isn't a buyer signal)
• Tier 1 cheatsheet point values must match the Section 6 scoring model — cross-check both before shipping

Step 6: Signal interpretation

Read references/signal-interpretation.md before writing interpretation columns. Key rules:

• Website content mentioning what the target sells = competitor signal (not buyer)
• Job listings = highest-intent buyer signal
• Same keyword means different things on product page vs careers page vs blog
• Tech stack tools need context — do they create or solve the target's problem?

Step 7: Top 10 net-new prospects (required)

10 companies are required for every run; contacts + emails are optional (additional Deepline credits). Always offer contact discovery; only run it if the user approves the spend.

# Companies only — no extra credits beyond Step 2 enrichment:
python3 scripts/find_contacts.py --input prospects_actionable.csv --output top10.csv --top 10 --no-contacts

# Companies + contacts + emails — asks for credit approval.
# --roles is REQUIRED in --contacts mode and must be the buyer-persona job
# titles surfaced in YOUR Step 0/0.5 (not a stale list from a different vertical):
python3 scripts/find_contacts.py --input prospects_actionable.csv --output top10.csv --top 10 \
    --contacts --roles "<persona job titles from Step 0.5>"

When --contacts is on, the orchestrator runs a 3-phase chain via Deepline:

1. company-to-contact (free, mature companies)
2. exa_search_people fallback for any company Phase 1 missed — mandatory. On the run that motivated this, Phase 1 returned 0 contacts on all 10 top prospects (small/non-US industrial); Exa found 15 real contacts at 6 of those 10 in the same pass.
3. name-and-domain-to-email-waterfall with linkedin_url supplied and apex-domain validation — providers return stale addresses (@orbitalatk.com for someone now at X-Bow, personal Gmails, wrong-company false positives). Mismatched apex → publish "(email not found)", keep the raw value in raw_email for auditing.

Read references/step-7-prospects.md for the required vs. optional output fields, the prospect-card skeleton, the Phase 2 Exa guardrails (title parsing + company-match filter), and the "10 is a ceiling, not a floor" guidance.

Enrichment data structure

After enrichment, each row has:

• website column → JSON: {"data":{"results":[{text, url, title}]}} (aggregated from Firecrawl scrapes)
• jobs column → JSON: {"result":{"listings":[{title, description, url}]}} (Crustdata format - note result not data, title not job_title)

scripts/analyze_signals.py auto-detects __dl_full_result__ columns; override with --website-col N --jobs-col N for other column names.

Common pitfalls (top 6 — full list in references/pitfalls.md)

1. Skipping target discovery (Step 0) → generic/irrelevant configs.
2. Homepage-only scraping → misses pricing, integrations, security, careers.
3. Generic tech stack ("AWS", "GitHub", "Slack" appear on most B2B sites) → search for niche SaaS specific to the buyer persona.
4. Trusting n=1 signals → require 3+ companies for Tier 1 scoring; flag single-company signals with a verification note.
5. Raw-string dedupe missing parent domains — amsynergy.nikon.com ≠ nikon.com for naive comparison. Always use extract_apex(). 24 of 50 "net-new" prospects in one real run were already in the CRM as parent-domain entries the raw-string dedupe missed.
6. Trusting confirmation-biased CRM fields (catalyst notes, OCR counts, MEDDPICC) as signals — they're downstream of AE engagement, not causal. Read the "What NOT to use for scoring" section above.

Read references/pitfalls.md for the full 18-item list including substring false positives, vendor-vs-buyer signal context, back-office-tool interpretation, and shipping-without-prospects.

Proven signal patterns

Read references/proven-signals.md for typical lift ranges across verticals (analyst validation 4.5-6.5x, hiring signals 3.8-5.5x, compliance infra 2.1-6.5x, etc.), high-confidence anti-fit patterns (consumer signals 0.2x, retention/churn 0.2-0.4x), and a starter 0-100 scoring model with three tiers (Core Fit / Buying Intent / Infrastructure Readiness).

References

• references/keyword-catalog.md — JSON schema + multi-vertical examples for Step 1.5 config generation
• references/dedupe.md — Step 1.0.5 dedupe failure modes, categorization rules, library usage
• references/quality-gate.md — Step 3 verification scripts, buffer-flush retry pattern, auto-extracted-domain validation
• references/report-template.md — Step 5 full report structure, signal-strength scale, prospecting-link format, all quality rules
• references/signal-interpretation.md — Step 6 buyer-vs-seller-vs-competitor rules
• references/step-7-prospects.md — Step 7 prospect-card skeleton, Exa guardrails, Phase 3 apex validation
• references/scoring-pitfalls.md — Confirmation-biased CRM fields to exclude from scoring
• references/pitfalls.md — Full 18-item pitfalls list
• references/proven-signals.md — Typical lift ranges + scoring model guidance
• scripts/analyze_signals.py — Step 4 differential analysis. Auto-detects columns.
• scripts/dedupe_utils.py — Step 1.0.5 + Step 7 email validation. extract_apex(), norm_name(), match_against_existing(). Stdlib only. --selftest flag for one-time install verification.
• scripts/find_contacts.py — Step 7 orchestrator. --contacts / --no-contacts toggle, 3-phase Deepline chain.

Changelog

• 2026-04-13 — Switched Step 2 from exa_search (5 credits) to Serper + Firecrawl (0.07 credits) for website content. Fixed analyze_signals.py to handle Crustdata's {"result":{"listings":[]}} wrapper. Verified E2E on 15 companies.
• 2026-04-07 — Added Step 1.0.5 (dedupe with apex helper), Step 7 (top 10 prospects required, contacts optional via --contacts/--no-contacts), references/scoring-pitfalls.md warning about confirmation-biased CRM fields, mandatory citation rule. Shipped scripts/dedupe_utils.py + scripts/find_contacts.py. Aggressively trimmed inline detail to references — moved Step 3 quality gate, Step 5 quality rules, Common Pitfalls (items 7-15), and Proven Signal Patterns into references/. SKILL.md went from 650 to ~250 lines via progressive disclosure.