Signal Prospector

A signal-based B2B prospecting engine that runs on free infrastructure. It finds companies that just entered "buying mode" — funding, new leadership, hiring, expansion, competitor/topic engagement — scores them against your Ideal Customer Profile, enriches the decision-maker with a free MX-validated email guess, and drafts a personalized, signal-aware outreach message. Inbound replies get a ready-to-send AI draft.

The whole thing is exposed three ways: a CLI, a self-serve Flask dashboard, and an MCP server so you can prospect from Claude in natural language.

The Flask dashboard. Every score and the funnel above are computed by the engine — the companies, contacts and signals shown here are illustrative demo data, not real records.

The leads table makes the scoring transparent: each row carries its triggering signal, the enriched contact and email, a lifecycle status, and a plain-English "why" the lead scored where it did.

Why it's interesting

Most "prospecting tools" are either a static lead list or a black box. This one is the opposite: a transparent detect → score → enrich → draft → reply → optimize loop where every number is explainable and every external call is free by default.

• Eight signal families behind one contract. Funding, job-change, leadership-change, hiring, expansion, competitor-engagement, topic-engagement and news are each a small detector returning a typed Signal, registered in a registry and fed injected free search_fn / fetch_fn functions — so adding a detector, or swapping the search backend, is a drop-in.
• An explainable ICP scorer, not a ranking black box. The 0–100 score is a precise weighted blend of right buyer × right moment (formula below), and every lead ships a plain-English "why this lead" reason string.
• A free email-enrichment waterfall. Decision-maker discovery parses public search snippets, strips RTL/zero-width Unicode, then infers the most likely corporate email from name+domain patterns and validates the domain over DNS — no paid enrichment vendor, and it never invents an address it can't form.
• Free-first by construction. Discovery is self-hosted SearXNG; fetching is an httpx → headless-browser → PDF cascade with trafilatura extraction; generation is DeepSeek (any OpenAI-compatible endpoint). Paid fallbacks stay off unless you key them.
• 561 tests across detectors, scorer, enrichment waterfall, reply classifier and the web layer. Unknown data always stays n/a rather than being fabricated.

The scoring, precisely

No hand-waving — this is exactly how a lead's score is computed (prospector/icp.py):

Because missing evidence is neutral rather than punishing, a strong buying signal can still surface a lead whose firmographics are only partly known — which is the whole point of signal-based prospecting.

The email waterfall, precisely

Enrichment never fabricates an address (prospector/enrich.py):

1. Resolve a mail-capable domain from the company site, rejecting aggregator/social hosts and website-builder domains (*.wixsite.com, *.github.io, …) that are never the company's own mail domain.
2. Generate candidates from the person's name, most-likely-first: {first}.{last} · {first} · {f}{last} · {first}{l} · {first}_{last} · {last} — dropping al-/el- prefixes and rejecting RFC-implausible locals (a bare initial like bisrat.d.@ is never emitted).
3. Classify the domain over DNS: verified (publishes MX), risky (resolves but no MX), invalid (NXDOMAIN or RFC 7505 null-MX). Outbound-25 SMTP probing is deliberately not done.

How it works

 ICP (a preset, or auto-drafted from a website URL)
        │
        ▼
  1. DETECT   eight signal detectors ──► Signal[]          prospector/signals/*
        │      (SearXNG discovery + free httpx→browser→PDF scrape cascade,
        │       trafilatura extraction, optional LLM entity refinement)
        ▼
  2. SCORE    fit × signal-strength × recency ──► 0..100   prospector/icp.py
        │      + explainable "why this lead" reason
        ▼
  3. ENRICH   decision-maker + email waterfall (MX-validated)   prospector/enrich.py
        │
        ▼
  4. DRAFT    DeepSeek signal-aware outreach (+ A/B variants)    prospector/outreach.py
        │      multilingual (en/ar/fr), draft-first (never auto-sent)
        ▼
  5. REPLY    classify inbound + draft a response      prospector/reply.py
        │
        ▼
  6. OPTIMIZE weekly "what converts" feeds back into step 4     prospector/campaign.py

  Surfaces:  CLI (cli.py) · Flask dashboard (prospector/web) · MCP server (prospector/mcp_server.py)
  Storage:   SQLite (prospector/db.py)  ·  Export: CSV / XLSX / HubSpot / Pipedrive

• prospector/signals/ — one detector per signal family behind a shared Signal dataclass + persist_signals; a registry runs them all with injected free search/fetch functions, so detectors are unit-tested offline.
• prospector/icp.py — the ICP model and the transparent composite scorer above.
• prospector/enrich.py — contact discovery + the email-pattern waterfall + free MX validation.
• prospector/outreach.py / reply.py / campaign.py — signal-aware draft generation, reply classification, and the weekly optimization loop.
• prospector/web/ — the self-serve Flask dashboard (onboarding, run progress, draft review, replies, exports).
• cli.py / prospector/mcp_server.py — the same engine over a CLI and an MCP stdio server.

Tech stack

Python · Flask + waitress · SQLite · httpx · BeautifulSoup / lxml / trafilatura · feedparser · rapidfuzz · dnspython · openpyxl · DeepSeek (any OpenAI-compatible endpoint) · self-hosted SearXNG · MCP · pytest

Running it locally

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
pip install -e .              # exposes the `prospector` command (same as `python cli.py`)

cp .env.example .env          # fill in DEEPSEEK_API_KEY etc. (all optional; it degrades)
prospector selfcheck          # verify config + external deps -> READY

# Fastest path: seed an ICP from a preset, run the pipeline, show leads
prospector quickstart

# ...or everything in the browser
prospector serve              # http://127.0.0.1:8101

# ...or drive it from Claude
prospector mcp                # stdio MCP server (find_leads / draft_outreach / query_analytics / ...)

Manual pipeline:

prospector init-icp "KSA RE developers" \
    --titles "CEO,Managing Director,Head of Development" \
    --industries "real estate,proptech" --geos "Saudi Arabia,Riyadh,GCC" \
    --keywords "off-plan,residential,giga-project" --competitors "ROSHN,Dar Al Arkan"

prospector run --icp "KSA RE developers" --limit 30    # detect -> score -> enrich -> draft
prospector leads --min-score 70
prospector export --xlsx data/leads.xlsx

Full command list: quickstart, presets, init-icp / list-icp, detect, score, enrich, draft, run, replies ingest|list, export, notify, report / leads, serve, mcp, selfcheck.

Safety defaults

• Draft-first — nothing is sent automatically. Email sends only with EMAIL_AUTOSEND=1 and SMTP configured; LinkedIn is never auto-sent.
• Free-first — paid fallbacks (Serper, Firecrawl, CRM) stay off unless keyed.
• Never fabricates — unknown data stays n/a; detectors skip results they can't ground to a real company.

Data & privacy

This repository contains code only — no leads, no database, no scraped output. The data/ directory (SQLite DB, search caches, session key) and .env are gitignored and never published. The example ICP presets name public real-estate developers, and all test fixtures use synthetic people with reserved example.com domains.

Project layout

prospector/      the engine: signals/ · icp · enrich · outreach · reply · campaign · db · web/ · mcp_server
cli.py           the `prospector` command surface
tests/           561 tests (detectors, scorer, enrichment, replies, web)
deploy/          systemd unit + Caddy reverse-proxy snippet
scripts/         nightly cron pipeline

License

MIT — see LICENSE.