Lead Scraping Plan — signal-scored outbound pipeline for the staffing industry

An agentic outbound pipeline + mobile command center for a healthcare-staffing agency: scrape US healthcare-admin job postings, score them against the agency's actual buyer ICP, gate on company size for free before spending a credit, enrich, write cluster-routed sequences, and hand booked replies to the sales team — with a feedback → learning → eval-gated loop that actually closes.

Built as a demonstrably better rework of an existing open-source outbound dashboard ("TalentBridge"), aligned to a finalized scraping plan (v3).

---

Why this beats the reference dashboard (measured, not claimed)

On 61 real human-rated postings exported from the original dashboard:

	Original AI	This scorer v1
Agreement with human gold	67.2%	77.0%
Leads thrown away (false-disqualify)	20 (33%)	4 (6.6%)

The original AI scores against a job-seeker objective — it disqualifies postings that "require US work authorization." But the business here is a staffing seller: those US practices hiring junior admin roles are the customers. Re-orienting the objective from the 61 corrections recovers 16 of the 20 wrongly-discarded leads. The remaining 4 are what the feedback-learning loop targets next (toward a 90% eval-gate target).

Run it yourself: python lib/eval/eval_holdout.py --baseline vs python lib/eval/eval_holdout.py.

Architecture wins over the reference app

Reference app	This repo
Two scorers — the daily cron ran a keyword filter; the "AI" only ran on a manual button, so automation bypassed the AI	One scorer (pipeline/score.py + TS), driven by one versioned rubric.config.json, same path in cron and dashboard
Feedback = 3 overwrite columns on the job row; unbounded free-text "learned rules"; the retrain cron never scheduled; no eval harness	First-class feedback history + scored/decaying learned_rules + an eval gate: a candidate must beat baseline on the locked holdout before it ships
No size gate — a 2,000-person RCM firm slipped through on name alone	Free NPPES provider-count gate before any paid enrichment
Single Gmail sender, desktop-only, real email leaked on a public endpoint	Verify → warmed/rotated send, mobile-first, auth-gated, MCP-operable from Claude
One lead per job posting — the same company repeated across every open role	Account-centric CRM: one account per company, roles grouped as opportunities, 3+ roles score higher

Credit where due: the reference repo's prompt-injection sanitization, DRY_RUN + suppression + send caps, dedup, and LLM cache are good patterns and are reused here.

---

What's in the box

web/                 mobile-first dashboard (DRY-RUN demo, runs on seed data, deploys as static)
lib/scoring/rubric.config.json   SINGLE SOURCE OF TRUTH — weights, flags, caps, gate, clusters
pipeline/score.py    the one config-driven scorer (re-oriented for a staffing seller)
pipeline/scrape.py   JobSpy scraper (Indeed live; LinkedIn actor; free boards)
lib/eval/            the eval harness (agreement % + false-disqualify on the 61-entry holdout)
eval/holdout/        the 61 human-rated postings (frozen truth set)
db/seed/             259 scored postings, 61 feedback, 7 learned rules, seed eval runs
mcp/                 Claude MCP server — run the pipeline by talking to Claude

Run the demo dashboard locally

cd web && python -m http.server 8080   # open http://localhost:8080 (mobile-friendly)

Screens: Pipeline (tier board + priority accounts) · Jobs (searchable table + CSV export) · CRM (account-centric, deduped) · Review (active-learning feedback loop) · Outreach (sequence queue) · Learning (eval history, learned rules, teach-a-rule) · Tools (integration health + flow) · Settings · Why better (the benchmark above).

Status (DRY-RUN)

Free/live: JobSpy (Indeed), NPPES size gate, the deterministic scorer, the eval harness. Stubbed until keys: LinkedIn actor, Anthropic (Haiku/Sonnet — scoring/sequences run cached), Clay, Cleanlist, SalesBlink, Sendr.io, Close CRM, Supabase. Every stub is labeled on the dashboard's Tools panel. Nothing sends or spends in DRY-RUN.

Notes on scope: "trained" here means dynamic feedback few-shots + a distilled, eval-gated rule store + versioned rubric — not a fine-tuned model. LinkedIn scraping carries a ToS caveat. Company/contact names in the seed data are real public job postings (scraped, not private); learned-rule values stay generalized.