Lead Prospecting

Lead Prospecting

ICP → companies → the right person at each, as a CSV of leads (ICP-matched contacts, not qualified prospects). Email lookup and deliverability are the companion email-verification skill, handed off after Step 3. Never drafts or sends outreach.

Prerequisites

• taskfuel CLI connected (taskfuel whoami), balance ≥ ~$0.35 for a full discover run.
• Base taskfuel skill covers payment mechanics — quote with --dry-run, pay with --max-amount, never suppress stderr on a paid call. Read it first if you haven't.

Rules

1. Prompt, never guess. Ask for each step's parameters with examples so answering takes one word. Guessed ICPs and job functions return wrong results or none.
2. Announce cost, then wait for OK. Never chain straight into a paid call. Confirm before any step adds >~50 rows.
3. Seller-agnostic — no built-in ICP. Fit criteria come only from the user's own words; never assume what is being sold.

Intake

• Entry point: discover (Step 1), or bring-your-own domains — skip Step 1, seed the table from the pasted domains, start at Step 2.
• In discover mode, pick the route from the shape of the ICP — never ask the user to name a tool:
  • Categorical / numeric (an industry, a country, a headcount band) → route A.
  • Thematic / semantic ("customer-support automation", "cross-border payroll") → route B. FullEnrich's 490-value industry enum has no agent/AI entry — its documented stand-ins (Software Development for SaaS, Research Services for AI labs) are far wider than the theme.
  • Both kinds given → route B to find them, then filter on the numbers client-side.
• Fit criteria (optional): "What are you selling, and what makes a company a good fit?" Free text. Blank ⇒ skip Step 1b and present unranked.

Deliverable

leads-YYYY-MM-DD.csv, written at the first data step and enriched in place. Nothing discarded — one row per person, plus a placeholder row per contactless company. Selected? marks the primary contact; the rest stay as fallbacks. All rows go to the CSV; show only a top slice in chat.

Company | Domain | Fit | Fit reason | Person | Title | Seniority | LinkedIn URL | Selected? | Status

email-verification appends Email | Email source | Verified? | Score on handoff, and keeps writing the existing Status column rather than adding one — leave the four new columns absent until then rather than writing them empty.

Status: company-found → duplicate | no-contact-found | candidate → selected. The companion skill continues this same enum from selected, with email-found | email-constructed → verified | email-unconfirmed | verify-failed.

1 — discover companies · skipped in BYO-domains mode

Intake picked the route. Parameters, response shape and the gotchas that cost real money live beside this file — read the one route you picked before calling it:

• Route A · categorical/numeric ICP · $0.15/call, free when zero results → ROUTE-A.md
• Route B · thematic ICP · $0.01 flat → ROUTE-B.md

→ Company, Domain, HQ, headcount · company-found

1a — dedupe · free

• Same non-empty domain → keep the fullest, mark copies duplicate (park, don't delete).
• Cross-domain near-dupes (same name/HQ, different TLD) → ask which to keep; never auto-merge, since TLDs can be distinct entities.
• Empty domain → resolve by web search before Step 2.
• After route B: drop any platform host excludeDomains did not catch — app stores, review sites, link-in-bio pages. Unfiltered, these were 16 of 31 rows in testing (9 of them *.notion.site), so add each new offender to excludeDomains for the next call.

1b — score fit · free · only if fit criteria given

• The scoring text is already in the Step 1 response — no extra call. After route A use description + specialties; after route B use results[].entities[].properties.description plus the result text.
• To target the criteria directly, re-run route B with contents.highlights.query set to the fit criteria — it takes its own query and returns a per-company relevance snippet, still $0.01.
• Set Fit (High/Med/Low) and a one-line Fit reason citing the criteria.
• Annotate, don't gate — low-fit rows stay in the table; it is a heuristic over marketing copy.
• → Fit, Fit reason

2 — people-search · POST https://stableenrich.dev/api/fullenrich/people-search · $0.15/call, free when no matches

Ask: job function and/or seniority, and which domains.

• Batch every domain into one current_company_domains — same $0.15 as a single company. One-at-a-time multiplies cost by company count for no extra data.
• current_position_job_functions takes canonical Function enums (Software = engineering, Sales, Product, Design, Marketing, Human Resources, Operations, Finance, Legal, Executive & Leadership…). Full list: taskfuel discover POST https://stableenrich.dev/api/fullenrich/people-search.
• current_position_titles is exact-match only — "CTO" matches that literal string and returns far fewer rows. Prefer seniority or function.
• current_position_seniority_level: Owner, Founder, C-level, Partner, VP, Head, Director, Manager, Senior.
• AND across categories, OR within one — use ≤2 categories. Three or more often returns zero. Prefer domains plus one of seniority/function.
• Check metadata.total — a free early warning of pool size. Page with offset/search_after at another $0.15 each.
• Response is a lean roster: people[].employment.current.{title,seniority}, LinkedIn under people[].social_profiles.professional_network, company detail in a top-level companies map keyed by company_id, plus headline, location and educations. Add include_employment_history or verbose only if full career history is needed.
• Expect most domains to return nobody — 19 domains yielded 9 people at 6 companies. A thin roster is normal for small companies, not a failed call: mark the rest no-contact-found.
• When FullEnrich is unavailable, or the run has ≲15 companies, Exa substitutes at $0.01/company → PEOPLE-EXA.md.
• → Person, Title, Seniority, LinkedIn URL, country · candidate | no-contact-found

3 — select · free

• Ask run mode first: single (one picked company, cheapest) or fan-out (one contact per company; handoff cost scales at ~$0.021 each). All rows stay in the table either way — mode only sets how many advance.
• Then pick manually, or auto-select most senior: Owner/Founder/C-level > VP > Head > Director > Manager. In fan-out, ask the rule once, apply it to all, and show the planned table for a single approval.
• → Selected?=yes · selected

Handoff to email-verification · ~$0.021 per contact

Finding and verifying the address is a separate skill. Invoke it with the selected rows; it enriches this same CSV in place. Per contact:

{"first_name": "Alex", "last_name": "Moreau", "domain": "example.com"}

returning {"email", "email_source", "verified", "score", "status"}.

• Pass names exactly as they appear — never normalize diacritics on the way out. Transliteration is language-dependent and belongs to the finder; a fixed ü→u rule produced a confirmed-undeliverable address in testing.
• Hand off full surnames. Recover a truncated one from the LinkedIn handle, or select a different contact — a bare initial cannot be looked up.
• State the cost before handing off: selected rows × ~$0.021.