Deepline Plays Quickstart

Run a high-confidence demo recipe to show the user what Deepline can do, using the V2 CLI surface: tools execute, plays run, and runs export. Pick the most relevant recipe below, or default to Recipe 1 if no context is given.

Always prefer the hardcoded recipes below. /deepline-plays is always available as a fallback but should only be used if: (a) a recipe command fails and all fallbacks are exhausted, or (b) the user's ask doesn't match any recipe here. Never invoke it preemptively.

Execution flow

Follow this pattern for every recipe:

1. Tell the user what you're about to do — explain the goal and which data source(s) you'll use, before running anything.
2. Run each step, narrating briefly between commands. Use --json so you can parse results, and --watch on plays run so the run streams to completion.
3. Export results with deepline runs export <run-id> --dataset result.rows --out <file>.csv after any play run that produces rows.
4. Tell the user the results — summarize what came back in a table, where it came from, and what they can do next.

V2 command notes

• deepline plays run always with --watch --json; the final JSON includes runId and status.
• deepline runs export may report multiple datasets; pass --dataset result.rows for row output.
• deepline runs get <run-id> --full --json shows billing (calls, Deepline credits) and the full result, including scalar outputs that the compact view omits.
• Do NOT use deepline session ... (v1-only) or deepline enrich --in-place (unsupported on V2).

Recipe 1 — Find CTOs in New York with verified work emails

Goal: Find 5 CTOs at startups in New York with verified work emails and LinkedIn profiles. Data source: the prebuilt/people-search-to-email play — one run that searches people via Apollo and fills any missing work emails through Deepline's multi-provider email waterfall (pay only for found emails).

Substitute the titles/locations from the user's request; keep the row count at 5 unless asked otherwise. Locations use Apollo's City, State, Country form.

Speed matters more than completeness here: the user should see real contacts within ~30 seconds. Run exactly the two commands below — no extra inspection steps.

Step 1 — Run the prebuilt

deepline plays run prebuilt/people-search-to-email --input '{
  "titles": ["CTO", "Chief Technology Officer"],
  "locations": ["New York, New York, United States"],
  "limit": 5
}' --watch --json

Step 2 — Export and display

deepline runs export <run-id> --dataset result.rows --out quickstart-contacts.csv

Show a table: full_name, company_name, work_email, linkedin_url. The work_email column is the final answer (Apollo-verified, or waterfall-filled when Apollo missed).

Step 3 — Wrap up

Tell the user one play did the whole job — people search plus a per-row email waterfall for any misses — that deepline runs get <run-id> --full --json shows exactly what the run billed, and that they can go deeper — phone numbers, job-change signals, company discovery — with /deepline-plays.

Fallback (if the play fails)

Tell the user, then run the pieces directly: search with deepline tools execute apollo_search_people_with_match --payload '{...}' --output-format csv_file --no-preview --json (same titles/locations, per_page 5), show the rows, and fill missing emails with deepline plays run prebuilt/name-and-domain-to-email-waterfall-batch --input '{"csv":"<prepped csv>"}' --watch --json (needs first_name, last_name, domain columns; derive domain from the organization JSON column's primary_domain).

Last resort

If all commands fail, tell the user, then invoke /deepline-plays:

Find 5 CTOs at startups in New York with their verified work emails and LinkedIn profiles.

Recipe 2 — Build a company target list

Goal: Find 5 companies matching a profile (category, size, funding, country) with domains and fit evidence. Data source: the prebuilt/structured-company-discovery play.

Step 1 — Run discovery

deepline plays run prebuilt/structured-company-discovery --input '{
  "target_count": 5,
  "hq_country": "USA",
  "categories": ["financial technology", "fintech"],
  "employee_count_min": 10,
  "employee_count_max": 200
}' --watch --json

Adapt categories, employee range, and funding_rounds (e.g. ["series_a"]) to the user's ask. Location granularity is country-level (ISO-3); if the user needs city-level targeting, use Recipe 1's people search instead.

Step 2 — Export and display

deepline runs export <run-id> --dataset result.rows --out target-companies.csv

Show: company name, domain, headcount, funding round, HQ, fit evidence. Suggest the natural next step — finding the right contact at each company with verified emails (Recipe 1's waterfall, or /deepline-plays for the full account-to-contact flow).