Run a quick Deepline demo with a maintained Play.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
npm install -g deepline
deepline -h
Use the current Plays workflow for quick demos. Do not use the retired CSV enrichment command or its flags.
Ask which field the user wants to enrich and what identifiers they have.
Search for a maintained Play and inspect its input and output contract:
deepline search "<input you have -> result you need>" --type prebuilts
deepline describe prebuilt/<play-from-search>
Confirm the user wants to spend credits, then pilot one real record:
deepline plays run prebuilt/<play-from-search> \
--input '{"linkedin_url":"https://www.linkedin.com/in/example/"}'
Report the run result and the exact next step. Do not claim an output file exists unless one was written.
Inspect the file and choose a batch Play whose input schema matches its columns. Run a representative pilot before the full file:
deepline search "<input you have -> result you need>" --type prebuilts
deepline describe prebuilt/<batch-play-from-search>
deepline plays check prebuilt/<batch-play-from-search>
WORKDIR="deepline/data/quickstart-pilot" && mkdir -p "$WORKDIR"
head -n 4 leads.csv > "$WORKDIR/leads-pilot.csv"
deepline plays run prebuilt/<batch-play-from-search> --csv "$WORKDIR/leads-pilot.csv"
deepline runs export <run-id> --out "$WORKDIR/leads-pilot-enriched.csv"
Wait for a terminal run result before exporting. Review row alignment and results before asking whether to process the rest of the file. Never substitute a scalar Play for a batch Play unless its contract explicitly accepts rows.
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Build a Total Addressable Market list: size the audience, source companies from ICP filters, then find contacts at those companies at scale.
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Convert a Clay table configuration into local Deepline scripts. Handles extraction (MCP or script), documentation, action mapping, script generation, and parity validation against Clay ground truth.
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Use this skill when building, enriching, auditing, or uploading B2B paid ads audiences to Google Customer Match, Meta/Facebook Custom Audiences, or LinkedIn Matched Audiences. Triggers on phrases like '/deepline-ads-audience', '/deepline-ads-audiences', 'upload this audience', 'create custom audiences', 'personal email hashes', 'increase Facebook match rate', 'Google ads audience', 'Meta audience', 'FB audience', or any workflow that turns CRM/customer/contact data into paid ads upload lists. Skip for outbound prospecting sequences, cold email, or pure campaign copywriting.
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Use this skill when answering business analytics, RevOps, GTM metric, pipeline, revenue, funnel, customer, or warehouse questions with Deepline. Triggers on phrases like 'query Snowflake', 'analyze pipeline', 'total ACV', 'break down by quarter', 'use the semantic layer', 'run a semantic query', or any use of snowflake_get_semantic_layer / snowflake_run_semantic_query. Skip prospecting, enrichment, contact finding, outbound, or personalization workflows; use deepline-gtm for those.
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Use for CRM audits, duplicate detection, missing or stale fields, job changes, domain checks, acquisitions, parent-child relationships, repair plans, prevention, monitoring, and evaluation. Supports HubSpot, Salesforce, Attio, and mapped exports. Start with read-only analysis. Skip outreach writing, CRM interface design, and unrelated analysis.
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Build, publish, and verify a Deepline Play as a durable state machine over Customer DB tables, including a small paid pilot. Invoke this skill explicitly when the user asks to build an engine.
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