/deepline-research

name: deepline-research
description: 'Use when the user wants a last30days-style pre-research pass in Deepline: discover the critical public, private, CRM, workflow, social, and web data sources for a research/enrichment job; compare provider coverage; estimate Deepline credit cost; recommend the source plan before building or running the workflow; or build custom language/messaging from buyer, competitor, community, and CRM evidence. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain language.'

Deepline Research

Quick Start

npm install -g deepline
deepline -h

CLI resolution

Run `deepline` when it is available. If the shell reports that command is missing, use `<workspace-root>/.deepline/runtime/bin/deepline` (or the npm-created `.cmd` shim on Windows). If neither exists, follow `https://code.deepline.com/INSTALL.md` to set up Deepline.

Before the first Deepline fanout in a task, run `deepline billing balance` as one standalone command and wait for it to finish. Never submit it beside another Deepline command.

Find the highest-signal GTM data sources, public evidence, and market language for a research or enrichment job before building the pipeline. This is a standalone Deepline skill that should behave like `last30days` with a GTM data lens: broad source coverage, recency, community signals, citations, source stats, and a grounded "What I learned" synthesis. In Deepline, the report first explains what the research found; only after that does it translate the findings into Deepline tool contracts, private/proprietary joins, and Deepline-facing cost.

Attribution

Portions of the query-design, public-source fanout, and consolidation approach are adapted from [`mvanhorn/last30days-skill`](https://github.com/mvanhorn/last30days-skill), MIT licensed, copyright (c) 2026 Matt Van Horn. Keep `THIRD_PARTY_NOTICES.md` with this skill when packaging or distributing it.

Non-Negotiables

• Use Deepline's live tool catalog before naming provider actions. Do not rely on memory.
• **Run live web search. Do not answer public-source discovery from model memory.** Every run MUST execute real searches with a web-search tool during the public-source fanout, with no exception for "obvious" verticals. A source family named from memory is a draft; the finding is the exact artifact the search returns.
• **Resolve every materializable dataset to its exact artifact, not its family.** For each public dataset/registry you recommend, record: (1) the exact file/endpoint name (e.g. `IA_FIRM_SEC_Feed_YYYY_MM_DD.xml.gz`, not "the ADV bulk feed"); (2) the canonical download/API URL; (3) any easier mirror (e.g. a data.gov catalog copy); (4) an existing open-source parser or GitHub repo, when one exists; (5) the vertical's government statistical registry, when one exists (BLS QCEW + NAICS codes, Census County Business Patterns), for free establishment counts and sizing. See the Artifact Resolution Gate (§4.55).
• Public-source discovery comes before provider routing. First find the best public registries, datasets, communities, discussions, reviews, directories, papers, repos, and source leads; then use Deepline routes to materialize, validate, enrich, and activate them.
• Quote only customer-visible Deepline credits/USD. Never expose provider spend.
• Do not run paid or cost-unknown full-scope work without approval.
• Treat private data sources as first-class: CRM, warehouse, workflow runs, product analytics, support/calls, sheets, and customer-owned datasets.
• Treat custom language as a first-class workflow: buyer words, objections, category language, competitor framing, community slang, sales-call phrasing, and support-ticket pain belong in the source plan.
• Use tiny probes to learn coverage. Scale only after observed coverage, cost basis, and evidence quality are legible.
• If the user asks for a named provider, X.com, Reddit comments, TikTok, Instagram, YouTube transcripts, Bluesky, Truth Social, or HN, include a current support/gap assessment instead of pretending every source is native.
• Do not depend on `/last30days` at runtime: do not copy its local scripts or call it as a sub-skill. It is only a design benchmark for source breadth and synthesis discipline; execute through Deepline tools, plays, workflows, and private-data connectors.
• Public registries and niche datasets without a native Deepline tool are still valid sources through generic web/search/extraction routes (e.g. the NPI registry for healthcare provider taxonomy). Classify them as `available through generic route`, not as an unusable gap.
• Every recommended source must be classified as `native`, `available through generic route`, `private connector`, or `missing provider to add`.

Start Here

1. Read `references/source-map.md`. 2. For GTM use cases, also read `references/last30days-gtm-corpus.md`; it summarizes the source patterns that saved `last30days` runs proved useful. 3. For query design, source fanout, or `last30days` parity, also read `references/query-design.md` and `references/fanout-consolidation.md`. 4. For GTM/prospecting/enrichment, also read `../deepline-gtm/SKILL.md` and the sub-doc it routes to. 5. For existing warehouse metrics or RevOps analytics, also read `../deepline-analytics/SKILL.md`. 6. When using `last30days` as a design reference, verify its latest upstream release: `git ls-remote --tags https://github.com/mvanhorn/last30days-skill.git | tail -20`

Optional Skill Handoffs

This skill owns pre-research and source planning. Once the source plan is clear, it may hand off to:

• `deepline-gtm`: execute GTM sourcing, enrichment, waterfalls, personalization, contact discovery, or CRM activation, and build, wrap, fork, run, inspect, or automate the final repeatable workflow/play.
• `deepline-analytics`: