/deepline-pre-research

name: deepline-pre-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 Pre-Research

Quick Start

npm install -g deepline
# Fallback for secure sandboxes: mkdir -p "$HOME/.local" && npm config set prefix "$HOME/.local" && export PATH="$HOME/.local/bin:$PATH" && npm install -g deepline --registry https://code.deepline.com/api/v2/npm/
deepline auth register --wait auto
deepline auth wait --timeout 120 # completes Cowork/browser approval; no-op if already connected
deepline auth status
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 preflight --json` as one standalone command and wait for it to finish. Never submit preflight beside another Deepline command. After it succeeds, prefix every Deepline command that may run concurrently with `DEEPLINE_SKIP_SELF_UPDATE=1`; serial commands may stay bare.

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 (`serper`/`exa`, or the equivalent web-search tool) during the public-source fanout. If a run names public datasets without having searched for them this session, it has failed the fanout — no exceptions for "obvious" verticals. Naming a source family from memory is a draft, not a finding; the finding is the exact artifact the search returns. (Eval evidence: runs that skipped web search lost or tied on exactly the prompts where a competitor searched and surfaced concrete artifacts.)
• **Resolve every materializable dataset to its exact artifact, not its family.** For each public dataset/registry you recommend, the fanout must return and 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 **mirror** (e.g. data.gov catalog copy) that is easier to pull; (4) an existing **open-source parser or GitHub repo** that already structures it, when one exists (search `"<dataset> parser github"`); (5) the **government statistical registry** for the vertical when one exists (BLS QCEW + NAICS codes, Census County Business Patterns, etc.) for free establishment counts and sizing. "Source family named" is not done. "Exact file + URL + mirror + parser + NAICS code recorded" is done. 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 ScrapeCreators, X.com, Reddit comments, TikTok, Instagram, YouTube transcripts, Bluesky, Truth Social, HN, or Polymarket, include a current support/gap assessment instead of pretending every source is native.
• Do not depend on `/last30days` at runtime. Reference it only as a design benchmark for source breadth and synthesis discipline.
• Public registries and niche datasets that do not have native Deepline tools are still valid sources through generic web/search/extraction routes. For example, the NPI registry for healthcare provider taxonomy can be discovered and pulled through generic web/API search and extraction even when no native `npi` tool exists. Classify this 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`.

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