deepline-research

deepline-research is a skill for Claude Code, Codex from getaero-io/gtm-eng-skills. It costs 134 tokens per session (5,425 once invoked), scanned A, a copy of deepline-pre-research, MIT.

A pre-research guide for Deepline, a workflow service for gathering and enriching information from public, private, customer-management, social, and web sources. It helps plan the sources and expected credit cost before a workflow is built or run.

In plain words
What is it for?
Use it to discover relevant data sources, compare their coverage, estimate Deepline credit use, and prepare a research or enrichment workflow.
Why use it?
Research workflows can use incomplete sources or spend credits before their coverage is understood. This guide supports comparing providers and choosing a source plan first.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to discover relevant data sources, compare their coverage, estimate Deepline credit use, and prepare a research or enrichment workflow.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/getaero-io/gtm-eng-skills/deepline-research
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add getaero-io/gtm-eng-skills --skill deepline-research
Clone the repo
git clone --depth 1 https://github.com/getaero-io/gtm-eng-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for deepline-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-research/github.svg)](https://agentmods.dev/skills/getaero-io/gtm-eng-skills/deepline-research)
Your own site
<a href="https://agentmods.dev/skills/getaero-io/gtm-eng-skills/deepline-research"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-research/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for deepline-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/getaero-io/gtm-eng-skills/deepline-research"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 134 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,425 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00134 $0.05425
Opus 5.5 $0.00054 $0.02170
Sonnet 5.5 $0.00027 $0.01085
Haiku 4.5 $0.00013 $0.00543

Measured today against content hash ea13c5d00fad, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

deepline-research scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/evaluate_examples.py, scripts/evaluate_public_private_corpus.py, scripts/query_design.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s "$DEEPLINE_API_BASE_URL/api/v2/pre-research/plan" \
Origin

This is a copy

95% identical to deepline-pre-research — 331 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/deepline-research/SKILL.md · 344 lines

How it starts

The opening of the file, as written. The whole thing — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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, 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.

Read the full file on GitHub · 344 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed · -1 lines ea13c5d00fad
  2. yesterday Changed b179bbdb1a25
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  9. 15d ago Changed 409fec6d298c
  10. 26d ago First seen · 466 lines · 134 tokens per session scan A 01a7b0fbc111

Subscribe to this mod's changes

deepline-research is a skill published in the GitHub repository getaero-io/gtm-eng-skills (68 stars, last pushed today), licensed MIT. It adds 134 tokens to every session and 5,425 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to deepline-pre-research, differing in 331 lines, and is treated as a copy.

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