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.
npx skills add getaero-io/gtm-eng-skills --skill deepline-researchgit clone --depth 1 https://github.com/getaero-io/gtm-eng-skillsWrote 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.
[](https://agentmods.dev/skills/getaero-io/gtm-eng-skills/deepline-research)<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.
<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>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.
| Model | Per session | Once 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 | $0.00543 |
Grade A, and why
deepline-research scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "$DEEPLINE_API_BASE_URL/api/v2/pre-research/plan" \ 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.
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
/last30daysat 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, ormissing provider to add.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/last30days-public-private-corpus.json 15 KB
- references/fanout-consolidation.md 7.1 KB
- references/last30days-gtm-corpus.md 12 KB
- references/query-design.md 4.0 KB
- references/source-map.md 27 KB
- scripts/evaluate_examples.py 20 KB runs code
- scripts/evaluate_public_private_corpus.py 5.8 KB runs code
- scripts/query_design.py 23 KB runs code
- skill-metadata.json 2.7 KB
- THIRD_PARTY_NOTICES.md 1.6 KB
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.
- today Changed · -1 lines ea13c5d00fad
- yesterday Changed b179bbdb1a25
- 2d ago Changed · -111 lines 1bfa7e41b38d
- 4d ago Changed · -3 lines 9b4aae0b759d
- 5d ago Changed · -1 lines 35713213a9eb
- 8d ago Changed 783dc03840a0
- 9d ago Changed ecb07c278fb4
- 14d ago Changed · -6 lines ef70dc3192bf
- 15d ago Changed 409fec6d298c
- 26d ago First seen · 466 lines · 134 tokens per session scan A 01a7b0fbc111
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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