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-enginegit 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-engine)<a href="https://agentmods.dev/skills/getaero-io/gtm-eng-skills/deepline-engine"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-engine/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-engine"><img src="https://agentmods.dev/badge/skills/getaero-io/gtm-eng-skills/deepline-engine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.03355 |
| Opus 5.5 | $0.00017 | $0.01342 |
| Sonnet 5.5 | $0.00009 | $0.00671 |
| Haiku 4.5 | $0.00004 | $0.00335 |
Grade A, and why
deepline-engine scanned grade A with 0 findings 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 9d ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deepline Engine
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.
Build an engine from the state machine the user defines.
An engine is an orchestrator Deepline Play that accepts an input, determines its state, calls the transition Play for that state, and produces a new state. The durable data plane lives in Customer DB: every state has an input table and an output table. The output of one transition can become the input to the next state.
The user owns the states, transition rules, transformations, and terminal behavior. This skill owns the reusable structure for turning those decisions into a replay-safe Play. Infer routine names, schemas, advancement behavior, and implementation details from the request and repository conventions. Ask only when missing information would force the engine to invent substantive business policy or create an unsafe side effect. Do not import an outbound workflow, GTM recipes, provider choices, or domain-specific policy unless the user asks for them.
Core model
For each state, define:
| Part | Meaning |
|---|---|
| Input table | Rows waiting to be handled in this state |
| State decision | The user-defined rule that establishes the row's current state |
| Transition Play | The child Play that performs the transformation for this state |
| Output table | The input, result, transition status, and next state |
| Next input | The row admitted to the next state's input table, unless the transition is terminal |
What ships with it
3 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.
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.
- 9d ago Changed · -1 lines 32542d963115
- 10d ago Changed d68dd81a4e8b
- 11d ago Changed ee6d8daf3d3e
- 13d ago Changed · +15 lines 44a5684ad6ea
- 18d ago Changed 34332ff7b0f6
- 23d ago Changed · +8 lines 10a3c737a4b5
- 1mo ago First seen · 115 lines · 43 tokens per session scan A 186298010ea1
deepline-engine is a skill published in the GitHub repository getaero-io/gtm-eng-skills (71 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 3,355 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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