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 neurawork-git/n8n-autopilot --skill stack-intakegit clone --depth 1 https://github.com/neurawork-git/n8n-autopilotWrote 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/neurawork-git/n8n-autopilot/stack-intake)<a href="https://agentmods.dev/skills/neurawork-git/n8n-autopilot/stack-intake"><img src="https://agentmods.dev/badge/skills/neurawork-git/n8n-autopilot/stack-intake.svg" alt="Measured on agentmods" 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 | $0.00146 | $0.01705 |
| Opus 5 | $0.00073 | $0.00852 |
| Sonnet 5 | $0.00029 | $0.00341 |
| Haiku 4.5 | $0.00015 | $0.00170 |
Grade A, and why
stack-intake 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 7d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stack Intake — guided PRP interview
Most people can describe what they want ("when a lead comes in, research it and tell me if it's
worth chasing") but not how to wire it in n8n. This skill bridges that: a friendly, classic
interview that draws out inputs, outputs, and concrete examples, then writes a PRP-style use-case
description that /n8n-autopilot:build-stack-v2 can decompose into a
real workflow stack. You do the n8n thinking; the user only describes their world.
Principles
- One topic at a time. Ask, listen, follow up. Do NOT dump all questions at once — this is an interview, not a form. Adapt: skip what's already obvious, dig where it's vague.
- Speak their language, not n8n's. Never ask "what trigger type?" Ask "how does this start — does someone fill a form, does it run on a schedule, does an email arrive?" Map their answer to n8n concepts yourself.
- Always anchor on a concrete example. Abstract specs decompose badly. Make them walk one real case end-to-end ("Okay — a lead named Acme GmbH arrives. What happens first? Then?").
- You hold the n8n knowledge. Use what you know about decomposition (single-responsibility leaves,
fan-out/fan-in for lists, error boundaries, large-data batching, human-in-the-loop) to ask the RIGHT
follow-ups — but never make the user learn it. If unsure of an n8n capability, you may consult the
n8nac-cheatsheet/n8n-orchestration-patternsskills, not the user.
The interview (cover every section; adapt the wording)
Use AskUserQuestion for the closed choices (trigger family, volume, error policy, HITL) so the user
just clicks; use plain conversation for the worked example and free descriptions.
- The goal in one sentence. "In one line, what should this automation achieve?" (Seed from
$ARGUMENTSif given.) - How it starts (input / trigger). A schedule? A webhook/form someone submits? An incoming email? A chat message? A manual run? — and what data comes with it. Ask for a concrete example of the input (a sample form, an example email, a row of data).
- What "done" looks like (output). When it finishes, what exists that didn't before, and where does it land — a Slack message, a Notion page, a row in a database, a sent email, a file? Ask for an example of the finished result.
- One worked example, end-to-end. Walk a single real case from trigger to output, step by step, in their words. This is the most important answer — capture it verbatim.
- External systems & accounts. Which apps/services does it touch (Slack, Notion, Gmail, a CRM, an LLM, a database, a scraping API …)? Each one likely needs a credential — note them, don't set them up.
- Volume & batching. One thing at a time, or a list/batch? Roughly how many per run? (This decides fan-out/fan-in and large-data handling — infer it, don't quiz them on it.)
- When something goes wrong. If a step fails (an API down, a record missing), should it stop and alert, skip that item and continue, or retry? (Maps to error boundaries.)
- Human checkpoints. Any step where a person must review/approve before it proceeds?
- Reuse & scope. Is any part of this already done elsewhere, or useful on its own? Anything explicitly out of scope for now?
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.
- 7d ago First seen · 124 lines · 146 tokens per session scan A 8042e4565b38
stack-intake is a skill published in the GitHub repository neurawork-git/n8n-autopilot (18 stars, last pushed 1mo ago), licensed MIT. It adds 146 tokens to every session and 1,705 once invoked, about $0.0007 per session on Opus 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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