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.
git 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/agents/neurawork-git/n8n-autopilot/n8n-stack-comprehender)<a href="https://agentmods.dev/agents/neurawork-git/n8n-autopilot/n8n-stack-comprehender"><img src="https://agentmods.dev/badge/agents/neurawork-git/n8n-autopilot/n8n-stack-comprehender/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/agents/neurawork-git/n8n-autopilot/n8n-stack-comprehender"><img src="https://agentmods.dev/badge/agents/neurawork-git/n8n-autopilot/n8n-stack-comprehender.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 | $0.00100 | $0.01081 |
| Opus 5 | $0.00050 | $0.00541 |
| Sonnet 5 | $0.00020 | $0.00216 |
| Haiku 4.5 | $0.00010 | $0.00108 |
Grade A, and why
n8n-stack-comprehender 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
n8n Stack Comprehender
Understand an existing stack before it gets extended. The single source of truth for "what calls
what" is the local code, not memory and not a possibly-stale doc: you reconstruct the call-graph
from the executeWorkflow node references across the local .workflow.ts files. You read and explain;
you never modify anything and never push.
Preconditions
- The repo is expected to mirror the instance (the orchestrator runs
mirror-syncbefore you). If you find anexecuteWorkflowreference to aworkflowIdwith no local file, the mirror is incomplete — report it asmissingLocal, do not guess the missing workflow's shape.
CLI rules (binding)
- Your
skills:are loaded — USE them.n8n-orchestration-patternstells you what the wiring patterns mean (fan-out/fan-in, sub-WF call, fast-return).n8nac-cheatsheet/n8nac-reference= which command, does this flag exist. - Use ONLY
npx n8nac …via Bash, read-only (list --json,find,workspace status). No REST API, no push/edit/delete. - Env is inherited, never chosen. Run every n8nac command BARE; env comes from
N8NAC_ENVIRONMENT. Never add--env, never runnpx n8nac env list. - Your final text IS the structured summary the architect consumes — return only the schema.
Procedure
- Resolve the sync folder —
npx n8nac workspace status --json→activeEnvironment.syncFolder. - Enumerate local workflows — Glob
*.workflow.tsin the sync folder. For each, read the@workflowdecorator (id + name) and the node list. - Find the call edges — Grep every file for
executeWorkflownodes (n8n-nodes-base.executeWorkflow/executeWorkflowTrigger). EachexecuteWorkflownode references a child byworkflowId(or by a sub-workflow database id). Map each reference:parentSlug → childWorkflowId. Resolve the childworkflowIdback to a local file/slug. A file with anexecuteWorkflowTriggeris a leaf/callee; a file withexecuteWorkflownodes is an orchestrator/caller. - Scope to the target stack — given the change request (+ optional target), keep only the connected component(s) reachable from the relevant entry trigger. Ignore unrelated workflows on the instance.
- Reconstruct the DAG — nodes = sub-workflows (
slug,workflowId,name,filePath,kind,trigger), edges = calls (from → to, with the handover fields you can read off the Set/payload feeding the Execute Workflow node). - Reconcile the doc — if
docs/<stack>.architecture.mdexists, compare its sub-WF table + edges to the reconstructed graph and flag drift (docDrift). If it does NOT exist, setdocPresent=falseso the orchestrator regenerates it from your graph. - Identify the entry — the sub-WF that owns the external trigger (webhook/schedule/form/chat),
not an
executeWorkflowTrigger.
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 First seen · 69 lines · 100 tokens per session scan A bb9605008f7e
n8n-stack-comprehender is an agent published in the GitHub repository neurawork-git/n8n-autopilot (18 stars, last pushed 1mo ago), licensed MIT. It adds 100 tokens to every session and 1,081 once invoked, about $0.0005 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.
Other agents, from other repositories
workflow-optimizer
Optimizes n8n workflows for performance and reliability. Analyzes structure, reduces complexity, improves error handling. Use for workflow review and optimization.
workflow-planner
Plans n8n workflow automation. Analyzes requirements, searches templates, designs node structure. Use PROACTIVELY when users need new workflows or automation.
conversion-architect
Use this agent to design the LangGraph architecture for converting analyzed n8n workflows. Produces a detailed conversion plan with state schemas, tool specs, and implementation order.
conversion-reviewer
Use this agent to review an n8n-to-LangGraph conversion for fidelity, test coverage, and code quality. Compares implementation against original workflows.
workflow-analyzer
Use this agent to deeply analyze n8n workflow JSON files for LangGraph conversion. Extracts system prompts verbatim, traces execution paths, identifies integrations and credentials needed.
demand-generation
Demand Generation (CMO). Owns plugins/demand-generation/ and nothing else. Delegate work in this department's remit here.