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 naveedharri/benai-skills --skill n8ngit clone --depth 1 https://github.com/naveedharri/benai-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/naveedharri/benai-skills/n8n)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/n8n"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/n8n/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/naveedharri/benai-skills/n8n"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/n8n.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 21 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 23 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 36 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 168 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 173 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Data Exfiltration · line 173 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 315 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.02469 |
| Opus 5 | $0.00073 | $0.01234 |
| Sonnet 5 | $0.00029 | $0.00494 |
| Haiku 4.5 | $0.00015 | $0.00247 |
Grade B, and why
n8n scanned grade B with 2 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 6d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
export $(cat .env | grep -v '^#' | xargs) && curl -s -X POST "${N8N_API_URL}/api/v1/workflows" -H "X-N8N-API-KEY: ${N8N_API_KEY}" -H "Content-Type: application/json" -d "$(cat <<'EOF' Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
export $(cat .env | grep -v '^#' | xargs) && curl -s "${N8N_API_URL}/api/v1/ENDPOINT" -H "X-N8N-API-KEY: ${N8N_API_KEY}" | jq . How it starts
The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
n8n Automation Skill
Build, test, and deploy n8n workflows via REST API with incremental testing.
When This Skill Loads (DO THIS IMMEDIATELY)
Step 1: Read Reference Files
Use the Read tool to read these files NOW:
1. Read references/pitfalls.md (critical command format rules)
2. Read references/build-process.md (step-by-step build workflow)
Step 2: Check .env Configuration
Read the .env file in the working directory to verify:
- N8N_API_URL is set
- N8N_API_KEY is set
- N8N_CREDENTIALS_TEMPLATE_URL is set
If any values are missing, ask the user for ALL of them in a single prompt.
Step 3: Create a Todo List
After understanding the user's request, create a todo list using TaskCreate.
SKILL LOADS -> READ FILES IMMEDIATELY -> CHECK .env -> THEN respond to user
Critical Rules
1. USE THE TOOLS THE USER SPECIFIES
If the user mentions specific tools, nodes, or services - YOU MUST USE THEM.
User says "use Apify" -> You use Apify node
User says "use OpenAI" -> You use OpenAI Chat Model
Never substitute, skip, or defer user-requested tools.
2. NEVER Simplify When Hitting Errors - FIX THE ISSUE
Loop node has error -> Debug why -> Fix the loop configuration
Don't switch to "simpler" approaches. Keep the correct architecture.
3. Only Use Nodes From Credentials Template
Never add nodes requiring authentication unless:
- The node exists in the user's credentials template, OR
- The user explicitly asks to use that specific tool/service
4. Only Use Node Types/Versions That Are INSTALLED
Always copy type AND typeVersion from the credentials template.
5. One Node at a Time
ADD NODE -> TEST -> ADD NEXT NODE -> TEST -> REPEAT
NEVER add 2+ nodes without testing between them.
6. NEVER Delete or Deactivate Workflows - UPDATE Instead
Node fails -> Debug the error -> Update the same workflow (PUT)
One workflow ID for the entire build process.
What ships with it
14 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.
- assets/.gitkeep 0 B
- references/ai-nodes.md 5.7 KB
- references/api-reference.md 6.0 KB
- references/build-process.md 4.8 KB
- references/credentials.md 5.0 KB
- references/data-nodes.md 14 KB
- references/expressions.md 5.8 KB
- references/javascript.md 8.2 KB
- references/node-config.md 5.0 KB
- references/pitfalls.md 12 KB
- references/python.md 3.7 KB
- references/transform-nodes.md 19 KB
- references/triggers.md 8.8 KB
- scripts/.gitkeep 0 B
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.
- 6d ago First seen · 354 lines · 146 tokens per session scan B f912f92fcdcc
n8n is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed today), licensed MIT. It adds 146 tokens to every session and 2,469 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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