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 datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill n8n-pto-pipelinegit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote 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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline/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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
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 →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium MCP Rug Pull · line 163 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 187 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium Excessive Agency · line 171 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00030 | $0.01442 |
| Opus 5.5 | $0.00012 | $0.00577 |
| Sonnet 5.5 | $0.00006 | $0.00288 |
| Haiku 4.5 | $0.00003 | $0.00144 |
Grade A, and why
n8n-pto-pipeline 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- n8n-pto-pipeline — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
n8n PTO-Foreman Pipeline
Business Case
Problem Statement
Daily work planning in construction involves:
- Manual task distribution from PTO (engineering) to field crews
- Paper-based or phone-based task assignment
- No systematic tracking of task completion
- Delayed reporting and status updates
Solution
Automated n8n pipeline connecting Google Sheets task lists with Telegram bots for real-time task distribution and status collection.
Business Value
- Real-time distribution - Tasks delivered automatically at 8:00 AM
- Digital tracking - All assignments and statuses in one table
- Mobile-first - Foremen use familiar Telegram interface
- No app installation - Works with any phone with Telegram
Technical Implementation
Architecture
┌─────────────────┐ ┌─────────────┐ ┌─────────────────┐
│ Google Sheets │───>│ n8n │───>│ Telegram Bot │
│ (Task List) │ │ Pipeline │ │ (To Foreman) │
└─────────────────┘ └─────────────┘ └─────────────────┘
▲ │ │
│ │ ▼
│ ┌──────┴──────┐ ┌───────────┐
└──────────────│ Status │<─────│ Foreman │
│ Update │ │ Response │
└─────────────┘ └───────────┘
n8n Pipeline Components
1. Morning Trigger (8:00 AM)
{
"nodes": [
{
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"parameters": {
"rule": {
"interval": [
{"field": "hours", "hoursInterval": 24}
]
},
"triggerTimes": {"item": [{"hour": 8, "minute": 0}]}
}
}
]
}
2. Get Tasks from Google Sheets
{
"name": "Get Today Tasks",
"type": "n8n-nodes-base.googleSheets",
"parameters": {
"operation": "read",
"sheetId": "YOUR_SHEET_ID",
"range": "Tasks!A:F",
"options": {}
}
}
What ships with it
2 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.
- 1mo ago First seen · 207 lines · 30 tokens per session scan A ab069e9d06ad
n8n-pto-pipeline is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (374 stars, last pushed 8d ago), licensed MIT. It adds 30 tokens to every session and 1,442 once invoked, about $0.0001 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 2026-09-03.
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