n8n-pto-pipeline

n8n-pto-pipeline is a skill for Claude Code, Codex from jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction. It costs 30 tokens per session (1,442 once invoked), scanned A, a copy of n8n-pto-pipeline, MIT.

An n8n workflow that reads daily tasks from Google Sheets and sends them to foremen through a Telegram bot. Foremen can report task status back through the same chat.

In plain words
What is it for?
It is for assigning daily construction work and collecting progress reports. It connects an engineering or planning team with field crews using Telegram.
Why use it?
It replaces paper, phone calls, and scattered messages with scheduled task delivery and a shared status record. Managers can see assignments and completion updates in one table.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: positional $N argument; built for openclaw.

Good fit It is for assigning daily construction work and collecting progress reports. It connects an engineering or planning team with field crews using Telegram.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline
Install

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.

Any agent
npx skills add jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill n8n-pto-pipeline
Clone the repo
git clone --depth 1 https://github.com/jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for n8n-pto-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline/github.svg)](https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline)
Your own site
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/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.

agentmods 80×15 button for n8n-pto-pipeline

Your own site · 80×15
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/n8n-pto-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,442 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00030 $0.01442
Opus 5 $0.00015 $0.00721
Sonnet 5 $0.00006 $0.00288
Haiku 4.5 $0.00003 $0.00144

Measured 9d ago against content hash ab069e9d06ad, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 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.

Origin

This is a copy

100% identical to n8n-pto-pipeline — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

3_DDC_Insights/Automation-Workflows/n8n-pto-pipeline/SKILL.md · 207 lines

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": {}
  }
}

Read the full file on GitHub · 207 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 207 lines · 30 tokens per session scan A ab069e9d06ad

Subscribe to this mod's changes

n8n-pto-pipeline is a skill published in the GitHub repository jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction (2 stars, last pushed 6mo ago), licensed MIT. It adds 30 tokens to every session and 1,442 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to n8n-pto-pipeline, differing in 0 lines, and is treated as a copy.

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