n8n-photo-report

n8n-photo-report is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 22 tokens per session (1,807 once invoked), scanned A, original, MIT.

An n8n workflow that collects construction photos, uses AI to analyse and categorise them, and creates reports. n8n is a tool for connecting automated steps between apps.

In plain words
What is it for?
Use it to receive photos from uploads or Dropbox, analyse and categorise them, generate reports, and distribute the results.
Why use it?
It reduces the manual work of sorting site photos, interpreting their contents, and assembling photo documentation.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to receive photos from uploads or Dropbox, analyse and categorise them, generate reports, and distribute the results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/n8n-photo-report
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 datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill n8n-photo-report
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/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-photo-report

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/n8n-photo-report"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/n8n-photo-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,807 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 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 Data Exfiltration · line 57
    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 184
    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.
How audits are shown
Origin original No closer match found 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 $0.00022 $0.01807
Opus 5.5 $0.00009 $0.00723
Sonnet 5.5 $0.00004 $0.00361
Haiku 4.5 $0.00002 $0.00181

Measured 1mo ago against content hash 4b83d93dc182, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

n8n-photo-report scanned grade A with 1 finding 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

response = requests.post(webhook_url, json=payload)
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

3_DDC_Insights/Automation-Workflows/n8n-photo-report/SKILL.md · 211 lines

How it starts

The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.

n8n Photo Report Automation

Business Case

Site photos require organization, analysis, and reporting. This workflow automates photo collection, AI analysis, and report generation.

Workflow Overview

[Photo Upload] → [AI Analysis] → [Categorization] → [Report Generation] → [Distribution]

n8n Workflow Configuration

1. Photo Input Triggers

{
  "nodes": [
    {
      "name": "Photo Webhook",
      "type": "n8n-nodes-base.webhook",
      "parameters": {
        "httpMethod": "POST",
        "path": "photo-upload",
        "options": {
          "binaryData": true
        }
      }
    },
    {
      "name": "Watch Dropbox Folder",
      "type": "n8n-nodes-base.dropbox",
      "parameters": {
        "operation": "listFolder",
        "path": "/SitePhotos/{{$today}}"
      }
    }
  ]
}

2. AI Image Analysis

{
  "name": "Analyze with Claude Vision",
  "type": "n8n-nodes-base.httpRequest",
  "parameters": {
    "method": "POST",
    "url": "https://api.anthropic.com/v1/messages",
    "headers": {
      "x-api-key": "={{$env.ANTHROPIC_API_KEY}}",
      "anthropic-version": "2023-06-01"
    },
    "body": {
      "model": "claude-3-5-sonnet-20241022",
      "max_tokens": 1024,
      "messages": [{
        "role": "user",
        "content": [
          {
            "type": "image",
            "source": {
              "type": "base64",
              "media_type": "image/jpeg",
              "data": "={{$binary.data.toString('base64')}}"
            }
          },
          {
            "type": "text",
            "text": "Analyze this construction site photo. Identify: 1) Work activity visible, 2) Approximate completion status, 3) Any safety concerns, 4) Weather conditions. Return JSON format."
          }
        ]
      }]
    }
  }
}

3. Categorize and Store

{
  "nodes": [
    {
      "name": "Parse AI Response",
      "type": "n8n-nodes-base.code",
      "parameters": {
        "jsCode": "const response = JSON.parse($json.content[0].text);\n\nreturn [{\n  json: {\n    filename: $('Photo Webhook').first().json.filename,\n    timestamp: new Date().toISOString(),\n    activity: response.work_activity,\n    completion: response.completion_status,\n    safety_issues: response.safety_concerns,\n    weather: response.weather,\n    category: response.work_activity.includes('concrete') ? 'CONCRETE' :\n              response.work_activity.includes('steel') ? 'STEEL' :\n              response.work_activity.includes('mep') ? 'MEP' : 'GENERAL'\n  }\n}];"
      }
    },
    {
      "name": "Store in Airtable",
      "type": "n8n-nodes-base.airtable",
      "parameters": {
        "operation": "create",
        "table": "Site Photos",
        "fields": {
          "Filename": "={{$json.filename}}",
          "Date": "={{$json.timestamp}}",
          "Activity": "={{$json.activity}}",
          "Category": "={{$json.category}}",
          "Completion": "={{$json.completion}}",
          "Safety Issues": "={{$json.safety_issues}}"
        }
      }
    }
  ]
}

Read the full file on GitHub · 211 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. 1mo ago First seen · 211 lines · 22 tokens per session scan A 4b83d93dc182

Subscribe to this mod's changes

n8n-photo-report 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 22 tokens to every session and 1,807 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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