incident-reporting

incident-reporting is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 27 tokens per session (3,524 once invoked), scanned A, original, MIT.

A construction safety incident-reporting workflow for recording near-misses, injuries, property damage, and environmental events.

In plain words
What is it for?
Use it to capture incidents, investigate root causes, assign corrective actions, and review safety trends.
Why use it?
It preserves investigation details and keeps corrective actions visible so teams can address causes and prevent repeat incidents.

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 capture incidents, investigate root causes, assign corrective actions, and review safety trends.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/incident-reporting
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 incident-reporting
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 incident-reporting

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/incident-reporting"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/incident-reporting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,524 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
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 $0.00027 $0.03524
Opus 5.5 $0.00011 $0.01410
Sonnet 5.5 $0.00005 $0.00705
Haiku 4.5 $0.00003 $0.00352

Measured 1mo ago against content hash 04306f07b487, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

incident-reporting 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

3_DDC_Insights/Safety-Quality/incident-reporting/SKILL.md · 464 lines

How it starts

The opening of the file, as written. The whole thing — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Incident Reporting System

Overview

Comprehensive incident reporting system for construction safety. Capture near-misses, injuries, and property damage. Conduct root cause analysis and track corrective actions to prevent recurrence.

"Near-miss reporting prevents 90% of future serious incidents" — DDC Community

Incident Pyramid

                    △
                   /│\        Fatality (1)
                  / │ \
                 /  │  \      Serious Injury (10)
                /   │   \
               /    │    \    Minor Injury (30)
              /     │     \
             /      │      \  Near Miss (300)
            /       │       \
           /        │        \ Unsafe Acts (3000)
          ──────────┴──────────

Technical Implementation

from dataclasses import dataclass, field
from typing import List, Dict, Optional
from enum import Enum
from datetime import datetime, timedelta
import json

class IncidentType(Enum):
    NEAR_MISS = "near_miss"
    FIRST_AID = "first_aid"
    MEDICAL_TREATMENT = "medical_treatment"
    LOST_TIME = "lost_time"
    FATALITY = "fatality"
    PROPERTY_DAMAGE = "property_damage"
    ENVIRONMENTAL = "environmental"

class IncidentCategory(Enum):
    FALL = "fall"
    STRUCK_BY = "struck_by"
    CAUGHT_IN = "caught_in"
    ELECTROCUTION = "electrocution"
    VEHICLE = "vehicle"
    MATERIAL_HANDLING = "material_handling"
    TOOL_EQUIPMENT = "tool_equipment"
    SLIP_TRIP = "slip_trip"
    FIRE = "fire"
    CHEMICAL = "chemical"
    OTHER = "other"

class InvestigationStatus(Enum):
    REPORTED = "reported"
    UNDER_INVESTIGATION = "under_investigation"
    ROOT_CAUSE_IDENTIFIED = "root_cause_identified"
    CORRECTIVE_ACTIONS_ASSIGNED = "corrective_actions_assigned"
    IN_REMEDIATION = "in_remediation"
    CLOSED = "closed"

@dataclass
class Person:
    name: str
    company: str
    role: str
    contact: str
    years_experience: int = 0

@dataclass
class CorrectiveAction:
    id: str
    description: str
    assigned_to: str
    due_date: datetime
    status: str = "open"
    completed_date: Optional[datetime] = None
    verification_notes: str = ""

@dataclass
class Incident:
    id: str
    incident_type: IncidentType
    category: IncidentCategory
    date_time: datetime
    location: str
    project_id: str
    project_name: str

    # Description
    description: str
    immediate_actions: str

    # People involved
    injured_person: Optional[Person] = None
    witnesses: List[Person] = field(default_factory=list)
    reported_by: str = ""

    # Investigation
    status: InvestigationStatus = InvestigationStatus.REPORTED
    root_causes: List[str] = field(default_factory=list)
    contributing_factors: List[str] = field(default_factory=list)
    corrective_actions: List[CorrectiveAction] = field(default_factory=list)

    # Documentation
    photos: List[str] = field(default_factory=list)
    weather_conditions: str = ""
    equipment_involved: List[str] = field(default_factory=list)

    # Metrics
    days_lost: int = 0
    property_damage_cost: float = 0.0
    osha_recordable: bool = False

class IncidentManager:
    """Manage construction incident reporting and investigation."""

    # 5 Whys root cause categories
    ROOT_CAUSE_CATEGORIES = [
        "Training/Competency",
        "Procedures/Work Instructions",
        "Equipment/Tools",
        "Supervision",
        "Communication",
        "Housekeeping",
        "PPE",
        "Work Environment",
        "Physical/Mental State",
        "Management System"
    ]

    def __init__(self):
        self.incidents: Dict[str, Incident] = {}
        self.corrective_actions: Dict[str, CorrectiveAction] = {}

    def report_incident(self, incident_type: IncidentType,
                       category: IncidentCategory,
                       date_time: datetime,
                       location: str,
                       project_id: str,
                       project_name: str,
                       description: str,
                       immediate_actions: str,
                       reported_by: str,
                       injured_person: Dict = None) -> Incident:
        """Report new incident."""
        incident_id = f"INC-{datetime.now().strftime('%Y%m%d%H%M%S')}"

        injured = None
        if injured_person:
            injured = Person(**injured_person)

        incident = Incident(
            id=incident_id,
            incident_type=incident_type,
            category=category,
            date_time=date_time,
            location=location,
            project_id=project_id,
            project_name=project_name,
            description=description,
            immediate_actions=immediate_actions,
            reported_by=reported_by,
            injured_person=injured
        )

        # Auto-flag OSHA recordable
        if incident_type in [IncidentType.MEDICAL_TREATMENT,
                            IncidentType.LOST_TIME,
                            IncidentType.FATALITY]:
            incident.osha_recordable = True

        self.incidents[incident_id] = incident
        return incident

    def add_witness(self, incident_id: str, witness: Dict) -> Incident:
        """Add witness to incident."""
        if incident_id not in self.incidents:
            raise ValueError(f"Incident {incident_id} not found")

        self.incidents[incident_id].witnesses.append(Person(**witness))
        return self.incidents[incident_id]

    def conduct_investigation(self, incident_id: str,
                             root_causes: List[str],
                             contributing_factors: List[str]) -> Incident:
        """Record investigation findings."""
        if incident_id not in self.incidents:
            raise ValueError(f"Incident {incident_id} not found")

        incident = self.incidents[incident_id]
        incident.root_causes = root_causes
        incident.contributing_factors = contributing_factors
        incident.status = InvestigationStatus.ROOT_CAUSE_IDENTIFIED
        return incident

    def five_whys_analysis(self, incident_id: str, whys: List[str]) -> Dict:
        """Conduct 5 Whys analysis."""
        if incident_id not in self.incidents:
            raise ValueError(f"Incident {incident_id} not found")

        analysis = {
            "incident_id": incident_id,
            "analysis_date": datetime.now().isoformat(),
            "whys": []
        }

        for i, why in enumerate(whys):
            analysis["whys"].append({
                "level": i + 1,
                "question": f"Why #{i+1}?",
                "answer": why
            })

        # The last "why" is typically the root cause
        if whys:
            self.incidents[incident_id].root_causes.append(whys[-1])

        return analysis

    def assign_corrective_action(self, incident_id: str,
                                description: str,
                                assigned_to: str,
                                due_days: int = 7) -> CorrectiveAction:
        """Assign corrective action."""
        if incident_id not in self.incidents:
            raise ValueError(f"Incident {incident_id} not found")

        action_id = f"CA-{datetime.now().strftime('%Y%m%d%H%M%S')}"
        action = CorrectiveAction(
            id=action_id,
            description=description,
            assigned_to=assigned_to,
            due_date=datetime.now() + timedelta(days=due_days)
        )

        self.incidents[incident_id].corrective_actions.append(action)
        self.incidents[incident_id].status = InvestigationStatus.CORRECTIVE_ACTIONS_ASSIGNED
        self.corrective_actions[action_id] = action
        return action

    def complete_corrective_action(self, action_id: str,
                                   verification_notes: str) -> CorrectiveAction:
        """Mark corrective action complete."""
        if action_id not in self.corrective_actions:
            raise ValueError(f"Corrective action {action_id} not found")

        action = self.corrective_actions[action_id]
        action.status = "completed"
        action.completed_date = datetime.now()
        action.verification_notes = verification_notes
        return action

    def get_incident_metrics(self, project_id: str = None,
                            start_date: datetime = None,
                            end_date: datetime = None) -> Dict:
        """Calculate incident metrics."""
        incidents = list(self.incidents.values())

        if project_id:
            incidents = [i for i in incidents if i.project_id == project_id]
        if start_date:
            incidents = [i for i in incidents if i.date_time >= start_date]
        if end_date:
            incidents = [i for i in incidents if i.date_time <= end_date]

        # Calculate metrics
        total = len(incidents)
        near_misses = len([i for i in incidents if i.incident_type == IncidentType.NEAR_MISS])
        first_aid = len([i for i in incidents if i.incident_type == IncidentType.FIRST_AID])
        recordables = len([i for i in incidents if i.osha_recordable])
        lost_time = len([i for i in incidents if i.incident_type == IncidentType.LOST_TIME])
        total_days_lost = sum(i.days_lost for i in incidents)

        # Category breakdown
        by_category = {}
        for cat in IncidentCategory:
            count = len([i for i in incidents if i.category == cat])
            if count > 0:
                by_category[cat.value] = count

        return {
            "total_incidents": total,
            "near_misses": near_misses,
            "first_aid_cases": first_aid,
            "osha_recordables": recordables,
            "lost_time_incidents": lost_time,
            "total_days_lost": total_days_lost,
            "by_category": by_category,
            "near_miss_ratio": near_misses / recordables if recordables else 0
        }

    def calculate_trir(self, hours_worked: int, project_id: str = None) -> float:
        """Calculate Total Recordable Incident Rate."""
        incidents = list(self.incidents.values())
        if project_id:
            incidents = [i for i in incidents if i.project_id == project_id]

        recordables = len([i for i in incidents if i.osha_recordable])

        if hours_worked == 0:
            return 0

        # TRIR = (Recordables × 200,000) / Hours Worked
        return (recordables * 200000) / hours_worked

    def calculate_dart(self, hours_worked: int, project_id: str = None) -> float:
        """Calculate Days Away, Restricted, or Transferred rate."""
        incidents = list(self.incidents.values())
        if project_id:
            incidents = [i for i in incidents if i.project_id == project_id]

        dart_cases = len([i for i in incidents
                         if i.incident_type in [IncidentType.LOST_TIME]])

        if hours_worked == 0:
            return 0

        return (dart_cases * 200000) / hours_worked

    def get_trend_analysis(self, months: int = 6) -> List[Dict]:
        """Analyze incident trends over time."""
        trends = []
        now = datetime.now()

        for i in range(months):
            month_start = datetime(now.year, now.month - i, 1) if now.month > i else datetime(now.year - 1, 12 - (i - now.month), 1)
            month_end = month_start.replace(day=28) + timedelta(days=4)
            month_end = month_end - timedelta(days=month_end.day)

            month_incidents = [inc for inc in self.incidents.values()
                             if month_start <= inc.date_time <= month_end]

            trends.append({
                "month": month_start.strftime("%Y-%m"),
                "total": len(month_incidents),
                "near_misses": len([i for i in month_incidents if i.incident_type == IncidentType.NEAR_MISS]),
                "recordables": len([i for i in month_incidents if i.osha_recordable])
            })

        return list(reversed(trends))

    def generate_incident_report(self, incident_id: str) -> str:
        """Generate detailed incident report."""
        if incident_id not in self.incidents:
            return "Incident not found"

        inc = self.incidents[incident_id]

        lines = [
            f"# Incident Report",
            f"",
            f"**Incident ID:** {inc.id}",
            f"**Type:** {inc.incident_type.value}",
            f"**Category:** {inc.category.value}",
            f"**Date/Time:** {inc.date_time.strftime('%Y-%m-%d %H:%M')}",
            f"**Location:** {inc.location}",
            f"**Project:** {inc.project_name}",
            f"**Status:** {inc.status.value}",
            f"**OSHA Recordable:** {'Yes' if inc.osha_recordable else 'No'}",
            f"",
            f"## Description",
            f"{inc.description}",
            f"",
            f"## Immediate Actions Taken",
            f"{inc.immediate_actions}",
            f"",
        ]

        if inc.injured_person:
            lines.extend([
                f"## Injured Person",
                f"- Name: {inc.injured_person.name}",
                f"- Company: {inc.injured_person.company}",
                f"- Role: {inc.injured_person.role}",
                f"- Experience: {inc.injured_person.years_experience} years",
                f""
            ])

        if inc.root_causes:
            lines.extend([
                f"## Root Causes",
                *[f"- {rc}" for rc in inc.root_causes],
                f""
            ])

        if inc.corrective_actions:
            lines.extend([
                f"## Corrective Actions",
                f"| Action | Assigned To | Due | Status |",
                f"|--------|-------------|-----|--------|"
            ])
            for ca in inc.corrective_actions:
                lines.append(f"| {ca.description} | {ca.assigned_to} | {ca.due_date.strftime('%Y-%m-%d')} | {ca.status} |")

        return "\n".join(lines)

Read the full file on GitHub · 464 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 · 464 lines · 27 tokens per session scan A 04306f07b487

Subscribe to this mod's changes

incident-reporting 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 27 tokens to every session and 3,524 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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