
incident-reporting
Construction safety incident reporting and analysis. Capture incidents, conduct investigations, track corrective actions, and analyze trends for prevention.
- Installs
- 117
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill incident-reporting- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads incident-reporting automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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)
Quick Start
# Initialize manager
manager = IncidentManager()
# Report near-miss
incident = manager.report_incident(
incident_type=IncidentType.NEAR_MISS,
category=IncidentCategory.STRUCK_BY,
date_time=datetime.now(),
location="Level 5, Grid C-4",
project_id="PRJ-001",
project_name="Office Tower",
description="Unsecured tool fell from scaffold, landed 2 feet from worker",
immediate_actions="Area cordoned off, toolbox talk conducted",
reported_by="Site Foreman"
)
# 5 Whys analysis
analysis = manager.five_whys_analysis(incident.id, [
"Tool fell from scaffold",
"Tool was not secured",
"No tool lanyard was used",
"Worker not trained on tool tethering",
"Tool tethering training not included in orientation"
])
# Assign corrective actions
manager.assign_corrective_action(
incident.id,
"Update orientation to include tool tethering training",
"Safety Manager",
due_days=14
)
manager.assign_corrective_action(
incident.id,
"Provide tool lanyards to all workers at height",
"Procurement",
due_days=7
)
# Get metrics
metrics = manager.get_incident_metrics()
print(f"Total Incidents: {metrics['total_incidents']}")
print(f"Near Miss Ratio: {metrics['near_miss_ratio']:.1f}")
# Generate report
print(manager.generate_incident_report(incident.id))
Requirements
pip install (no external dependencies)
FAQ
What does the incident-reporting skill do?
Construction safety incident reporting and analysis. Capture incidents, conduct investigations, track corrective actions, and analyze trends for prevention.
How do I install incident-reporting?
Run npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill incident-reporting in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/incident-reporting/ (personal) or .claude/skills/incident-reporting/ (project).
Which AI agents can use incident-reporting?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is incident-reporting safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains incident-reporting?
It is published by datadrivenconstruction in datadrivenconstruction/ddc_skills_for_ai_agents_in_construction. It has 117 recorded installs.
More from datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
warranty-tracker
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Track and manage construction warranties. Monitor expiration dates, claims, and manufacturer documentation.
cad-to-data
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Open the skill to read its SKILL.md instructions.
dwg-to-excel
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Open the skill to read its SKILL.md instructions.
drawing-analyzer
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Open the skill to read its SKILL.md instructions.
permit-tracking-automation
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Open the skill to read its SKILL.md instructions.
cost-estimation-resource
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Calculate construction costs using resource-based method. Estimate project costs from work items, physical resource norms, and current prices.
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available