daily-progress-report

daily-progress-report is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 27 tokens per session (2,477 once invoked), scanned A, original, MIT.

A tool for producing daily construction progress reports from structured site data. The reports can cover completed work, labour hours, equipment use, weather, and work status.

In plain words
What is it for?
Use it to record construction activities, quantities, crews, hours, equipment, weather conditions, delays, and notes in daily reports.
Why use it?
It reduces manual report writing and helps avoid inconsistent, late, or incomplete daily submissions.

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 record construction activities, quantities, crews, hours, equipment, weather conditions, delays, and notes in daily reports.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/daily-progress-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 daily-progress-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 daily-progress-report

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/daily-progress-report"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/daily-progress-report.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 2,477 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 $0.02477
Opus 5 $0.00014 $0.01239
Sonnet 5 $0.00005 $0.00495
Haiku 4.5 $0.00003 $0.00248

Measured 12d ago against content hash 5177524190bf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

daily-progress-report 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 12d 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:

1_DDC_Toolkit/Analytics/daily-progress-report/SKILL.md · 387 lines

How it starts

The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Daily Progress Report Generator

Business Case

Problem Statement

Site managers spend hours creating daily reports:

  • Manual data collection
  • Inconsistent formats
  • Delayed submissions
  • Missing information

Solution

Automated daily progress report generation from structured site data inputs.

Technical Implementation

import pandas as pd
from datetime import datetime, date
from typing import Dict, Any, List
from dataclasses import dataclass
from enum import Enum


class WeatherCondition(Enum):
    CLEAR = "clear"
    CLOUDY = "cloudy"
    RAIN = "rain"
    SNOW = "snow"
    WIND = "wind"
    EXTREME = "extreme"


class WorkStatus(Enum):
    COMPLETED = "completed"
    IN_PROGRESS = "in_progress"
    DELAYED = "delayed"
    NOT_STARTED = "not_started"


@dataclass
class WorkActivity:
    activity_id: str
    description: str
    location: str
    planned_qty: float
    actual_qty: float
    unit: str
    status: WorkStatus
    crew_size: int
    hours_worked: float
    notes: str = ""


@dataclass
class LaborEntry:
    trade: str
    company: str
    workers: int
    hours: float
    overtime_hours: float = 0


@dataclass
class EquipmentEntry:
    equipment_type: str
    equipment_id: str
    hours_used: float
    status: str  # active, idle, maintenance
    operator: str = ""


@dataclass
class DailyReport:
    report_date: date
    project_name: str
    project_number: str
    weather: WeatherCondition
    temperature_high: float
    temperature_low: float
    work_activities: List[WorkActivity]
    labor: List[LaborEntry]
    equipment: List[EquipmentEntry]
    delays: List[str]
    safety_incidents: int
    visitors: List[str]
    deliveries: List[str]
    prepared_by: str


class DailyProgressReporter:
    """Generate daily progress reports."""

    def __init__(self, project_name: str, project_number: str):
        self.project_name = project_name
        self.project_number = project_number

    def create_report(self,
                      report_date: date,
                      weather: WeatherCondition,
                      temp_high: float,
                      temp_low: float,
                      prepared_by: str) -> DailyReport:
        """Create new daily report."""

        return DailyReport(
            report_date=report_date,
            project_name=self.project_name,
            project_number=self.project_number,
            weather=weather,
            temperature_high=temp_high,
            temperature_low=temp_low,
            work_activities=[],
            labor=[],
            equipment=[],
            delays=[],
            safety_incidents=0,
            visitors=[],
            deliveries=[],
            prepared_by=prepared_by
        )

    def add_work_activity(self,
                          report: DailyReport,
                          activity_id: str,
                          description: str,
                          location: str,
                          planned_qty: float,
                          actual_qty: float,
                          unit: str,
                          crew_size: int,
                          hours_worked: float,
                          notes: str = ""):
        """Add work activity to report."""

        # Determine status
        if actual_qty >= planned_qty:
            status = WorkStatus.COMPLETED
        elif actual_qty > 0:
            status = WorkStatus.IN_PROGRESS
        elif actual_qty == 0 and planned_qty > 0:
            status = WorkStatus.DELAYED
        else:
            status = WorkStatus.NOT_STARTED

        activity = WorkActivity(
            activity_id=activity_id,
            description=description,
            location=location,
            planned_qty=planned_qty,
            actual_qty=actual_qty,
            unit=unit,
            status=status,
            crew_size=crew_size,
            hours_worked=hours_worked,
            notes=notes
        )

        report.work_activities.append(activity)

    def add_labor(self,
                  report: DailyReport,
                  trade: str,
                  company: str,
                  workers: int,
                  hours: float,
                  overtime_hours: float = 0):
        """Add labor entry."""

        report.labor.append(LaborEntry(
            trade=trade,
            company=company,
            workers=workers,
            hours=hours,
            overtime_hours=overtime_hours
        ))

    def add_equipment(self,
                      report: DailyReport,
                      equipment_type: str,
                      equipment_id: str,
                      hours_used: float,
                      status: str,
                      operator: str = ""):
        """Add equipment entry."""

        report.equipment.append(EquipmentEntry(
            equipment_type=equipment_type,
            equipment_id=equipment_id,
            hours_used=hours_used,
            status=status,
            operator=operator
        ))

    def calculate_summary(self, report: DailyReport) -> Dict[str, Any]:
        """Calculate report summary metrics."""

        total_workers = sum(l.workers for l in report.labor)
        total_manhours = sum(l.workers * l.hours for l in report.labor)
        total_overtime = sum(l.workers * l.overtime_hours for l in report.labor)
        equipment_hours = sum(e.hours_used for e in report.equipment)

        completed = sum(1 for a in report.work_activities if a.status == WorkStatus.COMPLETED)
        in_progress = sum(1 for a in report.work_activities if a.status == WorkStatus.IN_PROGRESS)
        delayed = sum(1 for a in report.work_activities if a.status == WorkStatus.DELAYED)

        return {
            'total_workers': total_workers,
            'total_manhours': round(total_manhours, 1),
            'total_overtime': round(total_overtime, 1),
            'equipment_hours': round(equipment_hours, 1),
            'activities_completed': completed,
            'activities_in_progress': in_progress,
            'activities_delayed': delayed,
            'safety_incidents': report.safety_incidents,
            'deliveries_count': len(report.deliveries)
        }

    def export_to_excel(self, report: DailyReport, output_path: str) -> str:
        """Export report to Excel."""

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Header
            header_df = pd.DataFrame([{
                'Project': report.project_name,
                'Project #': report.project_number,
                'Date': report.report_date,
                'Weather': report.weather.value,
                'High Temp': report.temperature_high,
                'Low Temp': report.temperature_low,
                'Prepared By': report.prepared_by
            }])
            header_df.to_excel(writer, sheet_name='Summary', index=False)

            # Work Activities
            if report.work_activities:
                activities_df = pd.DataFrame([
                    {
                        'Activity ID': a.activity_id,
                        'Description': a.description,
                        'Location': a.location,
                        'Planned': a.planned_qty,
                        'Actual': a.actual_qty,
                        'Unit': a.unit,
                        'Status': a.status.value,
                        'Crew': a.crew_size,
                        'Hours': a.hours_worked,
                        'Notes': a.notes
                    }
                    for a in report.work_activities
                ])
                activities_df.to_excel(writer, sheet_name='Work Activities', index=False)

            # Labor
            if report.labor:
                labor_df = pd.DataFrame([
                    {
                        'Trade': l.trade,
                        'Company': l.company,
                        'Workers': l.workers,
                        'Hours': l.hours,
                        'Overtime': l.overtime_hours,
                        'Total Hours': l.workers * (l.hours + l.overtime_hours)
                    }
                    for l in report.labor
                ])
                labor_df.to_excel(writer, sheet_name='Labor', index=False)

            # Equipment
            if report.equipment:
                equip_df = pd.DataFrame([
                    {
                        'Type': e.equipment_type,
                        'ID': e.equipment_id,
                        'Hours': e.hours_used,
                        'Status': e.status,
                        'Operator': e.operator
                    }
                    for e in report.equipment
                ])
                equip_df.to_excel(writer, sheet_name='Equipment', index=False)

        return output_path

    def generate_text_report(self, report: DailyReport) -> str:
        """Generate text version of report."""

        summary = self.calculate_summary(report)

        lines = [
            f"DAILY PROGRESS REPORT",
            f"=" * 50,
            f"Project: {report.project_name}",
            f"Project #: {report.project_number}",
            f"Date: {report.report_date}",
            f"Prepared by: {report.prepared_by}",
            f"",
            f"WEATHER CONDITIONS",
            f"-" * 30,
            f"Conditions: {report.weather.value}",
            f"Temperature: {report.temperature_low}°C - {report.temperature_high}°C",
            f"",
            f"SUMMARY",
            f"-" * 30,
            f"Total Workers: {summary['total_workers']}",
            f"Total Man-hours: {summary['total_manhours']}",
            f"Equipment Hours: {summary['equipment_hours']}",
            f"Activities Completed: {summary['activities_completed']}",
            f"Activities In Progress: {summary['activities_in_progress']}",
            f"Activities Delayed: {summary['activities_delayed']}",
            f"Safety Incidents: {summary['safety_incidents']}",
        ]

        if report.delays:
            lines.extend([f"", f"DELAYS", f"-" * 30])
            for delay in report.delays:
                lines.append(f"• {delay}")

        return "\n".join(lines)

Read the full file on GitHub · 387 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. 12d ago First seen · 387 lines · 27 tokens per session scan A 5177524190bf

Subscribe to this mod's changes

daily-progress-report is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 20d ago), licensed MIT. It adds 27 tokens to every session and 2,477 once invoked, about $0.0001 per session on Opus 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 2026-08-30.

Related

Other skills, from other repositories

skill-builder

Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.

yusufkaraaslan/Skill_Seekers · 38 tokens

brainstorm

Brainstorm Elixir/Phoenix features — explore ideas, compare approaches, gather requirements. Use when vague idea, not sure how to approach, or want to discuss before plan.

oliver-kriska/claude-elixir-phoenix · 39 tokens

ecto-patterns

Ecto patterns — schemas, changesets, queries, migrations, Multi, associations, preloads, upserts. Use when editing Repo calls, Ecto.Query, or schema fields. Skip for Ash.

oliver-kriska/claude-elixir-phoenix · 45 tokens

document

Generate @moduledoc/@doc for tested Elixir features; may update their README section or ADR. Not for docs lookup, documentation audits/reviews, or capturing standalone decisions.

oliver-kriska/claude-elixir-phoenix · 39 tokens

phx-research

Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.

oliver-kriska/claude-elixir-phoenix · 44 tokens

deep-research

Deep research and discovery before building something new. Explores local projects for reusable code, researches competitors, reads forums and reviews, analyses plugin ecosystems, investigates technical options, and produces a comprehensive research brief. Three depths: focused (30 min), wide (1-2 hours), deep (3-6…

jezweb/claude-skills · 126 tokens