{"slug":"datadrivenconstruction-productivity-analyzer","name":"productivity-analyzer","description":"Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.","long_description":"---\nname: \"productivity-analyzer\"\ndescription: \"Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.\"\nhomepage: \"https://datadrivenconstruction.io\"\nmetadata: {\"openclaw\": {\"emoji\": \"📊\", \"os\": [\"darwin\", \"linux\", \"win32\"], \"homepage\": \"https://datadrivenconstruction.io\", \"requires\": {\"bins\": [\"python3\"]}}}\n---\n# Productivity Analyzer\n\n## Business Case\n\n### Problem Statement\nUnderstanding productivity requires:\n- Tracking actual output rates\n- Comparing to planned rates\n- Identifying problem areas\n- Forecasting project completion\n\n### Solution\nAnalyze labor productivity data to identify trends, compare to benchmarks, and provide actionable insights.\n\n## Technical Implementation\n\n```python\nimport pandas as pd\nimport numpy as np\nfrom typing import Dict, Any, List, Optional\nfrom dataclasses import dataclass\nfrom datetime import date, timedelta\nfrom enum import Enum\n\n\nclass ProductivityStatus(Enum):\n    EXCELLENT = \"excellent\"    # >110% of planned\n    ON_TARGET = \"on_target\"    # 90-110%\n    BELOW = \"below\"            # 70-90%\n    CRITICAL = \"critical\"      # <70%\n\n\n@dataclass\nclass ProductivityRecord:\n    date: date\n    activity_code: str\n    description: str\n    planned_output: float\n    actual_output: float\n    unit: str\n    manhours: float\n    crew_size: int\n    conditions: str  # weather, access issues\n\n\n@dataclass\nclass ProductivityAnalysis:\n    activity_code: str\n    description: str\n    total_planned: float\n    total_actual: float\n    total_manhours: float\n    planned_rate: float  # unit per manhour\n    actual_rate: float\n    efficiency: float  # percentage\n    status: ProductivityStatus\n    trend: str  # improving, declining, stable\n\n\nclass ProductivityAnalyzer:\n    \"\"\"Analyze construction productivity data.\"\"\"\n\n    # Industry benchmark rates (unit per manhour)\n    BENCHMARKS = {\n        'concrete_pour': 0.5,      # m3/MH\n        'rebar_install': 15,       # kg/MH\n        'formwork': 0.8,           # m2/MH\n        'brick_laying': 35,        # bricks/MH\n        'drywall': 1.5,            # m2/MH\n        'painting': 3.0,           # m2/MH\n        'conduit': 8,              # m/MH\n        'pipe': 3,                 # m/MH\n        'excavation': 2.5,         # m3/MH\n        'backfill': 3.0,           # m3/MH\n    }\n\n    def __init__(self):\n        self.records: List[ProductivityRecord] = []\n\n    def add_record(self,\n                   date: date,\n                   activity_code: str,\n                   description: str,\n                   planned_output: float,\n                   actual_output: float,\n                   unit: str,\n                   manhours: float,\n                   crew_size: int,\n                   conditions: str = \"normal\"):\n        \"\"\"Add productivity record.\"\"\"\n\n        self.records.append(ProductivityRecord(\n            date=date,\n            activity_code=activity_code,\n            description=description,\n            planned_output=planned_output,\n            actual_output=actual_output,\n            unit=unit,\n            manhours=manhours,\n            crew_size=crew_size,\n            conditions=conditions\n        ))\n\n    def import_from_dataframe(self, df: pd.DataFrame):\n        \"\"\"Import records from DataFrame.\"\"\"\n        for _, row in df.iterrows():\n            self.add_record(\n                date=pd.to_datetime(row['date']).date(),\n                activity_code=row['activity_code'],\n                description=row.get('description', ''),\n                planned_output=float(row['planned_output']),\n                actual_output=float(row['actual_output']),\n                unit=row.get('unit', 'unit'),\n                manhours=float(row['manhours']),\n                crew_size=int(row.get('crew_size', 1)),\n                conditions=row.get('conditions', 'normal')\n            )\n\n    def _get_status(self, efficiency: float) -> ProductivityStatus:\n        \"\"\"Determine productivity status.\"\"\"\n        if efficiency >= 110:\n            return ProductivityStatus.EXCELLENT\n        elif efficiency >= 90:\n            return ProductivityStatus.ON_TARGET\n        elif efficiency >= 70:\n            return ProductivityStatus.BELOW\n        else:\n            return ProductivityStatus.CRITICAL\n\n    def _calculate_trend(self, records: List[ProductivityRecord]) -> str:\n        \"\"\"Calculate productivity trend.\"\"\"\n        if len(records) < 3:\n            return \"insufficient_data\"\n\n        # Sort by date\n        sorted_records = sorted(records, key=lambda x: x.date)\n\n        # Calculate efficiency for first and last third\n        n = len(sorted_records)\n        third = n // 3\n\n        early_efficiency = []\n        late_efficiency = []\n\n        for i, r in enumerate(sorted_records):\n            if r.manhours > 0:\n                eff = (r.actual_output / r.planned_output * 100) if r.planned_output > 0 else 0\n                if i < third:\n                    early_efficiency.append(eff)\n                elif i >= n - third:\n                    late_efficiency.append(eff)\n\n        if not early_efficiency or not late_efficiency:\n            return \"stable\"\n\n        early_avg = np.mean(early_efficiency)\n        late_avg = np.mean(late_efficiency)\n\n        if late_avg > early_avg * 1.05:\n            return \"improving\"\n        elif late_avg < early_avg * 0.95:\n            return \"declining\"\n        else:\n            return \"stable\"\n\n    def analyze_activity(self, activity_code: str) -> Optional[ProductivityAnalysis]:\n        \"\"\"Analyze productivity for specific activity.\"\"\"\n\n        activity_records = [r for r in self.records if r.activity_code == activity_code]\n\n        if not activity_records:\n            return None\n\n        total_planned = sum(r.planned_output for r in activity_records)\n        total_actual = sum(r.actual_output for r in activity_records)\n        total_manhours = sum(r.manhours for r in activity_records)\n\n        planned_rate = total_planned / total_manhours if total_manhours > 0 else 0\n        actual_rate = total_actual / total_manhours if total_manhours > 0 else 0\n        efficiency = (total_actual / total_planned * 100) if total_planned > 0 else 0\n\n        return ProductivityAnalysis(\n            activity_code=activity_code,\n            description=activity_records[0].description,\n            total_planned=round(total_planned, 2),\n            total_actual=round(total_actual, 2),\n            total_manhours=round(total_manhours, 1),\n            planned_rate=round(planned_rate, 3),\n            actual_rate=round(actual_rate, 3),\n            efficiency=round(efficiency, 1),\n            status=self._get_status(efficiency),\n            trend=self._calculate_trend(activity_records)\n        )\n\n    def analyze_all_activities(self) -> List[ProductivityAnalysis]:\n        \"\"\"Analyze all activities.\"\"\"\n        activities = set(r.activity_code for r in self.records)\n        return [self.analyze_activity(code) for code in activities if self.analyze_activity(code)]\n\n    def compare_to_benchmark(self, activity_code: str) -> Dict[str, Any]:\n        \"\"\"Compare activity to industry benchmark.\"\"\"\n\n        analysis = self.analyze_activity(activity_code)\n        if not analysis:\n            return {}\n\n        # Find matching benchmark\n        benchmark = None\n        for key, value in self.BENCHMARKS.items():\n            if key in activity_code.lower():\n                benchmark = value\n                break\n\n        if benchmark is None:\n            return {\n                'activity': activity_code,\n                'actual_rate': analysis.actual_rate,\n                'benchmark': 'Not available',\n                'vs_benchmark': 'N/A'\n            }\n\n        vs_benchmark = (analysis.actual_rate / benchmark * 100) if benchmark > 0 else 0\n\n        return {\n            'activity': activity_code,\n            'actual_rate': analysis.actual_rate,\n            'benchmark_rate': benchmark,\n            'vs_benchmark_pct': round(vs_benchmark, 1),\n            'recommendation': 'Above benchmark' if vs_benchmark >= 100 else 'Below benchmark - investigate'\n        }\n\n    def identify_problem_areas(self) -> List[Dict[str, Any]]:\n        \"\"\"Identify activities with productivity issues.\"\"\"\n\n        problems = []\n\n        for analysis in self.analyze_all_activities():\n            if analysis.status in [ProductivityStatus.BELOW, ProductivityStatus.CRITICAL]:\n                problems.append({\n                    'activity': analysis.activity_code,\n                    'efficiency': analysis.efficiency,\n                    'status': analysis.status.value,\n                    'trend': analysis.trend,\n                    'manhours_impacted': analysis.total_manhours,\n                    'priority': 'HIGH' if analysis.status == ProductivityStatus.CRITICAL else 'MEDIUM'\n                })\n\n        return sorted(problems, key=lambda x: x['efficiency'])\n\n    def forecast_completion(self,\n                            activity_code: str,\n                            remaining_quantity: float) -> Dict[str, Any]:\n        \"\"\"Forecast completion based on current productivity.\"\"\"\n\n        analysis = self.analyze_activity(activity_code)\n        if not analysis or analysis.actual_rate == 0:\n            return {}\n\n        # Manhours needed at current rate\n        manhours_needed = remaining_quantity / analysis.actual_rate\n\n        # Average daily manhours\n        activity_records = [r for r in self.records if r.activity_code == activity_code]\n        avg_daily_mh = np.mean([r.manhours for r in activity_records]) if activity_records else 8\n\n        days_needed = manhours_needed / avg_daily_mh if avg_daily_mh > 0 else 0\n\n        return {\n            'activity': activity_code,\n            'remaining_qty': remaining_quantity,\n            'current_rate': analysis.actual_rate,\n            'manhours_needed': round(manhours_needed, 1),\n            'days_needed': round(days_needed, 1),\n            'estimated_completion': date.today() + timedelta(days=int(days_needed))\n        }\n\n    def export_analysis(self, output_path: str) -> str:\n        \"\"\"Export analysis to Excel.\"\"\"\n\n        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:\n            # Summary\n            analyses = self.analyze_all_activities()\n            summary_df = pd.DataFrame([\n                {\n                    'Activity': a.activity_code,\n                    'Description': a.description,\n                    'Planned': a.total_planned,\n                    'Actual': a.total_actual,\n                    'Manhours': a.total_manhours,\n                    'Efficiency %': a.efficiency,\n                    'Status': a.status.value,\n                    'Trend': a.trend\n                }\n                for a in analyses\n            ])\n            summary_df.to_excel(writer, sheet_name='Summary', index=False)\n\n            # Problems\n            problems = self.identify_problem_areas()\n            if problems:\n                problems_df = pd.DataFrame(problems)\n                problems_df.to_excel(writer, sheet_name='Problem Areas', index=False)\n\n            # Raw data\n            records_df = pd.DataFrame([\n                {\n                    'Date': r.date,\n                    'Activity': r.activity_code,\n                    'Planned': r.planned_output,\n                    'Actual': r.actual_output,\n                    'Unit': r.unit,\n                    'Manhours': r.manhours,\n                    'Crew': r.crew_size,\n                    'Conditions': r.conditions\n                }\n                for r in self.records\n            ])\n            records_df.to_excel(writer, sheet_name='Raw Data', index=False)\n\n        return output_path\n```\n\n## Quick Start\n\n```python\nfrom datetime import date, timedelta\n\n# Initialize analyzer\nanalyzer = ProductivityAnalyzer()\n\n# Add records\nfor i in range(10):\n    analyzer.add_record(\n        date=date.today() - timedelta(days=i),\n        activity_code=\"concrete_pour\",\n        description=\"Slab pour Level 3\",\n        planned_output=20,\n    ","tagline":"Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.","category":"data-analysis","tags":["agent-skill"],"author":"datadrivenconstruction","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"recursive skill source sync","sourceDetail":"datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction","creatorName":"datadrivenconstruction","creatorUrl":"https://github.com/datadrivenconstruction","sourceUrl":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":282,"forks":74,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":40.26},"quality":{"score":71,"tier":"strong","label":"Strong","summary":"Solid option that is likely worth shortlisting for production workflows.","signals":[{"label":"GitHub stars","value":"282","tone":"neutral"},{"label":"Freshness","value":"2d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness."]},"trust":{"version":"trust-score-v5","score":67,"base_score":75,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["67/100 Trust Score v5","75/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":62,"weight":0.13,"status":"info","detail":"282 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":62,"weight":0.08,"status":"info","detail":"282 stars, 74 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"2d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":86,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":90,"weight":0.12,"status":"pass","detail":"no major dependency risk hints in public metadata"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":100,"weight":0.07,"status":"pass","detail":"no high-risk permission surface in public metadata"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer"},{"id":"review_status","label":"Review status","score":66,"weight":0.05,"status":"info","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"info","label":"GitHub adoption","detail":"282 GitHub stars"},{"status":"info","label":"Stars/forks activity","detail":"282 stars, 74 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"2d since push"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"pass","label":"Dependency/runtime risk","detail":"no major dependency risk hints in public metadata"},{"status":"pass","label":"Install availability","detail":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"pass","label":"Permission surface","detail":"no high-risk permission surface in public metadata"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer"},{"status":"info","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"pass","label":"OpenAgentSkill usage","detail":"3 views, 0 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern","Outcome loop is ready but needs first real agent run"],"warnings":["The SKILL.md excerpt is truncated; 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issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"2d since push"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"pass","label":"Dependency/runtime risk","detail":"no major dependency risk hints in public metadata"},{"status":"pass","label":"Install availability","detail":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"pass","label":"Permission surface","detail":"no high-risk permission surface in public metadata"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer"},{"status":"info","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"pass","label":"OpenAgentSkill usage","detail":"3 views, 0 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern","Outcome loop is ready but needs first real agent run"],"warnings":["The SKILL.md excerpt is truncated; 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issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"2d since push"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"pass","label":"Dependency/runtime risk","detail":"no major dependency risk hints in public metadata"},{"status":"pass","label":"Install availability","detail":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"pass","label":"Permission surface","detail":"no high-risk permission surface in public metadata"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer"},{"status":"info","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"pass","label":"OpenAgentSkill usage","detail":"3 views, 0 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern"],"warnings":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Quality score needs review"],"evidence":{"stars":"282 GitHub stars","repoActivity":"282 stars, 74 forks","lastPushed":"2d since push","license":"MIT","repository":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer","install":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","installSafety":"standard package or runtime install path","permissionSurface":"no high-risk permission surface in public metadata","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"installReadiness":{"ready":true,"command":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","policy":"human_review_before_install","label":"Human review before install","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","2d since push"]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["The SKILL.md excerpt is truncated; 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full documentation should be verified for completeness."],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","badge":"REVIEWED","auto_install_policy":"review","auto_install_allowed":false,"blocked":false,"human_review_required":true,"recommended_action":"Require human approval before installing into a real workspace.","reasons":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","69/100 agent safety score"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"review","score":75,"risk_level":"medium","decision":{"recommendation":"manual_review","reason":"Require human approval before installing into a real workspace.","auto_install_allowed":false,"policy":"review","human_review_required":true},"blockers":[],"warnings":["Trust score: Good trust signals with a few areas worth checking before rollout.","Audit score: Needs review","Agent safety gate: Usable candidate, but the agent should surface permission and audit notes before installation.","The SKILL.md excerpt is truncated; 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full documentation should be verified for completeness."]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"warn","score":69,"required_for_auto_install":true,"detail":"Usable candidate, but the agent should surface permission and audit notes before installation.","evidence":["Require human approval before installing into a real workspace.","The SKILL.md excerpt is truncated; full documentation should be verified for completeness."]},{"id":"readme_skillmd_completeness","label":"README/SKILL.md completeness","status":"pass","score":86,"required_for_auto_install":false,"detail":"Metadata includes enough usage and workflow context","evidence":["Strong README/SKILL.md context"]},{"id":"license_clarity","label":"License clarity","status":"pass","score":86,"required_for_auto_install":true,"detail":"MIT","evidence":["MIT"]},{"id":"recent_maintenance","label":"Recent maintenance","status":"pass","score":100,"required_for_auto_install":false,"detail":"2d since push","evidence":["2d since push"]},{"id":"permission_surface","label":"Permission surface","status":"pass","score":100,"required_for_auto_install":true,"detail":"no high-risk permission surface in public metadata","evidence":["Network access: medium"]},{"id":"alternatives","label":"Alternatives available","status":"info","score":55,"required_for_auto_install":false,"detail":"No close alternatives were found in the current shortlist.","evidence":[]}],"endpoints":{"web":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer/evals","api":"/api/agent/evals?slug=datadrivenconstruction-productivity-analyzer","text":"/api/agent/evals?slug=datadrivenconstruction-productivity-analyzer&format=text"}},"agent_readable_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"datadrivenconstruction-productivity-analyzer","name":"productivity-analyzer","description":"Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.","category":"data-analysis","url":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer","repository":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer","github_repo":"datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Navigate pages","Click and type safely"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install datadrivenconstruction-productivity-analyzer"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"productivity-analyzer\" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"productivity-analyzer\" as a Claude Code skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"productivity-analyzer\" from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/datadrivenconstruction-productivity-analyzer/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/datadrivenconstruction-productivity-analyzer"},"trust":{"score":75,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"282 GitHub stars","repoActivity":"282 stars, 74 forks","lastPushed":"2d since push","license":"MIT","repository":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer","install":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","installSafety":"standard package or runtime install path","permissionSurface":"no high-risk permission surface in public metadata","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["data-analysis","agent-skill"],"known_risks":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":81,"risk_level":"needs_review","risk_label":"Needs review","warnings":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Access to filesystem is granted, but no explicit sandboxing or validation of file paths is described in instructions.md.","Quality score needs review"]},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Require human approval before installing into a real workspace."},"quality":{"score":71,"label":"Strong"},"supply":{"track":"Coding and developer agents","scenario":"GitHub automation","maintenance":"2d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Access to filesystem is granted, but no explicit sandboxing or validation of file paths is described in instructions.md.","Quality score needs review","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"],"agent_contract":{"task_input":"Use productivity-analyzer in an agent workflow","recommended_action":"Require human approval before installing into a real workspace.","install_policy":"review","minimum_review_before_use":["Trust: 75/100 Strong shortlist","Audit: 81/100 Needs review","Safety: 69/100 Review before install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"datadrivenconstruction-productivity-analyzer (productivity-analyzer)","install_command":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","risk_summary":"Needs review; Reviewed with permission notes; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"datadrivenconstruction-productivity-analyzer","task":"Use productivity-analyzer in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer","api":"https://www.openagentskill.com/api/agent/skills/datadrivenconstruction-productivity-analyzer","audit":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=datadrivenconstruction-productivity-analyzer&task=Use%20productivity-analyzer%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20productivity-analyzer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20productivity-analyzer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/datadrivenconstruction-productivity-analyzer/install","manifest":"https://www.openagentskill.com/api/registry/manifest/datadrivenconstruction-productivity-analyzer"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"datadrivenconstruction-productivity-analyzer","name":"productivity-analyzer","description":"Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.","category":"data-analysis","url":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer","repository":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer","github_repo":"datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Navigate pages","Click and type safely"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install datadrivenconstruction-productivity-analyzer"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"productivity-analyzer\" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"productivity-analyzer\" as a Claude Code skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"productivity-analyzer\" from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/datadrivenconstruction-productivity-analyzer/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/datadrivenconstruction-productivity-analyzer"},"trust":{"score":75,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"282 GitHub stars","repoActivity":"282 stars, 74 forks","lastPushed":"2d since push","license":"MIT","repository":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer","install":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","installSafety":"standard package or runtime install path","permissionSurface":"no high-risk permission surface in public metadata","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["data-analysis","agent-skill"],"known_risks":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":81,"risk_level":"needs_review","risk_label":"Needs review","warnings":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Access to filesystem is granted, but no explicit sandboxing or validation of file paths is described in instructions.md.","Quality score needs review"]},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Require human approval before installing into a real workspace."},"quality":{"score":71,"label":"Strong"},"supply":{"track":"Coding and developer agents","scenario":"GitHub automation","maintenance":"2d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Access to filesystem is granted, but no explicit sandboxing or validation of file paths is described in instructions.md.","Quality score needs review","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"],"agent_contract":{"task_input":"Use productivity-analyzer in an agent workflow","recommended_action":"Require human approval before installing into a real workspace.","install_policy":"review","minimum_review_before_use":["Trust: 75/100 Strong shortlist","Audit: 81/100 Needs review","Safety: 69/100 Review before install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"datadrivenconstruction-productivity-analyzer (productivity-analyzer)","install_command":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","risk_summary":"Needs review; Reviewed with permission notes; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"datadrivenconstruction-productivity-analyzer","task":"Use productivity-analyzer in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer","api":"https://www.openagentskill.com/api/agent/skills/datadrivenconstruction-productivity-analyzer","audit":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=datadrivenconstruction-productivity-analyzer&task=Use%20productivity-analyzer%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20productivity-analyzer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20productivity-analyzer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/datadrivenconstruction-productivity-analyzer/install","manifest":"https://www.openagentskill.com/api/registry/manifest/datadrivenconstruction-productivity-analyzer"}},"supply_profile":{"track":{"slug":"coding","label":"Coding and developer agents","shortLabel":"Coding","description":"Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills."},"scenario":{"label":"GitHub automation","description":"I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.","useCases":[{"slug":"research-agents","title":"Research agents"},{"slug":"browser-automation","title":"Browser automation"},{"slug":"sports-analytics","title":"Sports analytics"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":282,"starsLabel":"282","forks":74,"license":"MIT","qualityScore":71,"trustScore":75,"auditScore":81},"maintenance":{"status":"fresh","label":"2d since push","daysSincePush":2,"lastPushedAt":"2026-08-22T07:59:22+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Access to filesystem is granted, but no explicit sandboxing or validation of file paths is described in instructions.md.","Quality score needs review","Needs review"]},"coverageTags":["Coding","GitHub automation","data-analysis","agent-skill"]},"audit":{"audit_score":81,"risk_level":"needs_review","risk_label":"Needs review","quality_score":71,"trust_score":75,"maintenance_score":100,"security_score":83,"install_score":92,"warnings":["The SKILL.md excerpt is truncated; full documentation should be verified for completeness.","Access to filesystem is granted, but no explicit sandboxing or validation of file paths is described in instructions.md.","Quality score needs review"]},"quality_signals":{"model":"v2","star_score":17.16,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"},{"slug":"sports-analytics","title":"Sports analytics","url":"https://www.openagentskill.com/use-cases/sports-analytics"},{"slug":"github-automation","title":"GitHub automation","url":"https://www.openagentskill.com/use-cases/github-automation"}],"stacks":[{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"},{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"frontend-product-ui","title":"Frontend and UI","url":"https://www.openagentskill.com/collections/frontend-product-ui"}],"install":"npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill productivity-analyzer","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install datadrivenconstruction-productivity-analyzer","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"productivity-analyzer\" agent skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"productivity-analyzer\" as a Claude Code skill from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"productivity-analyzer\" from https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"datadrivenconstruction-productivity-analyzer\",\"task\":\"Install productivity-analyzer\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer","github_repo":"datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction","version":"1.0.0","license":"MIT","urls":{"web":"https://www.openagentskill.com/skills/datadrivenconstruction-productivity-analyzer","repository":"https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/Analytics/productivity-analyzer","api":"/api/agent/skills/datadrivenconstruction-productivity-analyzer","install_api":"/api/skills/datadrivenconstruction-productivity-analyzer/install"},"meta":{"created_at":"2026-08-22T10:35:36.053223+00:00","updated_at":"2026-08-22T10:35:36.053223+00:00","agent_friendly":true}}