Construction Project Management Support Agent Skill
ποΈπ Construction Project Management Support | Production-grade construction PM audit engine. Evaluates project schedules, Earned Value Management (EVM), and site risk logs against PMI and Lean Construction standards via a crawl4ai pipeline.
Install / Use
npx skills add dungnotnull/construction-project-management-support-agent-skillInstalls into whichever agent you are using.
README
ποΈ Construction Project Management Support Agent Skill
A production-grade AI agent skill that evaluates construction project plans for schedule, cost control, and site risk management against PMI/Lean Construction standards.
π Overview
The Construction Project Management Support skill is a sophisticated AI agent that provides professional-grade project management assessments grounded in world-renowned frameworks. It conducts comprehensive evaluations across five critical dimensions:
- Schedule & CPM Integrity (25%) β Network logic, float analysis, critical path realism
- Cost Control & EVM (25%) β Budget structure, CPI/SPI analysis, contingency adequacy
- Risk & Safety Management (20%) β Risk register completeness, mitigation planning, safety protocols
- Stakeholder & Procurement (15%) β RACI clarity, contract terms, supply chain resilience
- Quality & Change Control (15%) β QA/QC processes, change order discipline, inspection regimes
β¨ Key Features
π¬ Evidence-Based Assessment
Every score and recommendation is backed by authoritative sources:
- PMI PMBOK Guide (7th Edition) β Global project management standard
- Earned Value Management (EIA-748) β Quantitative cost/schedule analysis
- Lean Construction & Last Planner System β Workflow optimization and pull planning
- Critical Path Method (AACE RP 46R-11) β Network scheduling best practices
- ISO 21500 & RICS NRM β International guidance and measurement standards
π― Multi-Dimensional Scoring
Comprehensive 0-100 scoring rubric with weighted dimensions, evidence citations, and letter grades (A-F).
πΊοΈ Prioritized Improvement Roadmap
Effort/impact-ranked recommendations with implementation phases, timelines, and expected outcomes.
π§ Self-Improving Knowledge Base
Automated weekly knowledge updates via crawl pipeline, keeping the skill current with latest research and standards.
π Graceful Degradation
Works offline using cached knowledge when web search unavailableβno single point of failure.
π Use Cases
| Scenario | Description | Output | |----------|-------------|--------| | Schedule Health | "Is my project schedule realistic?" | CPM analysis, duration validation, recovery roadmap | | EVM Diagnosis | "CPI is 0.85, SPI is 0.9 β what do I do?" | Variance analysis, root cause identification, corrective actions | | Risk Review | "Review our risk register" | Completeness check, gap analysis, mitigation enhancement | | Stakeholder Mapping | "Who signs off at each stage?" | RACI matrix, approval chains, communication plan | | Comprehensive Audit | Full project health assessment | Multi-dimensional scores with prioritized roadmap |
π Project Structure
construction-project-management-support/
βββ README.md # This file
βββ LICENSE # MIT License
βββ CLAUDE.md # Project instructions
βββ PROJECT-detail.md # Technical specification
βββ PROJECT-DEVELOPMENT-PHASE-TRACKING.md # Development status
βββ SECOND-KNOWLEDGE-BRAIN.md # Knowledge base (self-updating)
β
βββ skills/ # Agent skill definitions
β βββ main.md # Main harness (8-stage workflow)
β βββ sub-intake.md # Intake & context gathering
β βββ sub-framework-selector.md # Framework selection logic
β βββ sub-scoring-engine.md # Multi-dimensional scoring
β βββ sub-improvement-roadmap.md # Prioritized recommendations
β
βββ tools/ # Supporting tools
β βββ knowledge_updater.py # Weekly knowledge crawl pipeline
β
βββ tests/ # Test scenarios
βββ test-scenarios.md # 5 comprehensive test cases
ποΈ Harness Architecture
The skill implements a rigorous 8-stage workflow:
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β 1. INTAKE (sub-intake) β
β Gather structured inputs, scope, goals, constraints β
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β
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β 2. FRAMEWORK SELECTOR (sub-framework) β
β Select PMI/EVM/Lean/CPM/ISO frameworks with justification β
βββββββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββββββββββ
β
βββββββββββββββββββββββββββββΌββββββββββββββββββββββββββββββββββββββ
β 3. RESEARCH β
β Gather evidence from WebSearch + SECOND-KNOWLEDGE-BRAIN β
βββββββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββββββββββ
β
βββββββββββββββββββββββββββββΌββββββββββββββββββββββββββββββββββββββ
β 4. SCORING ENGINE (sub-scoring) β
β Apply 0-100 rubric with evidence citations, weighted scores β
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β
βββββββββββββββββββββββββββββΌββββββββββββββββββββββββββββββββββββββ
β 5. CHALLENGE β
β Devil's advocate review: test assumptions, grade certainty β
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β
βββββββββββββββββββββββββββββΌββββββββββββββββββββββββββββββββββββββ
β 6. IMPROVEMENT ROADMAP (sub-roadmap) β
β Prioritized effort/impact recommendations with phases β
βββββββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββββββββββ
β
βββββββββββββββββββββββββββββΌββββββββββββββββββββββββββββββββββββββ
β 7. SYNTHESIZE β
β Assemble professional report with all sections β
βββββββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββββββββββ
β
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β 8. QUALITY GATES β
β Verify citations, roadmap traceability, limitations stated β
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π Scoring Rubric
Each dimension is scored 0-100 with specific criteria:
| Score Range | Grade | Description | |-------------|-------|-------------| | 90-100 | A | Excellent β Industry best practice, comprehensive coverage, proactive management | | 75-89 | B | Good β Solid practice, minor gaps, generally well-managed | | 60-74 | C | Fair β Basic practice in place, notable gaps, reactive approach | | 40-59 | D | Poor β Significant gaps, minimal formal process, high exposure | | 0-39 | F | Critical β Major deficiencies, unacceptable risk, immediate attention required |
Overall Grade Calculation:
Overall = (Schedule Γ 0.25) + (Cost Γ 0.25) + (Risk Γ 0.20) +
(Stakeholder Γ 0.15) + (Quality Γ 0.15)
π§ Knowledge Base
The skill maintains a comprehensive knowledge base (SECOND-KNOWLEDGE-BRAIN.md) containing:
- Framework Documentation β Detailed coverage of PMI, EVM, Lean, CPM, ISO, RICS
- Research Papers β Key studies with relevance notes and citations
- Best Practices β State-of-the-art methods and industry tools
- Authoritative Sources β PMI, LCI, RICS, ISO, CII, AACE, and more
Self-Update Pipeline
The tools/knowledge_updater.py script runs weekly (recommended via cron) to:
- Fetch latest research from authoritative sources
- Parse and score entries by relevance
- Deduplicate against existing entries
- Append new findings to knowledge base
Install dependencies (optional):
pip install crawl4ai
Run manually:
python tools/knowledge_updater.py --dry-run --verbose
Set up weekly cron:
0 2 * * 0 cd /path/to/construction-project-management-support && python tools/knowledge_updater.py >> tools/knowledge_updater.log 2>&1
π§ͺ Testing
The skill includes 5 comprehensive test scenarios in tests/test-scenarios.md:
- Schedule Health β Commercial building, planning phase
- EVM Diagnosis β Infrastructure project, execution phase
- Risk Register Review β Healthcare construction, regulatory focus
- Stakeholder Mapping β School project, public sector approvals
- Degraded Mode β Offline operation using cached knowledge
Each scenario documents expected outputs stage-by-stage with pass criteria.
π οΈ Installation
For Claude Code Users
- Clone the repository:
git clone https://github.com/dungnotnull/construction-project-management-support-agent-skill.git
cd construction-project-management-support-agent-skill
- Verify skill structure:
# Should show all skill files
ls -la skills/
- Test the skill:
- Open Claude Code in the project directory
- Invoke the skill with a construction project management query
- Example: "Review this project schedule for realism"
For Integration
The skill follows the Claude Code skill format with frontmatter metadata:
---
name: construction-project-management-support
description: Evaluates constructio
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Audited on Aug 3, 2026
