🌐 Language: English Manual
🐙 View on GitHub ↗ 🇸🇦 الانتقال للنسخة العربية (Arabic Manual) →

Tasleemat PMO Logo

📘 Tasleemat PMO Toolkit — Comprehensive Practitioner & Automation Guide

Document Reference: TASLEEMAT-GUIDE-02-USAGE-GUIDE
Standard: PMI PMBOK® Guide 6th, 7th & 8th Edition Standard
Target Audience: Project Managers, PMO Directors, AI Engineers, Scrum Masters

Welcome to the definitive practitioner and automation guide for the Tasleemat PMO Toolkit. Whether you are a project manager looking to standardize physical deliverables, a PMO director establishing enterprise governance, or an AI engineer building autonomous project management agents, this guide covers every workflow end-to-end.

📑 Table of Contents

  1. The 5-File Architecture
  2. Global Parameters Configuration
  3. AI-Powered Document Generation Workflows
  4. Workflow A: Interactive Chat Prompting (Claude / ChatGPT / Gemini)
  5. Workflow B: Structured JSON & API Automation
  6. Workflow C: Local LLM Execution (Ollama / vLLM)
  7. Managing Lifecycle Dependencies
  8. Enterprise Publishing & PDF Export
  9. Data Analytics & Dashboard Automation
  10. Governance & Sign-off Matrix

1. The 5-File Architecture

Every one of the 114 deliverable directories (228 total bilingual bundles across ../../forms/en/ and ../../forms/ar/) contains exactly five synchronized files engineered for specific stages of the document lifecycle:

01_Project_Charter/
├── 03_01_Project_Charter_Guide.md     # 1. Human Practitioner Guide & Dependency Rules
├── 03_01_Project_Charter_Template.md  # 2. Production Markdown Template with Sign-offs
├── 03_01_Project_Charter.md           # 3. LLM System Prompt & Field Constraints
├── 03_01_Project_Charter.json         # 4. Machine-Readable Schema (API & JSON Mode)
└── 03_01_Project_Charter.csv          # 5. 4-Column Data Dictionary for BI & Analytics

1.1 The Practitioner Guide (*_Guide.md / *_دليل.md)

  • When to use: Read before drafting or reviewing the artifact.
  • Key Sections:
  • Purpose & Core Value: What problem this document solves and what risks arise if it is omitted.
  • Writing Guidelines: Quality standards, anti-patterns, and precision rules.
  • Column & Field Definitions: Guidance on each table column and metadata field.
  • Alignment & Dependencies: Explicit Mandatory and Optional upstream prerequisites and downstream dependents with official document IDs (e.g. PMO-03.01).

1.2 The Production Template (*_Template.md / *_قالب.md)

  • When to use: The final deliverable document for stakeholder review, wiki storage, and formal sign-offs.
  • Key Features:
  • Standardized metadata header (Date Prepared, Project Manager, Prepared By).
  • Document reference code and organizational branding placeholders ({{Company_Name}}, {{Project_Name}}).
  • Role-based governance approval table assigning sign-off responsibility to appropriate governance roles (e.g., Project Sponsor, Finance Controller, CCB Chair, QA Lead).

1.3 The LLM Generation Prompt (*.md)

  • When to use: Copy and paste into an LLM interface along with project context to generate a draft.
  • Key Features:
  • Structured prompt block quoting alignment rules and dependencies.
  • Detailed system instructions specifying exactly what each section must contain.

1.4 The Data Schema (*.json)

  • When to use: Programmatic document generation pipelines, LLM JSON mode, and schema validation.
  • Key Structure: json { "_llm_instructions": "Fill in the project_title and value properties...", "form_name": "Project Charter", "document_reference": "PMO-03.01", "project_title": "", "date_prepared": "", "fields": { "f01_Project_Purpose": { "section": "1. Project Purpose and Business Case", "label": "Project Purpose", "guidance": "Clear business rationale...", "value": "" } } }

1.5 The Tabular Data Dictionary (*.csv)

  • When to use: Importing into Excel, Google Sheets, PowerBI, or enterprise database tables.
  • Structure: Exactly 4 columns: Section, Field, Guidance, LLM_Generated_Value.

2. Global Parameters Configuration

Before generating documents with AI, define your project's global baseline variables in ../../forms/en/parameters.md (or ../../forms/ar/parameters.md for Arabic):

# Project Global Parameters (معاملات المشروع العامة)

* **Company_Name:** Enterprise Solutions Corp
* **Project_Name:** Customer Portal 2.0 Modernization
* **Project_ID:** PRJ-2026-088
* **Project_Manager_Name:** John Doe, PMP
* **Project_Sponsor:** Jane Smith (VP of Digital Transformation)
* **Prepared_By:** PMO Lead Analyst
* **Target_Start_Date:** 2026-11-01
* **Target_Finish_Date:** 2027-05-31
* **Total_Budget_BAC:** $1,250,000 USD
* **Currency:** USD

3. AI-Powered Document Generation Workflows

Workflow A: Interactive Chat Prompting (Claude / ChatGPT / Gemini)

  1. Open your LLM chat interface.
  2. Provide the system instructions and global parameters: ```text System: You are an expert Enterprise PMO Lead and Senior Project Manager. I will provide you with:
  3. Global project parameters
  4. Rough stakeholder meeting notes
  5. The exact PMO prompt specification for the target artifact

Your task is to generate a comprehensive, professional, audit-ready Markdown document matching the exact structure and tables of the target template. Do not summarize or omit sections. `` 3. Paste the contents of../../forms/en/parameters.md. 4. Paste the specific prompt file (e.g., [../../forms/en/04_Planning/08_Risk/02_Risk_Register/04_08_02_Risk_Register.md](../../forms/en/04_Planning/08_Risk/02_Risk_Register/04_08_02_Risk_Register.md)). 5. Paste your raw project notes (e.g., transcripts, bullet points, or email summaries). 6. The AI will return the complete, filled-out Markdown document ready to paste into_Template.md`.

Workflow B: Structured JSON & API Automation

For automated backend pipelines (e.g. Python, LangChain, LlamaIndex, or OpenAI Function Calling):

import json
import openai

# 1. Load schema
with open("../../forms/en/03_Initiating/01_Project_Charter/03_01_Project_Charter.json") as f:
    schema = json.load(f)

# 2. Call OpenAI with JSON Mode
response = openai.chat.completions.create(
    model="gpt-4o",
    response_format={"type": "json_object"},
    messages=[
        {
            "role": "system",
            "content": f"You are a PMO document generator. Populate the 'value' properties of this schema based on context: {json.dumps(schema)}"
        },
        {
            "role": "user",
            "content": "Project: Enterprise ERP Upgrade. Budget: $2M. Sponsor: CFO. Notes: Phase 1 replaces legacy billing."
        }
    ]
)

# 3. Save populated JSON
populated_data = json.loads(response.choices[0].message.content)
with open("ERP_Project_Charter.json", "w") as out:
    json.dump(populated_data, out, indent=2)

Workflow C: Local LLM Execution (Ollama / vLLM)

Run complete document generation locally and securely without external data transmission:

# Run local generation with Llama 3 or Mistral via Ollama
ollama run llama3:70b "$(cat ../../forms/en/parameters.md) $(cat ../../forms/en/05_Executing/01_Issue_Log/05_01_Issue_Log.md) Meeting notes: [insert notes]" > Issue_Log_Draft.md

4. Managing Lifecycle Dependencies

Tasleemat forms are interconnected through upstream prerequisites and downstream consumers. Refer to ../LEXICON.md or section ### Alignment & Dependencies in any *_Guide.md before approving baselines:

graph TD
    A["Business Case (PMO-01.01)"] --> B["Project Charter (PMO-03.01)"]
    B --> C["Scope Baseline / WBS (PMO-04.02.06)"]
    B --> D["Stakeholder Register (PMO-03.04)"]
    C --> E["Project Schedule (PMO-04.03.08)"]
    C --> F["Cost Baseline (PMO-04.04.04)"]
    E & F --> G["Earned Value Analysis (PMO-06.05)"]
    G --> H["Project Closeout (PMO-07.03)"]
  • Mandatory Prerequisite: You must have an approved Project Charter (PMO-03.01) before establishing the Project Management Plan (PMO-04.01.01).
  • Downstream Consumer: Updates to the Risk Register (PMO-04.08.02) directly feed into Risk Reports (PMO-04.08.06) and Change Requests (PMO-05.03).

5. Enterprise Publishing & PDF Export

The templates in Tasleemat are engineered to render cleanly across modern Markdown viewers and export into executive PDF reports:

  1. VS Code (Markdown PDF Extension):
  2. Open any _Template.md or populated Markdown file.
  3. Press Ctrl+Shift+P (or Cmd+Shift+P on Mac) $\rightarrow$ Markdown PDF: Export (pdf).
  4. Obsidian / Logseq:
  5. Open the note $\rightarrow$ Click Export to PDF (select standard A4 format with 1-inch margins).
  6. Enterprise Wikis (Confluence / Notion):
  7. Copy and paste the Markdown directly. HTML tables and headers will convert natively into rich tables.

6. Data Analytics & Dashboard Automation

Because every form maintains an identical 4-column CSV data structure, you can aggregate and analyze project health across multiple teams in minutes:

import pathlib
import pandas as pd

# Load all Arabic and English project fields into a unified DataFrame
records = []
for csv_file in pathlib.Path("forms").rglob("*.csv"):
    df = pd.read_csv(csv_file)
    df["form_name"] = csv_file.stem
    df["language"] = "ar" if "ar" in csv_file.parts else "en"
    records.append(df)

master_df = pd.concat(records, ignore_index=True)
print(f"Total PMO data fields cataloged: {len(master_df)}")
print(master_df.head())

7. Governance & Sign-off Matrix

Every template concludes with an Approval and Sign-off Table tailored to the governance authority required for that document:

Document Category Primary Approver Mandatory Signatories
Charters & Strategic Baselines Project Sponsor Project Sponsor, PMO Director, Project Manager
Scope, WBS & Technical Baselines Lead Systems Engineer Lead Systems Engineer, Technical Architect, PM
Cost & Procurement Baselines Finance Controller Finance Controller / CFO, Procurement Lead, PM
Quality & Acceptance Records QA Lead / Business Client Quality Lead, Key Stakeholder, PM
AI Governance & Model Cards AI Ethics Lead AI Ethics Officer, Data Protection Officer (DPO), ML Lead
Change Control & Exceptions CCB Chair Change Control Board (CCB) Members, Project Sponsor
For terminology translations and full index, consult ../LEXICON.md.