🧭 Onboarding & Pathways
Start Here: Three Delivery Pathways
Choose the right starting path based on how your team delivers projects.
Pathway 1: Project Practitioners & PMO Directors
Templates & GovernanceFor Project Managers, PMO Leaders, and Delivery Directors looking to establish structured governance, avoid blank-document paralysis, and tailor deliverables to organizational risk.
- Step 1 — Assess Project Sizing Tier: Classify your project into Tier 1 (> $10M / Mega), Tier 2 ($1M–$10M / Standard), or Tier 3 (< $1M / Lean). See the Tailoring Profiles Manual.
- Step 2 — Select Initiation Deliverables: Download foundational templates starting with PMO-03.01 Project Charter, PMO-03.03 Assumption Log, and PMO-03.04 Stakeholder Register.
- Step 3 — Review the 12 Core Lifecycle Manuals: Align stage gates and RACI authority before proceeding to detailed baseline planning in Stage 04.
Pathway 2: Technical Teams & Automation Engineers
CLI & Python SDKFor software engineers, DevOps practitioners, and automation leads integrating Tasleemat deliverables directly into GitOps pipelines, CI/CD validations, or internal portals.
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Step 1 — Install the Verified Python Package:
pip install tasleemat -
Step 2 — List Tailored Deliverables via CLI:
tasleemat list --tier 2 --pack agile -
Step 3 — Export Templates & Validate Data:
tasleemat asset PMO-04.14 template --lang en > risk_plan.md tasleemat validate PMO-04.14 completed_data.json
Pathway 3: AI Assistants, LLMs & Multi-Agent Systems
Model Context Protocol (MCP)For teams pairing with AI assistants (Claude Desktop, Cursor, Antigravity) or building multi-agent systems that draft, audit, and validate project governance documents deterministically.
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Step 1 — Configure the Stdio MCP Server:
Add the Tasleemat MCP tool server to your client configuration:
{ "mcpServers": { "tasleemat": { "command": "tasleemat", "args": ["mcp"] } } } -
Step 2 — Deterministic Artifact Fetching:
Instruct your LLM to fetch the official prompt, guide, and Draft 2020-12 schema using
get_artifact_assetrather than hallucinating deliverable structures. -
Step 3 — Closed Self-Correction Validation Loop:
Pass drafted JSON data to
validate_artifact_data. If errors occur, provide schema error paths back to the LLM for autonomous self-correction before human sign-off.