freelancer-project-delivery

name	freelancer-project-delivery
description	Use when onboarding clients, setting up git repositories, designing MLOps pipelines (ZenML, MLflow, DVC), structuring tests/QA for ML, and managing project milestones or delivery handoffs.

Freelancer Project Delivery System

Deliver production-grade work that exceeds client expectations, maintains quality standards, and creates natural upsell opportunities through systematic project management and MLOps excellence.

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Skill Architecture

To utilize this skill's core workflows, configuration patterns, or checklists, read the corresponding document in the references/ directory:

File	Focus	Purpose
04A-KICKOFF-SYSTEM.md	Kickoff System	Client onboarding checklist, technical access protocol, communication bounds, project portal configuration
04B-PROJECT-MANAGEMENT.md	Project Management	Agile sprint structures, milestone definitions, async weekly status templates
04C-DEVELOPMENT-WORKFLOW.md	Development Workflow	Git branch architecture, pull request templates, linter and formatter settings
04D-ML-OPS-PIPELINE.md	MLOps Pipeline	Production ML pipelines (ZenML, MLflow, DVC), model registration, drift detection (Evidently AI)
04E-TESTING-QA.md	Testing & QA	unit testing, integration tests, robustness testing, CI pipelines
04F-DELIVERY-HANDOFF.md	Delivery & Handoff	Production handoff checklists, demo video creation guide, live training scripts

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Agent Guidelines & Workflows

1. Onboarding & Kickoff

When initiating a new client contract:

• Reference 04A-KICKOFF-SYSTEM.md to set up the client's Project Portal (Slack/Notion/Linear) and request infrastructure/data access.
• Execute the kickoff checklist to define communication boundaries (no unscheduled calls, weekly async reports).

2. Agile Project Management

When managing timelines and writing client updates:

• Reference 04B-PROJECT-MANAGEMENT.md to run 2-week development sprints featuring clean milestone tracking.
• Draft professional weekly status updates detailing what was done, what is planned next, and blockages/risks.

3. Engineering & Development Workflows

When setting up codebases or code review pipelines:

• Reference 04C-DEVELOPMENT-WORKFLOW.md to establish clean git workflows (main $\rightarrow$ dev $\rightarrow$ feature/*).
• Configure automated code formatting (Black, Ruff) and apply standardized pull request templates.

4. MLOps System Design

When designing, building, or configuring machine learning pipelines:

• Reference 04D-ML-OPS-PIPELINE.md to structure model runs with ZenML, version data with DVC, track parameters/metrics with MLflow, and registry/deploy models.
• Set up automated data and concept drift detection using Evidently AI.

5. ML-Specific Testing and Quality Assurance

When writing testing pipelines for data or models:

• Reference 04E-TESTING-QA.md to establish a test suite containing: python unit tests, data schema validation (Pydantic), model performance assertion checks, and robustness/bias verification.

6. Client Handoff and Demos

When completing the project and transferring ownership:

• Reference 04F-DELIVERY-HANDOFF.md to record high-impact 2-minute demo videos, package documentation, and run live training sessions.

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Quick Reference Summary

Concept	File	Section	One-Line Summary
Project Portal	04A	3	Establish a shared workspace as the single source of truth for communication
2-Week Sprints	04B	2	Structure projects into bi-weekly sprints with concrete, demoable deliverables
Git Branch Flow	04C	2	Protect production branch: branch from dev, require PR verification and linting
Production ML Pipeline	04D	2	Maintain reproducible pipelines: Data Validation $\rightarrow$ Feature Prep $\rightarrow$ Model Registry
Drift Dashboards	04D	5	Implement data drift alerts to flag performance decay in production
Testing Pyramid	04E	2	Structure test suite: 70% Unit tests, 20% Integration tests, 10% End-to-End checks
2-Minute Demo	04F	2	Record a concise, problem-to-solution video demo of the working system

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