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Technical Review Rubric Library + Coaching Service

A boutique service that audits a company's codebase and technical projects, then builds a custom, domain-specific evaluation rubric (e.g., for microservices, data pipelines, frontend systems). The service includes 4 weeks of on-site or remote coaching where a senior engineer trains PMs and tech leads to use the rubric consistently, calibrate their judgments, and deliver structured feedback. Rubrics are documented in a shared wiki/Notion template the client owns forever.

SERVICE

18 weeks • 70% confidence

Value Proposition

Unlike generic frameworks, this is built on YOUR actual tech stack, architecture patterns, and risk profile. PMs stop guessing and start citing specific, measurable criteria. Developers get actionable feedback instead of 'this doesn't feel right.' Reduces rework, speeds up code review cycles, and cuts post-launch incidents tied to review misses. The coaching embeds the practice; it doesn't fade after onboarding.

Target Audience

Engineering managers and technical leads at mid-market software companies (50–500 engineers) where code review quality is a known pain point and PM–developer friction is visible.

Key Features

  • Custom rubric design based on live codebase audit (not template)
  • Rubric covers: architecture decisions, testability, security, performance, maintainability, team patterns
  • 4-week embedded coaching with PMs, tech leads, and senior developers
  • And more, with full implementation detail...

Tech Stack

Google Docs / Notion for rubric templates and knowledge base Zoom for coaching sessions and recordings GitHub API (optional) to pull real code examples for calibration exercises Simple landing page (Webflow or WordPress)
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Original Problem

Project managers struggle to evaluate and provide constructive feedback on complex technical work

Project managers and technical leads lack a systematic framework for reviewing difficult projects, leading to vague feedback, missed quality issues, and team frustration. Current approaches rely on subjective opinions rather than structured evaluation criteria, making it hard to identify specific problems and communicate actionable improvements to developers.

Score: 46.5%

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