This document describes how Github Engine recommendations are intended to work in future phases.
Produce recommendations that are specific, evidence-based, and sequenced for practical execution in real repositories.
Focus on README clarity, onboarding quality, architecture communication, and operational guidance.
Focus on confidence gaps, missing validation paths, unstable quality signals, and regression risk exposure.
Focus on workflow friction, script ergonomics, environment setup reliability, and contributor efficiency.
Focus on module boundaries, ownership clarity, scalability pressure points, and design coherence.
Focus on pipeline reliability, validation gates, quality checks, release safety, and feedback speed.
Focus on which integrations would materially improve recommendation quality and operational relevance.
Focus on predictable repository conventions, documentation structure, and repeatable quality reporting.
Recommendations should be:
- Contextual: grounded in observed repository characteristics and workflow signals
- Ranked: ordered by impact, urgency, and implementation effort
- Justified: accompanied by explicit reasoning and evidence references
- Non-generic: tailored to project type, maturity, and current constraints
- Maturity-aware: sensitive to whether a project is early-stage, scaling, or operationally mature
Future ranking may combine:
- expected impact
- confidence level
- implementation complexity
- risk reduction value
- dependency sequencing
The goal is not maximum recommendation count. The goal is high-value recommendation quality.
Recommendation outputs should include:
- clear action statement
- rationale
- expected outcome
- effort estimate band
- evidence/source attribution
This model is conceptual and defines target behavior for upcoming implementation phases.