Deterministic generator
Workout composition runs through explicit filtering, scoring, substitution, and safety stages.
FitnessCoach
Deterministic workout adaptation with a safety-first posture.
This repository contains a cross-platform training and recovery product scaffold that centers clear rules, conservative symptom handling, and explainable daily adjustments.
What this is and why it exists
FitnessCoach is built for training guidance that can be reviewed and defended. Instead of opaque recommendations, the product uses a deterministic rules engine, structured check-ins, and explicit safety escalation states.
- Explains why sessions are changed using plain language.
- Tracks soreness and pain in structured fields instead of loose free text.
- Keeps business rules in shared packages so platform expansion does not require a logic rewrite.
Core product surfaces
Structured symptom flow
Check-ins capture body area, severity, onset, and aggravating factors with conservative handling.
Safety escalation
Rule outcomes include caution and stop-and-refer behavior instead of diagnosis claims.
Session workflow
Active session, quick set presets, swaps, and recap are wired for lower logging friction.
Planner and analytics
Weekly plan controls and drill-down analytics pages are included in the current repo state.
Cross-platform base
iPhone-first client design with iPad-aware layouts and Android expansion plan on the same codebase.
Observed workflow arcs
Daily readiness loop
Check-in data updates local state, then deterministic generation composes a session with explanation lines.
In-session adaptation loop
Users can log sets quickly, adjust rest timing, swap exercises, and finish with a structured recap.
Weekly planning loop
Planner generation, day-level navigation, and quick edits support short-horizon structure.
Evidence review loop
Analytics and symptom surfaces connect training load with pain trends for practical reflection.
Visual reference
No in-repo UI screenshot set was found. This export uses existing public-safe assets and documented functionality.
Proof, trust, and availability
Meaningful commit window
2026-03-12 to 2026-03-17
From current git history in this repo.
Documented logic cases
5 deterministic example cases
Listed in rule-engine docs and README.
Current validation state
Workspace test, typecheck, and build passing
Latest local run date: 2026-03-17.
Platform posture
iPhone-first, iPad-aware, Android-ready
Based on architecture and expansion docs.
FAQ
Is this a released consumer app?
This repository presents a production-oriented scaffold and active implementation work. Public app store release status is not stated in this repo.
Does it use LLM-generated workout advice?
The documented approach is deterministic and rule-based, with explicit safety logic and explanation outputs.
Which platforms are evidenced here?
The repo documents an iPhone-first client with iPad support and an Android expansion path using the same shared codebase.
Where do safety decisions live?
Safety behavior is represented in the rule-engine layer and supported by structured symptom models and tests.
Where can I inspect the architecture and contracts?
See the docs directory in the repo, especially architecture, API contract, database schema, and rule-engine design docs.
Explore the build artifacts directly
If you want to evaluate this project quickly, start with README, architecture docs, and the rules engine package.