trainlog/AGENTS.md

Trainlog Development Contract

1. Scope

Trainlog consists of:

• a native Android application for fast workout and body-data capture;
• a Unix/Linux C17 terminal TUI for durable history, correction, analysis, visualization, and manual synchronization. The current authorized TUI_NOTCURSES_V1 infrastructure tranche migrates the active rendering/input backend from legacy ncursesw to Notcurses without changing product semantics;
• a small user-session PC agent, trainlog-syncd, for Android-triggered synchronization;
• versioned JSON synchronization artifacts exchanged over direct MTP.

The desktop SQLite database is the canonical long-term history.

Android has an independent local SQLite store for offline capture. SQLite database files are never synchronized directly.

2. Frozen compatibility boundary

TRAINLOG_FORMAT_V1 is frozen.

A published format version must never receive an incompatible semantic change.

New synchronization or domain needs use separate, explicitly versioned artifacts. Do not overload frozen v1 through notes, fake sets, or silent data loss.

3. Exercise model

Exercise behavior is metadata-driven:

recording_mode = SETS | CONTINUOUS
tracking_mode  = REPS | DURATION
data_fields    = bounded supplemental field mask

Valid model-v1 combinations are:

SETS + REPS
SETS + DURATION
CONTINUOUS + DURATION

CONTINUOUS + REPS is invalid.

Continuous activity must not be represented as a fake performed set.

Actual set values are independent records. Heterogeneous repetitions are valid.

4. Identity

Stable identities use UUIDv4-based creator IDs:

ex_<uuid-v4>   exercise
se_<uuid-v4>   session
bo_<uuid-v4>   body observation
sy_<uuid-v4>   synchronization run

Display names are not identities.

Import and synchronization paths must remain idempotent by stable IDs.

Android exercise editing preserves exercise_id: a rename trims and re-normalizes display metadata in the existing row and must not create a second exercise. Profile changes are rejected once completed history or an active draft references the exercise; renaming remains safe. Same-ID catalog reconciliation updates name metadata in place and rejects different-ID normalized-name collisions.

5. Desktop implementation

The desktop core is C17.

Current primary dependencies:

• SQLite3;
• utf8proc;
• libuuid;
• libudev;
• libmtp;
• Meson;
• Ninja;
• one active desktop terminal backend.

For the completed TUI_NOTCURSES_V1 infrastructure checkpoint:

legacy backend = ncursesw (historical only)
active backend = Notcurses

TUI_NOTCURSES_V1=PASS. Active desktop TUI code and build wiring use Notcurses and do not retain ncursesw as an unused compatibility backend.

Business logic, persistence, transport, and rendering remain separated.

SQLite operations must not be scattered through rendering code.

Important business rules must not depend directly on ncurses, Notcurses, or terminal-library-specific key constants.

Strict warning policy must not be weakened to make a change compile.

Desktop TUI backend contract

The terminal library is infrastructure, not product semantics.

The active TUI backend must preserve:

public entry point: trainlog_tui_run(TrainlogDatabase *)
minimum terminal: 72x20
small-terminal fallback
keyboard-first navigation
UTF-8 text input
resize recovery
semantic color roles
all existing screen/workflow behavior

Terminal-library state must not become process-global application state.

Application screen logic should consume Trainlog-owned key/input semantics rather than raw backend-specific KEY_*/event constants.

The Notcurses migration may modernize rendering with true color, Unicode borders, flat panels, and clearer focus/selection states, but must not change:

database schema or SQL semantics
TRAINLOG_FORMAT_V1
exercise semantics
session semantics
measured-max semantics
body-analytics semantics
sync/MTP protocols
Android behavior

After the migration is validated, canonical documentation must describe Notcurses as the active desktop TUI backend.

6. Android implementation

Android is a Kotlin/Jetpack Compose capture client.

It owns local data entry and local persistence for:

• exercise catalog entries;
• sessions;
• performed sets;
• continuous activities;
• body observations.

It is not the canonical analytics store.

The Android UI is driven by exercise metadata, never by exercise-name heuristics.

Android local SQLite schema v9 owns exactly one durable active-session draft. Every meaningful draft/form mutation is persisted by the repository. Back, backgrounding and process death never delete the draft. Home offers explicit resume; whole-draft discard requires confirmation. Final completed-session insertion and draft deletion are one transaction. Drafts are excluded from completed history and mobile export. Preserve raw partial form input and use an explicit, non-destructive migration for future Android schema changes.

Android schema v11 additionally stores optional planning metadata separately from actual occurrence data after the additive v10 -> v11 migration. Existing rows retain load_mode=none, zero rest and NULL targets. Desktop remains schema v11. The active completed-session exchange is the separate strict trainlog-mobile-export V3; V1/V2 remain readable and TRAINLOG_FORMAT_V1 remains unchanged.

7. Synchronization architecture

Desktop access to Android uses physical-device discovery with libudev and direct object access with libmtp.

Do not introduce a mandatory GVFS/FUSE mount.

Canonical exchange folder:

Download/Trainlog

The shared desktop synchronization engine is:

trainlog_sync_run()

Both the TUI and trainlog-syncd use this engine.

Android-triggered synchronization uses:

trainlog-sync-request-v1.json
trainlog-sync-receipt-v1.json

Android -> PC data uses:

trainlog-mobile-export-v1.json

PC -> Android catalog data uses:

trainlog-pc-catalog-v1.json

8. Persistence

Desktop SQLite schema is versioned with:

PRAGMA user_version;

The current desktop schema is v9. Schema v9 adds an occurrence-owned explicit maximum result without changing TRAINLOG_FORMAT_V1.

Every incompatible schema evolution requires an explicit migration and regression coverage.

Foreign keys must be enabled.

Multi-row mutations that represent one user operation must be transactional.

9. Error handling

Trainlog prefers explicit failure over silent corruption.

Examples:

• malformed exchange JSON -> reject;
• unsupported version -> reject;
• duplicate stable ID -> idempotent skip or explicit conflict as defined;
• incompatible exercise profile -> reject;
• incomplete session -> preserve explicitly;
• failed persisted-session replacement -> rollback;
• synchronization failure -> record a meaningful diagnostic.

User-facing code must not intentionally return placeholders such as error=unknown when a specific failure can be reported.

10. Documentation

Canonical documents:

• README.md;
• docs/current_state.md;
• docs/architecture.md;
• docs/coding_style.md;
• docs/exchange_format.md;
• docs/exercise_data_model.md;
• docs/database.md;
• docs/tui.md;
• docs/android.md;
• docs/sync_exchange.md;
• docs/tests.md;
• docs/roadmap.md;
• CHANGELOG.md.

Checkpoint history belongs in Git history and docs/reviews; canonical documents describe the current state rather than accumulating obsolete NEXT sections.

11. Validation

Before every meaningful push:

meson compile -C build
meson test -C build --print-errorlogs

python tools/validate_json.py
python tools/validate_import_contract.py

git diff --check
git status --short

When Android code changes:

cd android
JAVA_HOME=/usr/lib/jvm/java-17-openjdk ./gradlew assembleDebug

Run ASan/UBSan at meaningful C implementation checkpoints.

Hardware-dependent MTP tests remain explicit manual validations and are not required to run in CI without a connected unlocked Android device.

12. Git workflow

Forgejo is primary:

ssh://git@git.labfytools.com:2223/fy59/trainlog.git

GitHub is a mirror:

git@github.com:labfytools/trainlog.git

Do not develop directly against the GitHub mirror.

13. Definition of Done

A task is complete only when:

• behavior is implemented;
• the affected code builds without accepted warnings;
• relevant tests pass;
• new error paths are explicit;
• persistent-format changes have migrations;
• synchronization remains idempotent where applicable;
• documentation describes the resulting state;
• no known regression is intentionally left behind.

14. Training knowledge

Before domain decisions involving anatomy, biomechanics, exercise targeting, BODY ZONES, machine/exercise interpretation, substitution, MAX interpretation, workout generation or program generation, consult the project trainlog-anatomy skill, docs/domain/, the scientific catalogs in catalog/, and their cited references. Scientific knowledge is separate from runtime user data; preserve explicit uncertainty and settle scientific conclusions before encoding behavior.