ddday

## What ddday Does > **Record your daily work. Migrate your data. Resume instantly on a new machine.** ddday is a personal work journal and migration toolkit. It tracks all your projects, generates daily HTML dashboards and markdown logs, and when you switch machines, packs everything into a portable bundle that any AI agent can read to immediately understand your full work context. --- ## Setup ### Step 1 — Initialize ddday Create the ddday directory and workspace config: ```bash DDDAY_HOME="$HOME/Desktop/ddday" mkdir -p "$DDDAY_HOME"/{logs,context} ``` ### Step 2 — Create workspace.json Register your projects. Edit `$DDDAY_HOME/workspace.json`: ```json { "version": "1.0.0", "updated": "YYYY-MM-DD", "projects": [ { "name": "my-project", "path": "/path/to/my-project", "type": "node", "emoji": "📦", "description": "Brief description of the project" } ] } ``` **Supported types**: `node`, `python`, `shopify`, `design`, `workspace`, `skill` ### Step 3 — Register as Claude Code skill ```bash ln -sfn "$DDDAY_HOME" "$HOME/.claude/skills/ddday" ``` ### Step 4 — Set up daily cron (optional) ```bash chmod +x "$DDDAY_HOME/generate_dashboard.py" (crontab -l 2>/dev/null; echo "2 18 * * * cd $DDDAY_HOME && python3 generate_dashboard.py") | crontab - ``` --- ## Commands ``` /ddday — Scan all projects, generate daily log + dashboard /ddday add <path> — Register a new project /ddday context — Generate AI context pack (for new AI conversations) /ddday snapshot — Full work snapshot (all data + business reports + AI memory) /ddday export — One-command migration bundle to Desktop /ddday doctor — Environment health check (run after migration) /ddday log — View latest daily log /ddday status — Quick project status (no log written) ``` --- ## Mode Recognition Parse user input to select mode: - Contains `add` + a path → **Mode 2 (Register project)** - Contains `context` → **Mode 3 (AI context pack)** - Contains `export` → **Mode 4 (Migration export)** - Contains `setup` → **Mode 5 (New machine restore)** - Contains `doctor` → **Mode 6 (Health check)** - Contains `snapshot` → **Mode 7 (Work snapshot)** - Contains `log` → Read latest log file - Contains `status` → Quick scan, no file output - Default → **Mode 1 (Daily scan + record)** --- ## Mode 1 — Daily Scan + Record (Default) ### Step 1 — Load workspace ```python import json, os DDDAY_HOME = os.environ.get("DDDAY_HOME", os.path.expanduser("~/Desktop/ddday")) ws_file = os.path.join(DDDAY_HOME, "workspace.json") with open(ws_file, "r") as f: workspace = json.load(f) projects = workspace.get("projects", []) print(f"Registered {len(projects)} projects") for p in projects: print(f" - {p['name']}: {p['path']}") ``` ### Step 2 — Scan each project For each registered project folder: ```bash PROJECT_PATH="/path/to/project" echo "=== Today's commits ===" cd "$PROJECT_PATH" && git log --oneline --since="today" 2>/dev/null || echo "Not a git repo" echo "=== Uncommitted changes ===" cd "$PROJECT_PATH" && git diff --stat 2>/dev/null echo "=== Untracked files ===" cd "$PROJECT_PATH" && git status --short 2>/dev/null | head -20 echo "=== Recently modified files (24h) ===" find "$PROJECT_PATH" -maxdepth 3 -type f -mtime -1 \ -not -path '*/node_modules/*' \ -not -path '*/.git/*' \ -not -path '*/dist/*' \ -not -name '.DS_Store' \ 2>/dev/null | head -20 ``` Also read the project's `CLAUDE.md` or `README.md` if present. ### Step 3 — Generate daily log Save scan results to `logs/YYYY-MM-DD.md`: ```markdown # ddday work log · {date} ## Overview - Today's commits: {total_commits} - Active projects: {active}/{total} - Pending changes: {pending} ## {emoji} {project_name} ({type}) - Status: {status_emoji} {status_text} - Branch: {branch} - Today's commits: {count} - {commit_messages} - Uncommitted changes: {diff_stat} ## Summary - Completed: {from git commits} - In progress: {from diff} - Next steps: {AI suggestions} ``` ### Step 4 — Generate HTML dashboard Run the dashboard generator to create `dashboard.html`: - Project cards with status indicators - Commit history per project - Stats bar (total commits, active projects, pending changes) - Light/dark theme toggle ### Step 5 — Update project overview Save `context/projects-overview.md` for AI consumption. --- ## Mode 2 — Register Project (/ddday add <path>) ### Step 1 — Validate path and detect type ```python import os path = os.path.expanduser("<user-provided-path>") has_git = os.path.isdir(os.path.join(path, ".git")) has_package = os.path.isfile(os.path.join(path, "package.json")) has_shopify = os.path.isdir(os.path.join(path, ".shopify")) has_python = os.path.isfile(os.path.join(path, "requirements.txt")) project_type = "unknown" if has_shopify: project_type = "shopify" elif has_package: project_type = "node" elif has_python: project_type = "python" ``` ### Step 2 — Add to workspace.json Append project entry, check for duplicates first. ### Step 3 — Ask for description Use AskUserQuestion to get a brief description from the user. --- ## Mode 3 — AI Context Pack (/ddday context) Generates `context/projects-overview.md` containing: - All project metadata, paths, descriptions - Recent git log per project - Key file listings - Current status Purpose: feed to any new AI conversation for instant project awareness. --- ## Mode 4 — Migration Export (/ddday export) ### What it does 1. **Generates a work snapshot** first (Mode 7) 2. **Collects all data**: - ddday directory (logs, context, config) - Report archives (e.g., `~/.shopadmin/` or custom report dirs) - Claude Code skills - Credential files (user-configured paths) - Claude project memory (`~/.claude/projects/*/memory/`) - Crontab backup - Python requirements 3. **Creates `manifest.json`**: path mappings, credentials index, cron jobs, dependencies 4. **Packs into** `~/Desktop/ddday-migration-{date}.tar.gz` 5. **Includes** `READ-ME-FIRST.md` at pack root (= the work snapshot) ### Migration bundle structure ``` ddday-migration/ ├── READ-ME-FIRST.md # Work snapshot — new AI reads this first ├── manifest.json # Path mappings, dependencies, cron ├── ddday/ # Full ddday directory ├── skills/ # Custom skill definitions ├── credentials/ # Credential files ├── claude-memory/ # Claude project memory files ├── crontab.bak # Crontab backup ├── requirements.txt # Python dependencies ├── setup.sh # One-command restore script ├── doctor.py # Health check script └── work_snapshot.py # Snapshot generator ``` --- ## Mode 5 — New Machine Restore (/ddday setup) Run on new machine after extracting the migration bundle: ```bash tar xzf ddday-migration-*.tar.gz cd ddday-migration bash setup.sh ``` ### setup.sh automatically: 1. Detects new `$HOME` and username 2. Installs missing dependencies (Homebrew, Python, Git) 3. Installs Python packages 4. Copies all files to correct locations 5. Replaces all hardcoded paths (old `$HOME` → new `$HOME`) 6. Creates skill symlinks 7. Registers cron jobs --- ## Mode 6 — Health Check (/ddday doctor) Checks 8 categories: 1. **Project paths** — Do all registered projects exist? 2. **Core files** — SKILL.md, workspace.json, scripts present? 3. **Credentials** — Required credential files in place? 4. **Python deps** — Required packages installed? 5. **Skills** — Symlinks correct, skill directories intact? 6. **Cron jobs** — Registered and scripts exist? 7. **API connectivity** — Can reach configured APIs? 8. **Path consistency** — Any stale paths from old machine? Output: traffic-light report with pass/warn/fail counts. --- ## Mode 7 — Work Snapshot (/ddday snapshot) Generates a **self-contained Markdown file** that any AI can read to immediately resume all work: 1. **User profile** — Role, tech stack, work style 2. **Project panorama** — Path, git status, README, .env keys, recent commits 3. **Business data** — Latest reports from all configured report directories 4. **AI memory** — Claude Code project memory files 5. **Recent logs** — Last 7 days of ddday logs 6. **Team handbook** — Available skills and their triggers 7. **Environment** — Cron, credentials, symlinks, system info 8. **Quick start guide** — How to immediately pick up work Output: - `context/work-snapshot-YYYY-MM-DD.md` — dated archive - `context/LATEST-SNAPSHOT.md` — latest copy (AI reads this) --- ## Full Migration Flow ### Old machine ```bash /ddday export # Auto-generates snapshot + packs everything ``` ### New machine ```bash tar xzf ddday-migration-*.tar.gz cd ddday-migration cat READ-ME-FIRST.md # AI reads this → instantly knows all your work bash setup.sh # Auto-restore environment python3 doctor.py # Health check /ddday # Start working ``` --- ## Configuration ddday uses `$DDDAY_HOME` (defaults to `~/Desktop/ddday`) as its root: ``` $DDDAY_HOME/ ├── SKILL.md # This skill definition ├── workspace.json # Project registry ├── generate_dashboard.py # Dashboard generator ├── work_snapshot.py # Snapshot generator ├── export.py # Migration exporter ├── setup.sh # New machine restore ├── doctor.py # Health check ├── run_ddday.sh # Cron entry point ├── dashboard.html # Latest dashboard (auto-generated) ├── logs/ # Daily logs (YYYY-MM-DD.md) └── context/ # AI context files ├── projects-overview.md ├── LATEST-SNAPSHOT.md └── work-snapshot-*.md ``` ## Customization ### Adding report directories If you have report tools (analytics, monitoring, etc.), edit `export.py` to include their output directories in the migration bundle. The work snapshot (`work_snapshot.py`) can also be extended to read from custom report paths. ### Extending project types Add new type detection logic in Mode 2 by checking for framework-specific files (e.g., `Cargo.toml` for Rust, `go.mod` for Go).