sparky
SparkyFitness CLI for food diary, exercise tracking, biometric check-ins, and health summaries.
Best use case
sparky is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
SparkyFitness CLI for food diary, exercise tracking, biometric check-ins, and health summaries.
Teams using sparky should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/sparkyfitness/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How sparky Compares
| Feature / Agent | sparky | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
SparkyFitness CLI for food diary, exercise tracking, biometric check-ins, and health summaries.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# sparky Use `sparky` to interact with a self-hosted SparkyFitness server — log food, exercise, weight, steps, and mood. Install - Homebrew (macOS/Linux): `brew tap aronjanosch/tap && brew install sparky-cli` - Build from source (requires Go 1.21+): ``` git clone https://github.com/aronjanosch/sparky-cli cd sparky-cli go build -o sparky . sudo mv sparky /usr/local/bin/ ``` Setup (once) - `sparky config set-url <url>` — e.g. `sparky config set-url https://sparky.example.com` - `sparky config set-key <key>` - `sparky config show` - `sparky ping` — verify connection Food - Search: `sparky food search "chicken breast" [-l 10]` — local DB first, falls back to Open Food Facts; shows Brand column - Search by barcode: `sparky food search --barcode 4061458284547` — exact product lookup, no ambiguity - Log by name: `sparky food log "chicken breast" -m lunch -q 150 -u g [-d YYYY-MM-DD]` - Log by barcode: `sparky food log --barcode 4061458284547 -m lunch -q 113 -u g` — most reliable, no brand guessing - Log by ID: `sparky food log --id <uuid> -m lunch -q 150 -u g` — skips search, unambiguous - Pick result: `sparky food log "Hähnchenbrust" --pick 2` — select Nth search result instead of defaulting to results[0] - Diary: `sparky food diary [-d YYYY-MM-DD]` - Delete entry: `sparky food delete <uuid>` - Remove from library: `sparky food remove <external_id>` — purge a wrongly imported product from local library Exercise - Search: `sparky exercise search "bench press" [-l 10]` — local DB first, falls back to Free Exercise DB - Search external only: `sparky exercise search --external "pushup"` — bypasses local cache - Log by name: `sparky exercise log "Pushups" [--duration 45] [--calories 400] [-d YYYY-MM-DD]` - Log by ID: `sparky exercise log --id <uuid> --set 10x80@8 --set 10x80@9` — skips search, unambiguous - Sets format: `REPS[xWEIGHT][@RPE]` — e.g. `10x80@8` = 10 reps, 80 kg, RPE 8; `10x80` or `10@8` also valid - Diary: `sparky exercise diary [-d YYYY-MM-DD]` - Delete: `sparky exercise delete <uuid>` Check-ins - Weight: `sparky checkin weight 75.5 [-u kg|lbs] [-d YYYY-MM-DD]` - Steps: `sparky checkin steps 9500 [-d YYYY-MM-DD]` - Mood: `sparky checkin mood 8 [-n "notes"] [-d YYYY-MM-DD]` - Diary: `sparky checkin diary [-d YYYY-MM-DD]` — shows biometrics + mood together Summary & trends - `sparky summary [-s YYYY-MM-DD] [-e YYYY-MM-DD]` — nutrition/exercise/wellbeing totals (default: last 7 days) - `sparky trends [-n 30]` — day-by-day nutrition table Agentic workflow (always prefer --id to avoid ambiguity) Exercise — search first, then log by ID: ``` # 1. Find candidates; use --external to bypass local cache if needed sparky -j exercise search --external "pushup" # Each result has is_local: true/false # is_local: true → id is a UUID → use --id directly # is_local: false → id is a source string → log by exact name to import first, # then search again to get the UUID # 2a. Local exercise sparky -j exercise log --id <uuid> --set 3x10@8 # 2b. External exercise (import on first log, then switch to --id) sparky -j exercise log "Pushups" --set 3x10 sparky -j exercise search "Pushups" # now is_local: true sparky -j exercise log --id <uuid> --set 3x10 ``` Food — preferred agentic workflow: ``` # Option A: barcode (most reliable) sparky food log --barcode 4061458284547 -q 113 -u g -m lunch # Option B: search → inspect brand+macros → log by --id sparky -j food search "Hähnchenbrust" # check brand + calories in results; pick the right one sparky food log --id <uuid> -q 400 -u g -m dinner # Option C: search with --pick N (when brand column shows the right one) sparky food log "Hähnchenbrust" --pick 3 -q 400 -u g -m dinner # Remove a bad import from local library sparky food remove <external_id> # external_id from search results ``` Notes - `-j` / `--json` is a **root-level flag**: `sparky -j food diary`, not `sparky food diary -j` - Always verify brand in search results before logging — Open Food Facts has many products with identical names - `--barcode` is the most reliable option when the product has a scannable barcode - `--pick N` selects the Nth result (1-based); exact local match bypasses `--pick` entirely - Both search commands fall back to online providers automatically; matches are added to your library on first log - Weight is stored in kg; lbs are auto-converted (`166 lbs → 75.30 kg`) - Full UUIDs for delete: `sparky -j food diary | jq '.[0].id'` - Meal options: `breakfast`, `lunch`, `dinner`, `snacks` (default: `snacks`)
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