القائمة
SparkyFitness CLI for food diary, exercise tracking, biometric check-ins, and health summaries.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/GeorgeDoors888/GB-Power-Market-JJ --skill sparkyيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
超级简历 WonderCV 出品，3000 万用户信赖。简历分析、段落改写、JD 岗位匹配、自动匹配职位、PDF 导出、AI 求职导师（面试准备/薪资谈判/职业规划/多版本简历策略）。 触发条件：用户提供简历、要求简历点评/打分/反馈、希望改写某个简历部分、 希望将简历与岗位 JD 匹配、咨询求职建议或面试准备，或提到 CV/简历/求职。 不触发条件：用户讨论普通写作（非简历）、询问其他文档， 或讨论与求职和职业发展无关的话题。
Order food/drinks (点餐) on an Android device paired as an OpenClaw node. Uses in-app menu and cart; add goods, view cart, submit order (demo, no real payment).
调用久吾智能体API进行文本或文件分析处理。支持两种调用方式：(1) 文本内容分析 - 传入name(智能体名称)、docno(文档编号)、content(文本内容)；(2) 文件分析 - 传入name、docno和files(文件列表)进行智能评审。适用于合同评审、需求评审、文档审查等场景。当用户要求评审合同、分析条款、审查文档、需求评审、合同条款分析、或需要对文本和文件进行AI智能分析时触发。
| name | sparky |
| description | SparkyFitness CLI for food diary, exercise tracking, biometric check-ins, and health summaries. |
| homepage | https://github.com/CodeWithCJ/SparkyFitness |
| metadata | {"clawdbot":{"emoji":"🏃","requires":{"bins":"[Truncated]"}}} |
Use sparky to interact with a self-hosted SparkyFitness server — log food, exercise, weight, steps, and mood.
Install
brew tap aronjanosch/tap && brew install sparky-cligit 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.comsparky config set-key <key>sparky config showsparky ping — verify connectionFood
sparky food search "chicken breast" [-l 10] — local DB first, falls back to Open Food Facts; shows Brand columnsparky food search --barcode 4061458284547 — exact product lookup, no ambiguitysparky food log "chicken breast" -m lunch -q 150 -u g [-d YYYY-MM-DD]sparky food log --barcode 4061458284547 -m lunch -q 113 -u g — most reliable, no brand guessingsparky food log --id <uuid> -m lunch -q 150 -u g — skips search, unambiguoussparky food log "Hähnchenbrust" --pick 2 — select Nth search result instead of defaulting to results[0]sparky food diary [-d YYYY-MM-DD]sparky food delete <uuid>sparky food remove <external_id> — purge a wrongly imported product from local libraryExercise
sparky exercise search "bench press" [-l 10] — local DB first, falls back to Free Exercise DBsparky exercise search --external "pushup" — bypasses local cachesparky exercise log "Pushups" [--duration 45] [--calories 400] [-d YYYY-MM-DD]sparky exercise log --id <uuid> --set 10x80@8 --set 10x80@9 — skips search, unambiguousREPS[xWEIGHT][@RPE] — e.g. 10x80@8 = 10 reps, 80 kg, RPE 8; 10x80 or 10@8 also validsparky exercise diary [-d YYYY-MM-DD]sparky exercise delete <uuid>Check-ins
sparky checkin weight 75.5 [-u kg|lbs] [-d YYYY-MM-DD]sparky checkin steps 9500 [-d YYYY-MM-DD]sparky checkin mood 8 [-n "notes"] [-d YYYY-MM-DD]sparky checkin diary [-d YYYY-MM-DD] — shows biometrics + mood togetherSummary & 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 tableAgentic 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--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 entirely166 lbs → 75.30 kg)sparky -j food diary | jq '.[0].id'breakfast, lunch, dinner, snacks (default: snacks)