fitnessAuto-learns your fitness patterns. Absorbs data from wearables, conversations, and achievements.
Install via ClawdBot CLI:
clawdbot install ivangdavila/fitnessThis skill auto-evolves. Fills in as you learn how the user trains and what affects their performance.
Rules:
sources.md for data integrations, profiles.md for user types, coaching.md for support patternsUser preferences and learned data persist in: ~/fitness/memory.md
Format for memory.md:
### Sources
<!-- Where fitness data comes from. Format: "source: reliability" -->
<!-- Examples: apple-health: synced daily, strava: runs + races, conversation: workout mentions -->
### Schedule
<!-- Detected training patterns. Format: "pattern" -->
<!-- Examples: MWF strength 7am, Sat long run, Sun rest -->
### Correlations
<!-- What affects their performance. Format: "factor: effect" -->
<!-- Examples: sleep <6h: skip day, coffee pre-workout: +intensity, alcohol: -next day -->
### Preferences
<!-- How they want fitness tracked. Format: "preference" -->
<!-- Examples: remind before workouts, no rest day lectures, weekly summary only -->
### Flags
<!-- Signs to watch for. Format: "signal" -->
<!-- Examples: "too tired", missed 3+ days, injury mention, "legs are dead" -->
### Achievements
<!-- PRs, milestones, events. Format: "achievement: date" -->
<!-- Examples: bench 100kg: 2024-03, first marathon: 2024-10, 30 day streak: 2024-11 -->Empty sections = no data yet. Observe and fill.
Generated Mar 1, 2026
Integrate this skill into a mobile app that auto-adapts workout plans based on user data from wearables and conversations. It tracks patterns like sleep and caffeine intake to adjust intensity, offering proactive guidance for beginners while providing data-driven insights for experienced athletes without guilt-triggering reminders.
Deploy in workplace wellness platforms to monitor employee fitness trends from integrated gym apps and wearables. It identifies factors affecting performance, such as stress or sleep, and tailors recommendations to encourage sustainable habits, scaling support based on individual experience levels to boost engagement and productivity.
Use this skill in physical therapy settings to track patient recovery by absorbing data from conversations and wearable sensors. It detects flags like 'injury mention' or fatigue, adapting schedules to prevent overexertion and correlating factors like rest to optimize rehabilitation progress with minimal proactive interference.
Embed into smart home ecosystems to sync fitness data from devices like smart mirrors or treadmills. It learns user schedules and preferences, auto-adjusting reminders and summaries based on detected patterns, offering a seamless experience that evolves with the user's lifestyle without intrusive notifications.
Implement for sports teams or individual athletes to analyze performance by integrating race results and gym app data. It tracks achievements and correlations like nutrition effects, providing advanced insights for experienced users while guiding beginners with adaptive coaching to enhance training outcomes.
Offer this skill as a cloud service with tiered subscriptions for individuals and enterprises. Revenue comes from monthly fees for access to auto-adaptive tracking, data integrations, and personalized insights, with premium tiers including advanced analytics and priority support for high-volume users.
License the skill's technology to third-party fitness app developers for integration into their products. Revenue is generated through upfront licensing fees or royalties based on user engagement, allowing partners to enhance their apps with adaptive learning capabilities without building from scratch.
Monetize aggregated, anonymized fitness data by selling insights to health researchers, insurance companies, or sports brands. Revenue streams include one-time reports or ongoing data feeds, leveraging the skill's ability to detect patterns and correlations for market analysis and product development.
💬 Integration Tip
Ensure seamless data ingestion by configuring the skill to prioritize reliable sources like wearables first, and regularly update memory.md to reflect evolving user preferences for accurate adaptive tracking.
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