section11Evidence-based endurance coaching protocol (v11.35). Use when analyzing training data, reviewing sessions, generating pre/post-workout reports, planning work...
Install via ClawdBot CLI:
clawdbot install crankaddict/section11Grade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://github.com/CrankAddict/section-11#2-set-up-your-data-mirror-optional-butUses known external API (expected, informational)
raw.githubusercontent.comAudited Apr 18, 2026 · audit v1.0
Generated Mar 12, 2026
An amateur cyclist uses the skill to analyze their latest training data from Intervals.icu, fetching latest.json for a 7-day snapshot and history.json for trends. The AI coach generates a pre-workout report assessing readiness and providing a Go/Modify/Skip recommendation based on CTL, ATL, and TSB metrics, helping optimize daily workouts for endurance goals.
A triathlete integrates the skill with their private GitHub repo to sync training data automatically. The AI coach reviews longitudinal data from history.json to track progress over 90 days, generates post-workout reports with compliance metrics, and uses the protocol to plan polarized training phases, enhancing performance for upcoming races.
A recreational rider configures the heartbeat feature to receive daily wellness observations and weather checks. The skill analyzes latest.json for training load and fatigue metrics, providing brief reports when normal and detailed insights when thresholds are breached, supporting consistent training without overexertion.
A cycling club coach sets up the skill for multiple athletes, each with their own dossier and data sources. The AI coach fetches individual JSON data to generate tailored pre- and post-workout reports, helping manage group training schedules and offering evidence-based advice on interval training and recovery.
Researchers use the skill to analyze anonymized training data from studies, leveraging its protocol for standardized metrics like ACWR and RI. By fetching history.json for longitudinal comparison, they can investigate trends in endurance training effectiveness, citing frameworks per the validation checklist for rigorous analysis.
Offer tiered subscriptions where users pay monthly for access to the AI coaching protocol, personalized reports, and heartbeat automation. Revenue comes from premium features like advanced trend analysis, integration with multiple fitness platforms, and priority support, targeting serious athletes seeking data-driven guidance.
Provide the core skill for free, including basic data fetching and report generation. Monetize through add-ons such as custom dossier templates, extended heartbeat configurations, or integration with premium data sources like weather APIs. This attracts beginners while upselling to advanced users needing more functionality.
License the skill's protocol and templates to fitness apps, cycling software, or wearable companies. Revenue is generated through licensing fees or revenue-sharing agreements, enabling these platforms to embed evidence-based coaching features into their products, enhancing user engagement and retention.
💬 Integration Tip
Ensure users set up JSON data sources correctly, either locally or via private GitHub repos, and complete the dossier and heartbeat config before coaching to avoid errors in data fetching and analysis.
Scored May 17, 2026
Generate study materials. Use when creating study plans, quizzes, flashcards, tracking progress, or scheduling review sessions.
Loads any thinker's, leader's, philosopher's, or organization's complete mental operating system directly into the AI — so the AI reasons FROM inside that co...
Configures Rocq environments, runs preflight checks, and guides the proving workflow for OpenMath Rocq theorems. Use when the user wants to set up Rocq tooli...
Use when performing study tasks on browser-based platforms such as Yuketang, Xuexitong, Zhihuishu, and Pintia, including answering quizzes and page actions.
查询亚马逊商品的历史时序数据,包括价格走势、BSR(畅销排名)趋势、评分变化、卖家数量和月销量,支持多个亚马逊站点的任意ASIN。当用户提到价格历史、价格追踪、BSR历史、BSR趋势、历史定价、价格波动、Keepa数据、排名历史、降价提醒、秒杀历史价格、Buy Box价格趋势、优惠券价格、FBA/FBM价格对比、...
Delivers data-driven analysis on global food prices, crop supply-demand, food security, climate impacts, agritech, trade flows, and fisheries trends.