section-11: Evidence-based AI coach for endurance training

by CrankAddict · Codex Skill · ★ 164

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 68/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-coachai-coachingcyclingcycling-coachdeterministic-aiendurance-traininghermes-agentintervals-icuopenclawperiodization

Quick Facts

Stars164
Forks84
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score68/100
Last Updated2026-11-04
Created2026-01-20
Platformspython
Est. Tokens~21k

About section-11

Section 11 — AI Coaching Protocol An open protocol for deterministic, auditable AI-powered endurance coaching. Built for athletes who want AI coaches that follow science, not speculation. What Is This? Section 11 is a structured framework that enables AI systems (ChatGPT, Claude, Gemini, Grok, Mistral, etc.) to provide evidence-based endurance training advice with full auditability and...

Compatible Skills

These tools work well together with section-11 for enhanced workflows:

  • trainingpeaks-mcp — semantic(0.35)+complementary+rare_topics+similar_pop+shared_platform (61%)
  • ComfyUI-Expert — semantic(0.20)+complementary+similar_pop+shared_platform (47%)
  • create-llm — semantic(0.33)+complementary+similar_pop+shared_platform (47%)

section-11 alternative? Top 6 similar tools

Looking for a section-11 alternative? If you're comparing section-11 with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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    Deterministic endurance coaching engine using Intervals.icu data, governed training logic, performance intelli

  • kindly-web-search-mcp-server by Shelpuk-AI-Technology-Consulting · ⭐ 398

    Kindly Web Search MCP Server: Web search + robust content retrieval for AI coding tools (Claude Code, Codex, C

  • ComfyUI_Skills_OpenClaw by HuangYuChuh · ⭐ 395

    Agent-friendly ComfyUI workflow skills for OpenClaw, Hermes Agent, Codex, and Claude Code; complementary to Co

  • signetai by Signet-AI · ⭐ 314

    Sync and store memories, transcripts, institutional knowledge, and secrets between all of your favorite harnes

  • memex by nicosuave · ⭐ 305

    Search Claude Code, Codex, Pi, OpenCode, Github Copilot & Cursor transcripts. Resume sessions. Track tokens.

  • clawshell by clawshell · ⭐ 300

    The Runtime Security Layer for OpenClaw/Hermes-agent, the essential safety harness for PII & sensitive credent

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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