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intervals-icu-mcp: Skill for Claude Code

.agents/skills/predict-race/SKILL.md

predict-race is a skill for Claude Code, Codex from hadidwirsty/intervals-icu-mcp. It costs 60 tokens per session (1,193 once invoked), scanned A, original, MIT.

A race-time estimation and taper-planning tool for distances from 5K to marathon. Tapering is the planned reduction in training before a race so the athlete can recover while retaining fitness.

In plain words
What is it for?
Use it to estimate finish time and target pace, then plan a two- to three-week taper for a target race date.
Why use it?
It turns a recent race result or VDOT value, a running-performance score, into an estimated finish time and a shorter pre-race training plan.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is hadidwirsty/intervals-icu-mcp's own configuration. It tells Claude Code and Codex how to work on intervals-icu-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything intervals-icu-mcp configures →

not rated 1repo yesterday A scan 60 tokens original MIT
View source ↗ hadidwirsty/intervals-icu-mcp
Reuse

Borrowing it

Nothing to install: this file belongs to hadidwirsty/intervals-icu-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/hadidwirsty/intervals-icu-mcp/main/.agents/skills/predict-race/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/hadidwirsty/intervals-icu-mcp

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for predict-race

README.md
[![agentmods](https://agentmods.dev/badge/skills/hadidwirsty/intervals-icu-mcp/predict-race/github.svg)](https://agentmods.dev/skills/hadidwirsty/intervals-icu-mcp/predict-race)
Your own site
<a href="https://agentmods.dev/skills/hadidwirsty/intervals-icu-mcp/predict-race"><img src="https://agentmods.dev/badge/skills/hadidwirsty/intervals-icu-mcp/predict-race/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for predict-race

Your own site · 80×15
<a href="https://agentmods.dev/skills/hadidwirsty/intervals-icu-mcp/predict-race"><img src="https://agentmods.dev/badge/skills/hadidwirsty/intervals-icu-mcp/predict-race.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,193 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00060 $0.01193
Opus 5 $0.00030 $0.00596
Sonnet 5 $0.00012 $0.00239
Haiku 4.5 $0.00006 $0.00119

Measured yesterday against content hash c84f92630291, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

predict-race scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/predict-race/SKILL.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: /predict-race

Skill ini digunakan untuk menghitung estimasi waktu finish race, target pace rata-rata, dan menyusun rencana tapering penurunan volume mingguan.

1. Input Kebutuhan

Mintalah informasi berikut dari atlet (jika belum tersedia):

  • Jarak Race: 5k, 10k, half_marathon (21.1 km), full_marathon (42.2 km), atau jarak kustom (km).
  • Nilai VDOT: Hasil tes VDOT atau estimasi dari kalkulator /calc-vdot.
  • Target Tanggal Race: Tanggal pelaksanaan Race A (format YYYY-MM-DD).

2. Langkah Pengambilan & Kalkulasi Data via MCP

  1. Penentuan Nilai VDOT:

    • Opsi A (Dari Riwayat Race / Time Trial): Panggil MCP Tool calculate_vdot dengan argumen { raceTime: "MM:SS" / "HH:MM:SS", distanceKm: X } (misal 10K dalam 48:30 menghasilkan VDOT ~42.3).
    • Opsi B (Diberikan Langsung oleh Atlet): Gunakan angka VDOT yang diinput atlet (misal VDOT 50).
    • Opsi C (Fallback Profil): Panggil get_athlete_profile atau get_fitness_chart untuk melihat metrik eftp / threshold_pace sebagai estimasi.

  2. get_fitness_chart:

    • startDate: 42 hari lalu.
    • endDate: Hari ini.
    • cols: ctl,atl,tsb
    • Ambil nilai ctl (Kebugaran Kronis 42 hari) dan tsb (Form / Kesiapan Akut).

  3. predict_race_time:

    • Argument: { vdot, targetDistanceKm, ctl, tsb }
    • Dapatkan: predictedTimeFormatted, predictedPaceFormatted, ctlAdjustmentFactor, dan tsbAdjustmentFactor.

  4. calculate_taper_plan:

    • Argument: { raceDate, currentCtl: ctl, currentTsb: tsb, taperWeeks: 2, racePriority: "A" | "B" | "C" }
    • Opsi racePriority:
      • "A" (Default): Full Taper (10–14 hari / 2–3 minggu). Frekuensi lari tetap, volume dipotong ke 75% lalu 50%, repetisi interval dipotong ~50%, target RPE finish 9–10/10.
      • "B": Mini Taper (4–6 hari). Volume ~85%, eliminasi severe stressor (VO₂max), aturan jeda minimal 4 hari dari sesi Subthreshold, target RPE finish 8–9/10 (CP Test / tune-up).
      • "C": No Taper (0 hari). Volume 100%, protokol tukar 1 hari hard workout menjadi Easy Run, target RPE finish 6–7/10, doktrin Joe Friel (anti-upgrade all-out).
    • Dapatkan: racePriority, taperTypeDescription, targetFinishRpe, weeklySchedule, subthresholdGapRule, dan egoManagementRule.

Read the full file on GitHub · 74 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 74 lines · 60 tokens per session scan A c84f92630291

Subscribe to this mod's changes

predict-race is a skill published in the GitHub repository hadidwirsty/intervals-icu-mcp (1 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 1,193 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-10.

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