workout-logger

workout-logger is a skill for Claude Code, Codex from malue-ai/dazee-small. It costs 21 tokens per session (791 once invoked), scanned A, original, MIT.

A local workout log that records exercises, distances, times, and progress. It can summarize the saved data in fitness reports.

In plain words
What is it for?
Use it to log strength training and cardio, compare results with earlier sessions, and review weekly activity reports.
Why use it?
It removes the need to remember workouts or maintain a separate spreadsheet. Keeping the records locally means the data is stored on the user's computer.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to log strength training and cardio, compare results with earlier sessions, and review weekly activity reports.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/malue-ai/dazee-small/workout-logger
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add malue-ai/dazee-small --skill workout-logger
Clone the repo
git clone --depth 1 https://github.com/malue-ai/dazee-small

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 workout-logger

README.md
[![agentmods](https://agentmods.dev/badge/skills/malue-ai/dazee-small/workout-logger/github.svg)](https://agentmods.dev/skills/malue-ai/dazee-small/workout-logger)
Your own site
<a href="https://agentmods.dev/skills/malue-ai/dazee-small/workout-logger"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/workout-logger/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 workout-logger

Your own site · 80×15
<a href="https://agentmods.dev/skills/malue-ai/dazee-small/workout-logger"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/workout-logger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 791 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 $0.00021 $0.00791
Opus 5 $0.00010 $0.00396
Sonnet 5 $0.00004 $0.00158
Haiku 4.5 $0.00002 $0.00079

Measured 9d ago against content hash f435b342e97f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

workout-logger 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 9d ago.

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.

instances/xiaodazi/skills/workout-logger/SKILL.md · 98 lines

What it actually says

健身记录

帮助用户记录运动和锻炼,追踪进步,生成健身报告。数据保存在本地。

使用场景

  • 用户说「记录今天的运动」「跑了 5 公里」
  • 用户说「今天做了卧推 60kg 3组」
  • 用户说「这周运动了几次」「看看我的运动记录」
  • 用户说「我的深蹲进步了多少」

执行方式

数据存储

在用户数据目录维护 ~/Documents/xiaodazi/workouts.json

{
  "logs": {
    "2026-02-26": [
      {
        "type": "strength",
        "exercises": [
          {"name": "卧推", "sets": [{"weight": 60, "reps": 8}, {"weight": 60, "reps": 8}, {"weight": 55, "reps": 10}]},
          {"name": "深蹲", "sets": [{"weight": 80, "reps": 6}, {"weight": 80, "reps": 6}]}
        ],
        "duration_min": 45,
        "notes": "状态不错"
      }
    ],
    "2026-02-25": [
      {
        "type": "cardio",
        "activity": "跑步",
        "distance_km": 5.2,
        "duration_min": 28,
        "pace": "5:23/km"
      }
    ]
  }
}

记录流程

力量训练

用户:今天卧推 60kg 做了 3 组,每组 8 个
→ 记录:卧推 60kg × 8 × 3 组
→ 回复:卧推已记录 ✅  60kg × 8 × 3
→ 对比上次:上次 55kg × 8,进步了 5kg 💪

有氧运动

用户:刚跑了 5 公里,用了 28 分钟
→ 记录:跑步 5km / 28min / 配速 5:36/km
→ 回复:跑步已记录 ✅  5km 28分钟(配速 5:36/km)

周报模板

## 运动周报(2/19 - 2/25)

本周运动 **4 次**,总时长 **160 分钟**

| 日期 | 类型 | 内容 | 时长 |
|---|---|---|---|
| 周一 | 力量 | 胸+三头 | 50min |
| 周三 | 有氧 | 跑步 5km | 28min |
| 周五 | 力量 | 背+二头 | 55min |
| 周六 | 有氧 | 跑步 6km | 32min |

### PR 记录(个人最佳)
- 卧推:60kg × 8(+5kg ↑)
- 跑步 5km:28:00(-1:30 ↓)

输出规范

  • 记录后简洁确认,对比上次数据
  • 有进步时积极鼓励
  • 长时间未运动时温和提醒,不施压
  • 自动计算配速、组间容量等衍生指标
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. 9d ago First seen · 98 lines · 21 tokens per session scan A f435b342e97f

Subscribe to this mod's changes

workout-logger is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 791 once invoked, about $0.0001 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 2026-09-03.

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