What is log-training?

A workout logging workflow that records exercises, loads, repetitions, sets, volume, and training notes in a daily journal.

It helps log gym sessions, calculate exercise volume, identify new top loads, and publish only approved training metrics to public pages.

It keeps workout details consistent and makes it possible to compare a session with the previous session and earlier best performance.

How aligundogdu/bite-ai-dietitian configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to aligundogdu/bite-ai-dietitian. 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/aligundogdu/bite-ai-dietitian/main/.claude/skills/log-training/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/aligundogdu/bite-ai-dietitian

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/aligundogdu/bite-ai-dietitian/log-training/github.svg)](https://agentmods.dev/skills/aligundogdu/bite-ai-dietitian/log-training)

Your own site

<a href="https://agentmods.dev/skills/aligundogdu/bite-ai-dietitian/log-training"><img src="https://agentmods.dev/badge/skills/aligundogdu/bite-ai-dietitian/log-training/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/aligundogdu/bite-ai-dietitian/log-training"><img src="https://agentmods.dev/badge/skills/aligundogdu/bite-ai-dietitian/log-training.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 383 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.

Model	Per session	Once invoked
Fable 5.1	$0.00052	$0.00383
Opus 5.5	$0.00021	$0.00153
Sonnet 5.5	$0.00010	$0.00077
Haiku 4.5	$0.00005	$0.00038

Measured 9d ago against content hash 6e527da6817a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

log-training 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.

.claude/skills/log-training/SKILL.md · 26 lines

What it actually says

Log a training session

Private journal (daily log)

Add a ## Training section to the day's file:

warm-up, blocks/supersets/circuits as the user did them, cool-down
one table: exercise, load, reps × sets, volume (load × reps × sets; dumbbells per hand count ×2), note ("top load equalled", "new top")
how it felt, heart rate, anything physical: private only

Compare with the last session of the same type and with the exercise's best so far. Report new tops plainly. Don't suggest replacement exercises: the program or the coach decides.

Public metrics (bite/, optional)

Write bite/src/content/training/YYYY-MM-DD.md following bite/CONTENT_GUIDE.md:

metrics only: exercise, weight, reps, sets, section (main / circuit), scheme for non-load work ("500 m", "2 min"), week code, block counter, warm-up / cool-down
never: heart rate, symptoms, medications, body measurements, how the user felt physically
use canonical exercise names (same spelling every time) so the per-exercise pages and charts link up
if loads were not reported for part of the session, set incomplete: true so the volume chart doesn't show a fake drop

If the user follows a numbered block (e.g. a 12-session coaching package), update bite/src/data/blocks.json (done, and closed + new block when it ends).

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.

9d ago First seen · 26 lines · 52 tokens per session scan A 6e527da6817a

Subscribe to this mod's changes

log-training is a skill published in the GitHub repository aligundogdu/bite-ai-dietitian (5 stars, last pushed 9d ago), licensed MIT. It adds 52 tokens to every session and 383 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-19.

Analytics and metrics

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bite-ai-dietitian: Skill for Claude Code