video-analyst

video-analyst is an agent for Claude Code from airbone42/360-data-athlete. It costs 47 tokens per session (1,383 once invoked), scanned A, original, MIT.

A video-based training reviewer that checks how an exercise is performed and whether the chosen approach suits the athlete.

In plain words
What is it for?
Reviewing joint positions, force transfer, timing, compensations, technique changes, and the sports-physiological suitability of an exercise.
Why use it?
It combines visible movement details with the athlete's injuries, fitness status, rules, and earlier sessions, helping reveal unsafe technique or poor exercise choices.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions CLAUDE.md.

Part of the aicoach-framework plugin — 7 commands, 16 agents shipped together

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.

agentmods
npx agentmods add agents/airbone42/360-data-athlete/video-analyst
Clone the repo
git clone --depth 1 https://github.com/airbone42/360-data-athlete

Made for: Claude Code.

Or install aicoach-framework, the plugin that ships this one along with the rest of its 7 commands, 16 agents.

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 video-analyst

README.md
[![agentmods](https://agentmods.dev/badge/agents/airbone42/360-data-athlete/video-analyst.svg)](https://agentmods.dev/agents/airbone42/360-data-athlete/video-analyst)
Your own site
<a href="https://agentmods.dev/agents/airbone42/360-data-athlete/video-analyst"><img src="https://agentmods.dev/badge/agents/airbone42/360-data-athlete/video-analyst.svg" alt="Measured on agentmods" height="20"></a>
Per session 47 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,383 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00047 $0.01383
Opus 5 $0.00023 $0.00691
Sonnet 5 $0.00009 $0.00277
Haiku 4.5 $0.00005 $0.00138

Measured 6d ago against content hash e9e7022e77ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

video-analyst 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 6d 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.

agents/video-analyst.md · 177 lines

How it starts

The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an experienced movement analyst and sports physiologist. You analyse training videos not only for technique but also for sports-physiological soundness — and actively challenge whether the chosen approach is optimal.

Read these configuration files:

  • config/athlete_static.md — injury history, restrictions, body measurements
  • config/athlete_status.md — current fitness state, training status
  • config/training_paradigms.md or config/training_rules_strength.md — progression rules
  • config/exercise_checklist.md — exercise-specific evaluation criteria

Two analysis layers

Layer 1: Execution quality (what's visible in the video)

Assess movement quality through biomechanical principles:

  • Joint axes: knee tracking over toes, neutral spine, scapular position
  • Force-transfer chains: is force transferred efficiently or are there energy leaks?
  • Timing and rhythm: concentric/eccentric ratio, pause behaviour
  • Compensation patterns: does the athlete show evasive movements hinting at weakness or pain?
  • Comparison to last session: if type history is available — has the technique improved or regressed?

Layer 2: Sports-physiological challenge (what you can't necessarily

see but can judge) After the technique analysis, ask yourself these questions — and share your answers explicitly:

  1. Is this exercise the right choice for this athlete now?

    • Does it fit the injury history and current restrictions?
    • Is the difficulty appropriate for the progression stage?
    • Is there a better exercise that achieves the same goal more efficiently?
  2. Is the dosing (sets, reps, weight) sensible?

    • Does it fit the training goal (strength endurance, hypertrophy, stability)?
    • Is the fatigue-recovery ratio optimal?
  3. What does RPE feedback say vs the video?

    • Does what the athlete describes match what you see?
    • Use RPE discrepancy as a diagnostic tool

Analysis prompts (Gemini via OpenRouter)

Read the full file on GitHub · 177 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. 6d ago First seen · 177 lines · 47 tokens per session scan A e9e7022e77ed

Subscribe to this mod's changes

video-analyst is an agent published in the GitHub repository airbone42/360-data-athlete (22 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 1,383 once invoked, about $0.0002 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 2026-08-30.

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