Embody: Skill for Claude Code

.claude/skills/visual-aesthetics/SKILL.md

visual-aesthetics is a skill for Claude Code from dylanroscover/Embody. It costs 70 tokens per session (7,066 once invoked), scanned A, original, MIT.

A visual design guide for TouchDesigner output, including generative art, live visuals, shaders, scenes, projections, and renders.

In plain words
What is it for?
Use it before building or refining TouchDesigner visuals, especially when creating rendered images, motion graphics, or captured TOP output.
Why use it?
It helps ensure that what appears on screen has intentional composition, lighting, color, contrast, motion, and finishing.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is dylanroscover/Embody's own configuration. It tells Claude Code how to work on Embody 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 Embody configures →

Reuse

Borrowing it

Nothing to install: this file belongs to dylanroscover/Embody. 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/dylanroscover/Embody/main/.claude/skills/visual-aesthetics/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/dylanroscover/Embody

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.

agentmods badge for visual-aesthetics

README.md
[![agentmods](https://agentmods.dev/badge/skills/dylanroscover/embody/visual-aesthetics/github.svg)](https://agentmods.dev/skills/dylanroscover/embody/visual-aesthetics)
Your own site
<a href="https://agentmods.dev/skills/dylanroscover/embody/visual-aesthetics"><img src="https://agentmods.dev/badge/skills/dylanroscover/embody/visual-aesthetics/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 visual-aesthetics

Your own site · 80×15
<a href="https://agentmods.dev/skills/dylanroscover/embody/visual-aesthetics"><img src="https://agentmods.dev/badge/skills/dylanroscover/embody/visual-aesthetics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,066 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00070 $0.07066
Opus 5 $0.00035 $0.03533
Sonnet 5 $0.00014 $0.01413
Haiku 4.5 $0.00007 $0.00707

Measured 9d ago against content hash bc00eaa61a53, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

visual-aesthetics 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/visual-aesthetics/SKILL.md · 237 lines

How it starts

The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Visual Aesthetics

Make rendered image and motion output read clearly, intentionally, and with craft: deliberate composition, camera, lighting, palette, motion, and finish. This skill is about what appears on screen, not node or network layout.

Operator and parameter names in this skill are verified against the official Derivative docs (TD 2025+). Anything you add beyond them still follows td-python.md -> Verify Before Claiming.

When to Load This Skill

Load before building or refining any network whose purpose is visual output: generative art, VJ visuals, shaders, render scenes, projection content, UI visuals, or captured TOP output. Pair it with capture_top to evaluate the actual result against the rubric at the end.

Output-first: show the work in the backdrop

Before building any TOP chain, create the terminal Out TOP named out1 and position it per network-layout rules. Immediately call set_op_flags(op_path, display=True) on out1. A TOP with its Display flag on renders as the network editor backdrop when the pane's Display > Backdrop TOPs option is enabled; TD defaults this on. As soon as any stage produces pixels, wire the current chain end into out1. Keep out1 wired while iterating, including while replacing, inserting, or bypassing intermediate nodes. The user should watch progress live behind the network, not wait for a final capture. The final deliverable stays terminated in out1, matching the existing output terminal convention. Use capture_top on out1 for verification passes.

The Working Loop (mandatory)

  1. Write a 3-line intent brief before creating any operator: the subject (what the eye lands on first), the mood and palette (name one from Color, or design one), and the motion character (calm drift / pulsing energy / mechanical precision / still). If the user's request doesn't specify, propose a brief and build to it. Never start assembling operators with no target look -- that is how default-looking output happens.
  2. Build in passes, in this order: composition + camera blocking -> lighting + value -> color -> motion -> finishing. Get each pass reading correctly before decorating the next. Post cannot rescue a weak composition; color cannot rescue a weak value structure.
  3. Capture and judge after every pass: capture_top the OUTPUT TOP and actually look at the frame. Judge it against the brief AND the self-check rubric. NEVER declare a visual task done without capturing and assessing the frame -- a clean network is not success; a good-looking, correct frame is.
  4. Judge motion over time, not from one still. For anything animated: (a) let time-evolving systems SETTLE before judging -- feedback and particle systems need dozens-to-hundreds of frames, and an early capture misreads as empty or thin; (b) verify it actually animates: capture, let real seconds pass, capture again -- the frames must differ; (c) judge motion character across 3-5 captures spread over the cycle, including both sides of a loop wrap (the wrap must be invisible); (d) for audio-reactive work, capture at a peak AND at silence -- the silence floor must still look composed; (e) emergent looks (e.g. feedback painting) invert the pass order: build the process, run it, judge the accumulated frame.
  5. If the captured frame is black or empty, diagnose before continuing: (a) display/render flags on the output op; (b) for 3D, that a camera, a light, and geometry with render/display on all exist; (c) the chain terminates in a cooking Null; (d) no bypass flag is on; (e) resolution is not 0; (f) alpha/premultiply if the image seems present but invisible.
  6. Iterate with the smallest knob set: adjust the fewest upstream controls (see the Fix-It Map at the end), re-capture, re-judge.

Read the full file on GitHub · 237 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. 9d ago First seen · 237 lines · 70 tokens per session scan A bc00eaa61a53

Subscribe to this mod's changes

visual-aesthetics is a skill published in the GitHub repository dylanroscover/Embody (170 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 7,066 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 2026-08-30.

Related

Other skills, from other repositories

agentcore-investigation

Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session/trace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.

awslabs/mcp · 52 tokens

amazon aurora dsql

Deprecated compatibility redirect for Aurora DSQL guidance. Use when a request concerns DSQL, Aurora DSQL, distributed SQL, DSQL schemas, migrations, queries, authentication, performance, or application development.

awslabs/mcp · 46 tokens

vibeue

Unreal Engine 5 development using the VibeUE Python API. Use when working in Unreal Engine — blueprints, state trees, materials, actors, landscapes, animation, niagara, widgets, sound, foliage, gameplay tags, enhanced input, skeletons, PCG (procedural content generation), and more. VibeUE is an extension of Unreal's…

kevinpbuckley/VibeUE · 82 tokens

testing-python

Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, async testing, and CI integration.

AI-Riksarkivet/ra-mcp · 48 tokens

issue_analyse_supervisor

Autonomous issue analysis — supervisor delegates to engineer subagents.

MarcusJellinghaus/mcp-tools-py · 11 tokens

scrape-batch

Extract many known URLs in one polite, rate-limited pass. Use when the user hands over a list of links, a set of search hits to read in full, or asks to "scrape these pages" / "pull the content from all of them". Drives extract(action="batch"), which fans out with per-domain rate limiting and returns partial results…

n24q02m/wet-mcp · 84 tokens