Borrowing it
Nothing to install: this file belongs to ReScienceLab/TrySkills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ReScienceLab/TrySkills/main/.agents/skills/shape/SKILL.mdgit clone --depth 1 https://github.com/ReScienceLab/TrySkillsWrote 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.
[](https://agentmods.dev/skills/resciencelab/tryskills/shape)<a href="https://agentmods.dev/skills/resciencelab/tryskills/shape"><img src="https://agentmods.dev/badge/skills/resciencelab/tryskills/shape/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.
<a href="https://agentmods.dev/skills/resciencelab/tryskills/shape"><img src="https://agentmods.dev/badge/skills/resciencelab/tryskills/shape.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00049 | $0.01104 |
| Opus 5.5 | $0.00020 | $0.00442 |
| Sonnet 5.5 | $0.00010 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00110 |
Grade A, and why
shape 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Invoke /impeccable, which contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding. If no design context exists yet, you MUST run /impeccable teach first.
Shape the UX and UI for a feature before any code is written. This skill produces a design brief: a structured artifact that guides implementation through discovery, not guesswork.
Scope: Design planning only. This skill does NOT write code. It produces the thinking that makes code good.
Output: A design brief that can be handed off to /impeccable craft, /impeccable, or any other implementation skill.
Philosophy
Most AI-generated UIs fail not because of bad code, but because of skipped thinking. They jump to "here's a card grid" without asking "what is the user trying to accomplish?" This skill inverts that: understand deeply first, so implementation is precise.
Phase 1: Discovery Interview
Do NOT write any code or make any design decisions during this phase. Your only job is to understand the feature deeply enough to make excellent design decisions later.
Ask these questions in conversation, adapting based on answers. Don't dump them all at once; have a natural dialogue. ask the user directly to clarify what you cannot infer.
Purpose & Context
- What is this feature for? What problem does it solve?
- Who specifically will use it? (Not "users"; be specific: role, context, frequency)
- What does success look like? How will you know this feature is working?
- What's the user's state of mind when they reach this feature? (Rushed? Exploring? Anxious? Focused?)
Content & Data
- What content or data does this feature display or collect?
- What are the realistic ranges? (Minimum, typical, maximum, e.g., 0 items, 5 items, 500 items)
- What are the edge cases? (Empty state, error state, first-time use, power user)
- Is any content dynamic? What changes and how often?
Design Goals
- What's the single most important thing a user should do or understand here?
- What should this feel like? (Fast/efficient? Calm/trustworthy? Fun/playful? Premium/refined?)
- Are there existing patterns in the product this should be consistent with?
- Are there specific examples (inside or outside the product) that capture what you're going for?
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.
- 1mo ago First seen · 96 lines · 49 tokens per session scan A 20ac5e5abf2f
shape is a skill published in the GitHub repository ReScienceLab/TrySkills (3 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 1,104 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 2026-08-31.
Other skills, from other repositories
webgl-holographic-foil
A self-contained WebGL2 hero: thin-film interference over a crushed-foil surface whose palette shifts with the viewing angle; move the cursor to tilt the film.
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
html-ppt-taste-brutalist
16:9 HTML deck in tactical-telemetry / CRT-terminal taste. Deactivated-CRT charcoal slides, white-phosphor monospace, hazard-red accent, scanline overlay, ASCII syntax, density over decoration. Distilled from Leonxlnx/taste-skill brutalist-skill (Tactical Telemetry mode).
visual-ralph
Visual Ralph orchestration for frontend UI from generated references, static references, or live URL targets, using $ultragoal with built-in visual verdict and pixel-diff evidence until the implementation matches and leaves a reproducible design system.
glance-canonical-layouts
Use this skill to create home screen widgets using Jetpack Glance Canonical Layouts from the Android Platform Samples project, or when asked to implement standard widget layout patterns (including Check List, Image Grid, Snap Scrolling, and Standard Toolbar).
accessibility
Consolidated accessibility skill entrypoint for WCAG 2.2, ARIA Authoring Practices, cognitive accessibility, Section 508, EN 301 549, design intent verification, and the Accessibility Planner workflow.