delight

delight is a skill for Claude Code from AkaraChen/aghub. It costs 56 tokens per session (2,189 once invoked), scanned A, a copy of delight, MIT.

A design guide for adding personality, small surprises, and enjoyable interactions to an interface.

In plain words
What is it for?
Use it to improve success, empty, loading, error, and interaction states with suitable visual or interactive touches.
Why use it?
It helps make functional screens feel less flat or forgettable without losing sight of the product's purpose.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: installed under .agents/ (shared by several agents).

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 skills/akarachen/aghub/delight
Any agent
npx skills add AkaraChen/aghub --skill delight
Clone the repo
git clone --depth 1 https://github.com/AkaraChen/aghub

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 delight

README.md
[![agentmods](https://agentmods.dev/badge/skills/akarachen/aghub/delight.svg)](https://agentmods.dev/skills/akarachen/aghub/delight)
Your own site
<a href="https://agentmods.dev/skills/akarachen/aghub/delight"><img src="https://agentmods.dev/badge/skills/akarachen/aghub/delight.svg" alt="Measured on agentmods" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,189 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 97% copy Near-identical to another mod 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 $0.00056 $0.02189
Opus 5 $0.00028 $0.01094
Sonnet 5 $0.00011 $0.00438
Haiku 4.5 $0.00006 $0.00219

Measured 6d ago against content hash 10ea4e37c325, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

delight 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.

Origin

This is a copy

97% identical to delight — 78 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/delight/SKILL.md · 336 lines

How it starts

The opening of the file, as written. The whole thing — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Identify opportunities to add moments of joy, personality, and unexpected polish that transform functional interfaces into delightful experiences.

MANDATORY PREPARATION

Invoke /frontend-design — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first. Additionally gather: what's appropriate for the domain (playful vs professional vs quirky vs elegant).


Assess Delight Opportunities

Identify where delight would enhance (not distract from) the experience:

  1. Find natural delight moments:

    • Success states: Completed actions (save, send, publish)
    • Empty states: First-time experiences, onboarding
    • Loading states: Waiting periods that could be entertaining
    • Achievements: Milestones, streaks, completions
    • Interactions: Hover states, clicks, drags
    • Errors: Softening frustrating moments
    • Easter eggs: Hidden discoveries for curious users
  2. Understand the context:

    • What's the brand personality? (Playful? Professional? Quirky? Elegant?)
    • Who's the audience? (Tech-savvy? Creative? Corporate?)
    • What's the emotional context? (Accomplishment? Exploration? Frustration?)
    • What's appropriate? (Banking app ≠ gaming app)
  3. Define delight strategy:

    • Subtle sophistication: Refined micro-interactions (luxury brands)
    • Playful personality: Whimsical illustrations and copy (consumer apps)
    • Helpful surprises: Anticipating needs before users ask (productivity tools)
    • Sensory richness: Satisfying sounds, smooth animations (creative tools)

If any of these are unclear from the codebase, ask the user directly to clarify what you cannot infer.

CRITICAL: Delight should enhance usability, never obscure it. If users notice the delight more than accomplishing their goal, you've gone too far.

Delight Principles

Read the full file on GitHub · 336 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 · 336 lines · 56 tokens per session scan A 10ea4e37c325

Subscribe to this mod's changes

delight is a skill published in the GitHub repository AkaraChen/aghub (264 stars, last pushed 3d ago), licensed MIT. It adds 56 tokens to every session and 2,189 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to delight, differing in 78 lines, and is treated as a copy.