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.
npx agentmods add skills/guillermoscript/lms-front/delightnpx skills add guillermoscript/lms-front --skill delightgit clone --depth 1 https://github.com/guillermoscript/lms-frontWhat 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 | $0.00028 | $0.02245 |
| Opus 5 | $0.00014 | $0.01123 |
| Sonnet 5 | $0.00006 | $0.00449 |
| Haiku 4.5 | $0.00003 | $0.00225 |
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 2d 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.
This is a copy
100% identical to delight — 0 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.
How it starts
The opening of the file, as written. The whole thing — 317 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
Context Gathering (Do This First)
You cannot do a great job without having necessary context, such as target audience (critical), desired use-cases (critical), brand personality (playful vs professional vs quirky vs elegant), and what's appropriate for the domain.
Attempt to gather these from the current thread or codebase.
- If you don't find exact information and have to infer from existing design and functionality, you MUST STOP and STOP and call the AskUserQuestionTool to clarify. whether you got it right.
- Otherwise, if you can't fully infer or your level of confidence is medium or lower, you MUST STOP and call the AskUserQuestionTool to clarify. clarifying questions first to complete your context.
Do NOT proceed until you have answers. Delight that's wrong for the context is worse than no delight at all.
Use frontend-design skill
Use the frontend-design skill for design principles and anti-patterns. Do NOT proceed until it has executed and you know all DO's and DON'Ts.
Assess Delight Opportunities
Identify where delight would enhance (not distract from) the experience:
-
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
-
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)
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.
- 2d ago First seen · 317 lines · 28 tokens per session scan A 00513f6f0db4
delight is a skill published in the GitHub repository guillermoscript/lms-front (24 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 2,245 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to delight, differing in 0 lines, and is treated as a copy.
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