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 skills add subhansh-dev/agent-maxxing --skill 06-delightgit clone --depth 1 https://github.com/subhansh-dev/agent-maxxingWrote 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/subhansh-dev/agent-maxxing/06-delight)<a href="https://agentmods.dev/skills/subhansh-dev/agent-maxxing/06-delight"><img src="https://agentmods.dev/badge/skills/subhansh-dev/agent-maxxing/06-delight/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/subhansh-dev/agent-maxxing/06-delight"><img src="https://agentmods.dev/badge/skills/subhansh-dev/agent-maxxing/06-delight.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 | $0.00028 | $0.02162 |
| Opus 5 | $0.00014 | $0.01081 |
| Sonnet 5 | $0.00006 | $0.00432 |
| Haiku 4.5 | $0.00003 | $0.00216 |
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 5d 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
95% identical to delight — 11 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 — 303 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
Use the frontend-design skill — 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:
-
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)
-
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
Follow these guidelines:
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.
- 5d ago First seen · 303 lines · 28 tokens per session scan A 6690d52ecde2
delight is a skill published in the GitHub repository subhansh-dev/agent-maxxing (2 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 2,162 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to delight, differing in 11 lines, and is treated as a copy.
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