CCG is a command-line workflow engine that coordinates Claude, Codex, Gemini, and other models as specialized collaborators on coding tasks. It is used to analyze requests, choose a strategy, delegate work to model-specific roles, and combine their results. The catalogue entries provide the skills, commands, agents, and plugin that implement this workflow.
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 fengshao1227/ccg-workflow --skill delightgit clone --depth 1 https://github.com/fengshao1227/ccg-workflowWrote 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/fengshao1227/ccg-workflow/delight)<a href="https://agentmods.dev/skills/fengshao1227/ccg-workflow/delight"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/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/fengshao1227/ccg-workflow/delight"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/delight.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.02185 |
| Opus 5 | $0.00028 | $0.01092 |
| Sonnet 5 | $0.00011 | $0.00437 |
| Haiku 4.5 | $0.00006 | $0.00218 |
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.
Copies of this mod
8 near-identical copies found in the catalogue:
- delight — 100% identical, 2 lines differ
- delight — 100% identical, 2 lines differ
- delight — 97% identical, 7 lines differ
- delight — 97% identical, 78 lines differ
- delight — 97% identical, 11 lines differ
- delight — 97% identical, 7 lines differ
- delight — 95% identical, 11 lines differ
- delight — 95% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 304 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:
-
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 using AskUserQuestion.
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:
Delight Amplifies, Never Blocks
- Delight moments should be quick (< 1 second)
- Never delay core functionality for delight
- Make delight skippable or subtle
- Respect user's time and task focus
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 · 304 lines · 56 tokens per session scan A af8e0079bcbb
delight is a skill published in the GitHub repository fengshao1227/ccg-workflow (5,879 stars, last pushed 5d ago), licensed MIT. It adds 56 tokens to every session and 2,185 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 2026-09-03.
Other skills, from other repositories
absolute-ui
Build polished, intentional UIs with concrete CSS/Tailwind values — typography, color, layout, spacing, dark mode, accessibility, animations, components. Encodes specific, opinionated rules with exact values, not vague advice. Covers buttons, cards, forms, tables, navigation, dashboards, landing pages, onboarding, and…
ve-terminal-mono
OpenDesign from the CLI: driving the full design workflow with the od command — scripted, composable, agent-ready. Built as a decision-grade AI literacy deck for developers, power users.
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-obsidian-claude-gradient
OpenDesign's enterprise AI-adoption brief: local-first agents at work, the risk controls, the ROI, and the rollout plan. Built as a decision-grade AI literacy deck for leadership, IT, security.
design-review
Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems, AI slop patterns, and slow interactions — then fixes them. Iteratively fixes issues in source code, committing each fix atomically and re-verifying with before/after screenshots. For plan-mode design review (before implementation), use…
create-openbitfun-cinematic-wallpaper-skin
Apply the cinematic animated-wallpaper style recipe to a OpenBitFun Appearance skin. Use after loading the parent create-openbitfun-skin Skill when the user provides animated character artwork and wants a host-managed video background, source-derived glass materials, image-led cards, illustrated dialogs, reproducible…