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 normalizegit 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/normalize)<a href="https://agentmods.dev/skills/fengshao1227/ccg-workflow/normalize"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/normalize/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/normalize"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/normalize.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 | $0.00050 | $0.00826 |
| Opus 5.5 | $0.00020 | $0.00330 |
| Sonnet 5.5 | $0.00010 | $0.00165 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
normalize 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- normalize — 100% identical, 2 lines differ
- normalize — 100% identical, 2 lines differ
- normalize — 98% identical, 5 lines differ
- normalize — 98% identical, 33 lines differ
- normalize — 98% identical, 5 lines differ
- normalize — 97% identical, 16 lines differ
- normalize — 97% identical, 16 lines differ
- normalize — 97% identical, 16 lines differ
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze and redesign the feature to perfectly match our design system standards, aesthetics, and established patterns.
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.
Plan
Before making changes, deeply understand the context:
-
Discover the design system: Search for design system documentation, UI guidelines, component libraries, or style guides (grep for "design system", "ui guide", "style guide", etc.). Study it thoroughly until you understand:
- Core design principles and aesthetic direction
- Target audience and personas
- Component patterns and conventions
- Design tokens (colors, typography, spacing)
CRITICAL: If something isn't clear, ask. Don't guess at design system principles.
-
Analyze the current feature: Assess what works and what doesn't:
- Where does it deviate from design system patterns?
- Which inconsistencies are cosmetic vs. functional?
- What's the root cause—missing tokens, one-off implementations, or conceptual misalignment?
-
Create a normalization plan: Define specific changes that will align the feature with the design system:
- Which components can be replaced with design system equivalents?
- Which styles need to use design tokens instead of hard-coded values?
- How can UX patterns match established user flows?
IMPORTANT: Great design is effective design. Prioritize UX consistency and usability over visual polish alone. Think through the best possible experience for your use case and personas first.
Execute
Systematically address all inconsistencies across these dimensions:
- Typography: Use design system fonts, sizes, weights, and line heights. Replace hard-coded values with typographic tokens or classes.
- Color & Theme: Apply design system color tokens. Remove one-off color choices that break the palette.
- Spacing & Layout: Use spacing tokens (margins, padding, gaps). Align with grid systems and layout patterns used elsewhere.
- Components: Replace custom implementations with design system components. Ensure props and variants match established patterns.
- Motion & Interaction: Match animation timing, easing, and interaction patterns to other features.
- Responsive Behavior: Ensure breakpoints and responsive patterns align with design system standards.
- Accessibility: Verify contrast ratios, focus states, ARIA labels match design system requirements.
- Progressive Disclosure: Match information hierarchy and complexity management to established patterns.
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 · 71 lines · 50 tokens per session scan A 6b37acca54c6
normalize is a skill published in the GitHub repository fengshao1227/ccg-workflow (5,943 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 826 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 2026-09-03.
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