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 hardengit 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/harden)<a href="https://agentmods.dev/skills/fengshao1227/ccg-workflow/harden"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/harden/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/harden"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/harden.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 | $0.00062 | $0.02190 |
| Opus 5 | $0.00031 | $0.01095 |
| Sonnet 5 | $0.00012 | $0.00438 |
| Haiku 4.5 | $0.00006 | $0.00219 |
Grade A, and why
harden 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- harden — 97% identical, 2 lines differ
- harden — 97% identical, 2 lines differ
- harden — 95% identical, 8 lines differ
- harden — 94% identical, 6 lines differ
- i-harden — 94% identical, 82 lines differ
- harden — 94% identical, 6 lines differ
- harden — 94% identical, 168 lines differ
- harden — 94% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strengthen interfaces against edge cases, errors, internationalization issues, and real-world usage scenarios that break idealized designs.
Assess Hardening Needs
Identify weaknesses and edge cases:
-
Test with extreme inputs:
- Very long text (names, descriptions, titles)
- Very short text (empty, single character)
- Special characters (emoji, RTL text, accents)
- Large numbers (millions, billions)
- Many items (1000+ list items, 50+ options)
- No data (empty states)
-
Test error scenarios:
- Network failures (offline, slow, timeout)
- API errors (400, 401, 403, 404, 500)
- Validation errors
- Permission errors
- Rate limiting
- Concurrent operations
-
Test internationalization:
- Long translations (German is often 30% longer than English)
- RTL languages (Arabic, Hebrew)
- Character sets (Chinese, Japanese, Korean, emoji)
- Date/time formats
- Number formats (1,000 vs 1.000)
- Currency symbols
CRITICAL: Designs that only work with perfect data aren't production-ready. Harden against reality.
Hardening Dimensions
Systematically improve resilience:
Text Overflow & Wrapping
Long text handling:
/* Single line with ellipsis */
.truncate {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/* Multi-line with clamp */
.line-clamp {
display: -webkit-box;
-webkit-line-clamp: 3;
-webkit-box-orient: vertical;
overflow: hidden;
}
/* Allow wrapping */
.wrap {
word-wrap: break-word;
overflow-wrap: break-word;
hyphens: auto;
}
Flex/Grid overflow:
/* Prevent flex items from overflowing */
.flex-item {
min-width: 0; /* Allow shrinking below content size */
overflow: hidden;
}
/* Prevent grid items from overflowing */
.grid-item {
min-width: 0;
min-height: 0;
}
Responsive text sizing:
- Use
clamp()for fluid typography - Set minimum readable sizes (14px on mobile)
- Test text scaling (zoom to 200%)
- Ensure containers expand with text
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.
- 6d ago First seen · 356 lines · 62 tokens per session scan A 91ef3508516d
harden is a skill published in the GitHub repository fengshao1227/ccg-workflow (5,881 stars, last pushed 6d ago), licensed MIT. It adds 62 tokens to every session and 2,190 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.
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