No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by bjwanneng · Agent Tool · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is veriflow-cc safe to install? View the security audit →
VeriFlow-CC Claude Code-driven RTL design pipeline — zero Python dependencies, Claude Code main session is the driver. What It Is VeriFlow-CC treats Claude Code as the pipeline brain: the main Claude Code session controls stage transitions, calls a sub-agent for RTL generation, and handles errors and rollbacks. Differences from the full VeriFlow-Agent: No LangGraph / LangChain / Streamlit No required Claude Code itself is the interaction and decision layer State persisted to JSON, recoverable after session restart Architecture 4 stages: specgolden → codegen → verifyfix → lintsynth. Sub-agents handle specialist work (RTL coding, lint, synthesis). Main session handles orchestration and error recovery. Quick Start Install from Source bash git clone https://github.com/bjwanneng/veriflow-cc.git
| Stars | 50 |
| Forks | 10 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 65.9458716263696/100 |
| Last Updated | 2026-08-03 |
| Created | 2026-04-12 |
| Platforms | claude-code, python |
| Est. Tokens | ~18k |
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veriflow-cc is VeriFlow-CC: A Claude Code-driven RTL design pipeline. Automates Chip-on-Chat from architecture to synthesis (iVerilog/Yosys) using a stateful, zero-dependency LLM orchestration skill. Features sub-ag. It is categorized as a Agent Tool with 50 GitHub stars.
veriflow-cc is primarily written in Python. It covers topics such as ai-agents, chip-design, claude-code-skill.
You can find installation instructions and usage details in the veriflow-cc GitHub repository at github.com/bjwanneng/veriflow-cc. The project has 50 stars and 10 forks, indicating an active community.
The top alternatives to veriflow-cc on Agent Skills Hub include agent-skills, claude-skills, LiteWebAgent. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: