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 gokeshenzhen · MCP Server · ★ 112
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is TraceWeave safe to install? View the security audit →
🐙 TraceWeave English · 简体中文 MCP server for simulation-failure debug through log parsing and waveform analysis Waveform + log root-cause MCP — stop debugging by hand, use TraceWeave. What sets TraceWeave apart: when a Verdi license is available it engages KDB/NPI for accurate cross-hierarchy driver / load / connectivity analysis; without a license it still locates issues via the built-in Static backend, log parsing, and VCD/FSDB waveform reads. It supports driver backtracking, load/fanout lookup, value-at-time queries, cycle-aligned sampling, arbit
| Stars | 112 |
| Forks | 29 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.7743365565889/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-19 |
| Created | 2026-02-27 |
| Platforms | mcp, python |
| Est. Tokens | ~21k |
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TraceWeave is Open-source, evidence-driven MCP server for RTL simulation debugging: correlate VCS/Xcelium logs, VCD/FSDB waveforms, SystemVerilog/UVM source, hierarchy, and connectivity to trace failures to root ca. It is categorized as a MCP Server with 112 GitHub stars.
TraceWeave is primarily written in Python. It covers topics such as chip-verification, eda, fsdb.
You can find installation instructions and usage details in the TraceWeave GitHub repository at github.com/gokeshenzhen/TraceWeave. The project has 112 stars and 29 forks, indicating an active community.
TraceWeave is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to TraceWeave on Agent Skills Hub include wave-mcp, vivado-mcp, roam-code. 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: