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 zhaixin244-wq/fnw --skill chip-design-space-explorergit clone --depth 1 https://github.com/zhaixin244-wq/fnwWrote 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/zhaixin244-wq/fnw/chip-design-space-explorer)<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/chip-design-space-explorer"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/chip-design-space-explorer/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/zhaixin244-wq/fnw/chip-design-space-explorer"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/chip-design-space-explorer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00074 | $0.00898 |
| Opus 5 | $0.00037 | $0.00449 |
| Sonnet 5 | $0.00015 | $0.00180 |
| Haiku 4.5 | $0.00007 | $0.00090 |
Grade A, and why
chip-design-space-explorer 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 12d 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.
What it actually says
Chip Design Space Explorer
任务
在 Area-Performance-Power 三维空间中进行设计空间探索（DSE），寻找帕累托最优解集。
执行步骤
- 明确探索变量（设计旋钮）：
- 流水线级数、并行度、缓存大小、总线宽度、工作频率、电压域配置等。
- 建立简化评估模型（解析模型或基于经验的快速估算）。
- 枚举或采样设计点，计算每个设计点的 PPA 指标。
- 使用帕累托支配关系筛选非支配解集（Pareto Frontier）。
- 可视化结果：
- 以 Markdown 表格列出关键设计点
- 使用 Mermaid 或 ASCII 绘制二维投影图（Latency vs Area、Power vs Performance 等）
- 给出推荐设计点及理由（考虑裕量、物理可实现性、验证复杂度）。
输出格式
### DSE 结果（帕累托前沿）
| 设计点 | 配置摘要 | 延迟 (ns) | 吞吐 (GB/s) | 面积 (kGates) | 功耗 (mW) | 帕累托最优 |
|--------|----------|-----------|-------------|---------------|-----------|------------|
| A | 3级流水线, 64b总线 | 5.0 | 12.8 | 80 | 15 | ✓ |
| B | 5级流水线, 128b总线 | 3.2 | 25.6 | 150 | 28 | ✓ |
### 推荐设计点
**推荐：B**
- 理由：...
- 风险：...
使用示例
示例 1：FIFO 深度 DSE
用户：帮我探索 data_adpt 的 FIFO 深度设计空间，突发量 32 beats，生产者速率 1B/cycle，消费者速率 0.5B/cycle
预期行为：
- 以 FIFO 深度为设计旋钮（16/32/64/128）
- 计算每个深度的面积、功耗、溢出风险
- 输出帕累托前沿表 + 推荐深度
示例 2：流水线级数 DSE
用户：data_adpt 的处理路径可以做 3/4/5 级流水线，帮我比选最优方案
预期行为：枚举 3 个设计点，计算延迟/吞吐/面积/功耗，标注帕累托最优解
异常处理
| 场景 | 触发条件 | 处理动作 |
|---|---|---|
| 设计旋钮未定义 | 用户未指定探索变量 | 列出常见旋钮（流水线级数/并行度/缓存/总线宽度/频率），用户选择 |
| 评估模型缺失 | 无法估算 PPA | 使用经验公式 + 标注"待综合验证"，建议 DSE 后跑综合确认 |
| 设计点过多 | > 100 个组合 | 使用拉丁超立方采样减少到 20-30 个代表点 |
| 帕累托前沿为空 | 所有设计点被支配 | 检查约束条件是否过紧，建议放宽 1-2 个约束重新探索 |
检查点
- 探索前：展示设计旋钮列表和约束条件，用户确认后开始枚举
- 探索后：展示帕累托前沿表格和推荐点，用户选择最终方案
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.
- 12d ago First seen · 67 lines · 74 tokens per session scan A c6dce316d3f7
chip-design-space-explorer is a skill published in the GitHub repository zhaixin244-wq/fnw (29 stars, last pushed 3mo ago), licensed MIT. It adds 74 tokens to every session and 898 once invoked, about $0.0004 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 2026-08-30.
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