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-budget-allocatorgit 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-budget-allocator)<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/chip-budget-allocator"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/chip-budget-allocator/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-budget-allocator"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/chip-budget-allocator.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 | $0.00073 | $0.00871 |
| Opus 5 | $0.00036 | $0.00436 |
| Sonnet 5 | $0.00015 | $0.00174 |
| Haiku 4.5 | $0.00007 | $0.00087 |
Grade A, and why
chip-budget-allocator 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 11d 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 Budget Allocator
任务
将系统级 PPA 指标自顶向下拆解到子模块，并确保预算闭合。
执行步骤
- 收集系统级目标：
- Latency Budget、Throughput Budget、Power Budget、Area Budget、Frequency Target。
- 分析模块层次结构，识别关键子模块及其交互关系。
- 基于经验公式和快速模型，将系统指标分配到各子模块：
- Latency：按流水线级数或路径延迟比例分配。
- Throughput：按数据宽度和处理速率分配。
- Power：按活动因子、翻转率、SRAM 容量分配。
- Area：按逻辑门数、SRAM、布线通道估算分配。
- 计算预算总和，检查是否与系统目标闭合（含 10-20% 裕量）。
- 对超出预算或裕量不足的模块标注风险并建议优化方向。
输出格式
### PPA 预算分配表
#### Latency Budget (@ 1GHz)
| 子模块 | 预算 (cycles) | 占比 | 裕量 | 备注 |
|--------|---------------|------|------|------|
#### Power Budget (mW)
| 子模块 | 动态功耗 | 漏电流 | 占比 | 裕量 |
|--------|----------|--------|------|------|
#### Area Budget (kGates + mm²)
| 子模块 | 逻辑门 | SRAM | 总面积 | 占比 |
|--------|--------|------|--------|------|
**闭合检查：X/Y = Z% （建议 ≥ 85% 且 ≤ 100%）**
使用示例
示例 1：系统级 PPA 拆解
用户：帮我把 data_adpt 的 PPA 目标拆到子模块，目标延迟 10 cycles @ 1GHz，面积 150kGates，功耗 15mW
预期行为：
- 列出 data_adpt 的子模块（buf、crc、align 等）
- 按流水线级数分配延迟，按逻辑复杂度分配面积，按活动因子分配功耗
- 输出闭合检查，超预算项标红
示例 2：检查预算闭合
用户：检查 data_adpt 各子模块的 PPA 预算是否闭合
预期行为：汇总各子模块预算，与系统目标对比，输出闭合率
异常处理
| 场景 | 触发条件 | 处理动作 |
|---|---|---|
| 系统目标缺失 | 用户未提供 PPA 目标 | 暂停，列出需要的指标清单，等待用户补充 |
| 子模块未确定 | 模块层次不清晰 | 先输出建议的子模块划分，用户确认后再分配 |
| 预算不闭合 | 总和 > 系统目标 | 标红超预算项，建议优化方向（降频/减并行/缩缓存） |
| 估算无依据 | 经验公式不适用 | 标注"待综合验证"，建议用 DSE Skill 进一步探索 |
检查点
- 分配前：展示系统级目标和子模块列表，用户确认后开始分配
- 分配后：展示闭合检查结果，超预算项需用户确认是否接受
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.
- 11d ago First seen · 72 lines · 73 tokens per session scan A 0c01144b6c44
chip-budget-allocator is a skill published in the GitHub repository zhaixin244-wq/fnw (29 stars, last pushed 3mo ago), licensed MIT. It adds 73 tokens to every session and 871 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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