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.
git 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/commands/zhaixin244-wq/fnw/wiki-ingest)<a href="https://agentmods.dev/commands/zhaixin244-wq/fnw/wiki-ingest"><img src="https://agentmods.dev/badge/commands/zhaixin244-wq/fnw/wiki-ingest/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/commands/zhaixin244-wq/fnw/wiki-ingest"><img src="https://agentmods.dev/badge/commands/zhaixin244-wq/fnw/wiki-ingest.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 | $0.00000 | $0.00487 |
| Opus 5 | $0.00000 | $0.00244 |
| Sonnet 5 | $0.00000 | $0.00097 |
| Haiku 4.5 | $0.00000 | $0.00049 |
Grade A, and why
wiki-ingest 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
Wiki Ingest — 知识源文件编译
将 knowledge/ 目录中的原始文档编译为结构化 wiki 页面。
执行流程
-
读取源文件：从
$ARGUMENTS指定的路径读取原始文档- 如未指定参数，扫描 knowledge/ 中未编译的文件
- 如指定
--all，批量处理所有未编译文件 - 如指定
--dry-run，仅预览不实际写入
-
提取实体/概念：从源文件中识别：
- 实体（协议、模块、IP、设计方法）
- 概念（设计模式、策略、机制）
- 关键参数和信号
-
生成结构化页面：按 wiki 模板格式生成：
- entities/ — 实体页面（协议/CBB/IP/设计方法）
- concepts/ — 概念页面（设计模式/策略）
- comparisons/ — 对比页面（如涉及多实体比较）
-
更新交叉引用：
- 新页面引用已存在的相关页面
- 已存在页面添加对新页面的引用
-
更新索引：更新
wiki/index.md -
更新 Hot Cache：更新
wiki/hot.md
芯片领域 Ingest 规则
- 实体页面必须包含：基本信息、核心特征、关键信号/参数、设计要点
- 来源必须追溯到 knowledge/ 中的原始文件
- CBB 模块页面需包含：接口定义、时序约束、面积预估
- 协议页面需包含：信号列表、时序图描述、设计约束
- 交叉引用必须双向：新页面→相关页面 + 相关页面→新页面
参数
$ARGUMENTS— 源文件路径、--all或--dry-run
使用示例
/wiki-ingest .claude/knowledge/bus-protocol/pcie.md # 单文件
/wiki-ingest --all # 批量所有
/wiki-ingest --dry-run # 预览模式
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 · 49 lines · 0 tokens per session scan A 52c605d6be97
wiki-ingest is a command published in the GitHub repository zhaixin244-wq/fnw (29 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 487 tokens. 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.
Other commands, from other repositories
querying
Query documents from a search index using type-safe filters with support for pagination, sorting, field selection, scoring, and highlighting. Count matching documents efficiently without returning results.
agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
ingest
Manually add knowledge to the Weaviate store.
inference.embed
Embed one or more texts into vectors.
vector.batch_exists
Check existence for multiple vector keys.
vector.batch_upsert
Upsert multiple vectors in one itemwise batch.