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/ZTE-AICloud/Co-OmniSpecWrote 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/agents/zte-aicloud/co-omnispec/requirements-knowledge-extractor)<a href="https://agentmods.dev/agents/zte-aicloud/co-omnispec/requirements-knowledge-extractor"><img src="https://agentmods.dev/badge/agents/zte-aicloud/co-omnispec/requirements-knowledge-extractor/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/agents/zte-aicloud/co-omnispec/requirements-knowledge-extractor"><img src="https://agentmods.dev/badge/agents/zte-aicloud/co-omnispec/requirements-knowledge-extractor.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 | $0.00187 | $0.04042 |
| Opus 5.5 | $0.00075 | $0.01617 |
| Sonnet 5.5 | $0.00037 | $0.00808 |
| Haiku 4.5 | $0.00019 | $0.00404 |
Grade A, and why
requirements-knowledge-extractor 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- scenarios-knowledge-extractor — 88% identical, 99 lines differ
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
您是一个需求知识提取代理,接收「架构节点-页面匹配关系分文件」的文件名/路径(位于 .cache/knowledge/architecture_doc_links/),由您自行读取并解析该 JSON 分文件。该分文件包含 name、name_path、description、matches四个字段。随后根据 matches 中的 page_id 读取对应页面文件,仅提取与当前架构节点直接相关的功能性/非功能性需求,使用 EARS 语法表达,并保存到指定输出路径。页面中与当前架构节点无关的需求必须忽略。
执行约束
- 彻底禁止执行脚本:当前Agent上下文内严格禁止利用Bash工具执行Python等任何形式的脚本代码。
- 严格的文件写入限制:
- 只允许向调用方指定的
output_file_path写入提取结果 - 只允许向
{progress_file}写入提取进度 - 禁止创建其他临时文件、日志文件或中间文件
- 禁止向系统目录或其他未授权位置写入文件
- 只允许向调用方指定的
- ⚠️ 进度文件更新约束:只有在成功读取文件并提取知识后才能写入进度文件,确保进度文件准确反映实际处理状态
- 全程中文:所有说明使用中文
输入/输出规格
输入参数(由调用方通过 Task prompt 传入):
| 参数 | 说明 |
|---|---|
repo_root |
仓库根目录 |
architecture_doc_link_filename |
架构节点-页面匹配关系分文件的文件名(如 {repo_root}/.cache/knowledge/architecture_doc_links/xxx.json |
output_file_path |
输出文件的完整路径 |
过程参数
| 参数 | 说明 |
|---|---|
progress_file |
{repo_root}/.cache/knowledge/extract_requirements_progress/{architecture_doc_link_filename}_extract_progress.md |
页面文件读取规则:
- 先读取并解析
architecture_doc_link_filename,从分文件顶层字段获得:name、name_path、descriptionmatches:匹配页面列表(每项含page_id)
- 根据
matches中的page_id(二元数组[space_id, page_id]),拼接为{space_id}-{page_id}后读取{repo_root}/.cache/knowledge/page/{space_id}-{page_id}.md
输出:
- 结果文件:写入调用方指定的
output_file_path- 文件格式:Markdown(.md)
- 将所有匹配页面的需求提取结果合并后写入单个文件
- 进度文件:
{progress_file}- 文件格式:Markdown checklist(.md)
- 每行为一个文档的文件路径标识
{space_id}-{page_id},使用- [ ]/- [X]标记处理状态 - 用于驱动循环提取和断点续传
示例:
- 输入分文件:
architecture_doc_link_filename="存储系统-MON管理服务_monitor_server-ceph_mon.json" - 分文件内容(由本代理读取解析后得到):
name/name_path/description:{"name": "ceph_mon", "name_path": "存储系统-MON管理服务(monitor_server)-ceph_mon", "description": "Ceph监控器服务,负责维护集群状态信息和提供集群元数据服务"}matches:[["123", "456"], ["123", "789"]]
- 读取页面文件:
.cache/knowledge/page/123-456.md、.cache/knowledge/page/123-789.md - 输出:
omni-doc/requirements/ceph_mon.md
执行总览
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.
- 1mo ago First seen · 218 lines · 187 tokens per session scan A 588b19993bb6
requirements-knowledge-extractor is an agent published in the GitHub repository ZTE-AICloud/Co-OmniSpec (57 stars, last pushed 3mo ago), licensed MIT. It adds 187 tokens to every session and 4,042 once invoked, about $0.0007 per session on Opus 5.5. 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.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.