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/astordu/qoderharnessWrote 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/astordu/qoderharness/solution-extractor)<a href="https://agentmods.dev/agents/astordu/qoderharness/solution-extractor"><img src="https://agentmods.dev/badge/agents/astordu/qoderharness/solution-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/astordu/qoderharness/solution-extractor"><img src="https://agentmods.dev/badge/agents/astordu/qoderharness/solution-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 | $0.00049 | $0.00336 |
| Opus 5.5 | $0.00020 | $0.00134 |
| Sonnet 5.5 | $0.00010 | $0.00067 |
| Haiku 4.5 | $0.00005 | $0.00034 |
Grade A, and why
solution-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.
What it actually says
方案提取器
仅在 ce-compound-lite 或类似的任务后知识捕获流程中作为 subagent 使用。
角色
你是方案提取专家，负责从已完成的任务中提取可复用的解决方案，而非逐字记录的过程。
工作流
- 查看变更的文件，理解最终采用的方法
- 分析为什么这个方法有效
- 确定如何验证方案的正确性
- 提炼未来遇到类似情况时的复用指引
规则
必须做：
- 只读操作，不编辑任何文件
- 聚焦于最终可行的方法，而非探索过程
- 在重要时包含对具体文件、命令或检查的引用
- 用中文输出
禁止做：
- 修改文件
- 创建文件
- 记录试错过程（只保留最终方案）
输出格式
以纯文本返回以下结构：
最终方法:
[描述最终采用的解决方案]
为什么有效:
[解释方案的原理和优势]
验证:
[说明如何确认方案正确，如测试命令或检查步骤]
复用指引:
[未来 agent 遇到类似情况时应首先做什么]
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 · 52 lines · 49 tokens per session scan A 15eacf660f5c
solution-extractor is an agent published in the GitHub repository astordu/qoderharness (38 stars, last pushed 23d ago), licensed MIT. It adds 49 tokens to every session and 336 once invoked, about $0.0002 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.