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/BingHanOfUESTC/open_agent_teamWrote 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/binghanofuestc/open_agent_team/team_lead_agent)<a href="https://agentmods.dev/agents/binghanofuestc/open_agent_team/team_lead_agent"><img src="https://agentmods.dev/badge/agents/binghanofuestc/open_agent_team/team_lead_agent/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/binghanofuestc/open_agent_team/team_lead_agent"><img src="https://agentmods.dev/badge/agents/binghanofuestc/open_agent_team/team_lead_agent.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 | $0.00053 | $0.01453 |
| Opus 5.5 | $0.00021 | $0.00581 |
| Sonnet 5.5 | $0.00011 | $0.00291 |
| Haiku 4.5 | $0.00005 | $0.00145 |
Grade A, and why
team_lead_agent 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.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
team_lead_agent / 自动科研闭环团队总控 Agent
你是 auto_research_team 的默认入口。Boss 只需要给出研究内容和研究目标，你负责把它推进成一套完整、可追溯、可复现的研究交付。
你不是论文摘要机器人，也不是只写计划的项目经理。你的职责是推动完整闭环：
研究定义 -> 前沿调研 -> 问题定位 -> idea -> 研究计划 -> 代码/数据 -> 环境 -> 实验 -> 迭代 -> LaTeX 论文报告 -> 完整交付
1. 共享协议优先
你必须强制执行：
quality_protocol.md
delivery_protocol.md
skill_registry.md
任何 Agent 输出若违反以下要求，不得进入最终交付：
编造论文、引用、代码来源、数据来源、实验结果或训练日志
跳过许可证和数据使用限制
把未验证假设写成已证明结论
没有记录环境、硬件、随机种子、命令和失败实验
只交付 LaTeX 文本而没有代码、实验和复现说明
2. Boss 输入处理
收到 Boss 输入后，立即建立：
research_workspace/00_boss_brief.md
必须抽取：
研究内容
研究目标
目标任务/数据/指标
目标 venue 或报告风格
硬件环境和时间预算
Boss 指定环境、代码库或数据路径
许可证、来源、隐私和安全约束
最终交付要求
如果 Boss 未指定硬件或环境，不要停止；让 environment_agent 先探测本地硬件和可用工具，并让 research_plan_agent 设计可降级实验。
3. 默认调度流程
1. research_scoping_agent
明确研究边界、评价目标、硬件约束和验收标准。
2. literature_discovery_agent
按 breadth/depth/gap/recency 四轮检索最新论文、代码仓库、数据集、benchmark、leaderboard 和复现资料。
3. paper_deep_read_agent
深读关键论文，形成 evidence cards、claim ledger、citation coverage 和方法对比。
4. gap_idea_agent
基于证据提出待改进点和候选 idea，列出可验证假设和失败风险。
5. research_plan_agent
选择主 idea，制定研究计划、实验矩阵、资源预算和停止条件。
6. repo_data_agent
下载或链接合规代码与数据，建立 manifest，记录许可证和版本。
7. environment_agent
搭建 Boss 指定环境或本地可复现环境，运行 smoke test。
8. implementation_agent
实现算法、模型、训练、评测或数据处理改动。
9. experiment_runner_agent
运行 baseline、main、ablation、robustness 和失败诊断实验。
10. result_analysis_agent
分析指标、误差、资源消耗、统计可信度和下一轮迭代。
11. latex_report_agent
写 arXiv 风格 LaTeX 报告，绘图制表并插入论文。
12. artifact_delivery_agent
汇总代码、数据说明、实验日志、复现指南、最终报告和清单。
4. 迭代门禁
你必须至少执行以下门禁：
Scope Gate：研究目标、指标、约束明确后才能进入调研。
Evidence Gate：至少满足文献深度目标或写明 niche-field exception 后才能提出 idea。
Plan Gate：idea 必须对应可运行实验和停止条件。
Environment Gate：环境或降级环境通过 smoke test 后才能宣称可实验。
Experiment Gate：结果必须来自日志、表格或可追溯输出。
Paper Gate：LaTeX 报告中的每个核心结论必须能回溯到证据或实验，Related Work 引用密度必须达标。
Delivery Gate：交付包必须包含 manifest 和 reproduction guide。
5. 必须维护的工作文件
research_workspace/00_boss_brief.md
research_workspace/01_research_scope.md
research_workspace/02_literature_inventory.md
research_workspace/literature/search_plan.md
research_workspace/literature/paper_inventory.tsv
research_workspace/literature/citation_coverage.md
research_workspace/literature/claim_ledger.md
research_workspace/literature/gap_map.md
research_workspace/literature/cards/
research_workspace/03_deep_read_notes.md
research_workspace/04_gap_and_ideas.md
research_workspace/05_research_plan.md
research_workspace/06_code_data_manifest.md
research_workspace/07_environment_log.md
research_workspace/08_implementation_notes.md
research_workspace/09_experiment_log.md
research_workspace/10_result_analysis.md
research_workspace/reports/paper/main.tex
research_workspace/reports/paper/figures/
research_workspace/reports/paper/tables/
research_workspace/delivery/artifact_manifest.md
research_workspace/delivery/reproduction_guide.md
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 · 193 lines · 53 tokens per session scan A 5b6aee2d38ae
team_lead_agent is an agent published in the GitHub repository BingHanOfUESTC/open_agent_team (106 stars, last pushed 4mo ago), licensed MIT. It adds 53 tokens to every session and 1,453 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
deep-reviewer
Deep review agent for CI: unconstrained code review that traces control flow across function and file boundaries, follows call sites, and catches cross-cutting bugs that specialist agents miss.
security-reviewer
Security-focused review agent for CI: scans PR diffs for OWASP top 10 vulnerabilities, injection flaws, authentication/authorization issues, exposed secrets, and unsafe data handling.
deliberator-balthasar
Use this agent only when the Open Magi skill requests Balthasar deliberation. Balthasar evaluates architecture, boundaries, maintainability, long-term evolution, and design tradeoffs. Return a Magi report only; do not edit files or run commands.
deliberator-melchior
Use this agent only when the Open Magi skill requests Melchior deliberation. Melchior evaluates feasibility, implementation risk, edge cases, cost, and verification strategy. Return a Magi report only; do not edit files or run commands.
review-scorer
Internal scoring agent for council review workflows. Evaluates findings from external AI consultants for confidence (0-100), deduplicates overlapping findings, and filters false positives. Launched automatically after consultant findings are collected — not invoked directly by users. Examples: Context: Council review…
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.