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 ooooooooooooooooooop/agent-tools --skill research-labgit clone --depth 1 https://github.com/ooooooooooooooooooop/agent-toolsWrote 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/ooooooooooooooooooop/agent-tools/research-lab)<a href="https://agentmods.dev/skills/ooooooooooooooooooop/agent-tools/research-lab"><img src="https://agentmods.dev/badge/skills/ooooooooooooooooooop/agent-tools/research-lab/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/ooooooooooooooooooop/agent-tools/research-lab"><img src="https://agentmods.dev/badge/skills/ooooooooooooooooooop/agent-tools/research-lab.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 | $0.00115 | $0.01145 |
| Opus 5.5 | $0.00046 | $0.00458 |
| Sonnet 5.5 | $0.00023 | $0.00229 |
| Haiku 4.5 | $0.00012 | $0.00114 |
Grade A, and why
research-lab 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Lab
目标
Research Skill 是独立 Research Runtime 的使用协议,不是研究系统本体。它负责识别研究任务、约束研究设计、调用稳定 research_ 前缀工具,并依据 Evidence 返回结论;实验、统计、Workspace、同步和恢复由 Research Core 负责。
触发门禁
满足任一条件时使用:
- 用户要求比较模型、方案或系统,且需要质量、成本或延迟证据。
- 用户提出“能否替代”“是否显著更好”“哪个更可靠”等可证伪问题。
- 结论依赖数据集、Baseline、重复运行、盲评、消融或环境记录。
- 用户要求恢复、复现、审计或跨设备继续既有 Research。
不要用于简单事实查询、单一确定答案、无需实证的文案建议或低成本可逆选择。
硬规则
- 不得从用户问题直接跳到结论;先建立或验证 ResearchSpec。
- Spec 至少明确 question、hypotheses、单个
baseline、candidates、dataset/cases、metrics、decision rules 和 adapters;执行字段以当前research validate为准。 - 不具备所需模型、数据或执行能力时返回
UNSUPPORTED,不得模拟实验结果。 - Spec 或执行过程无效时返回
INVALID;有效实验但证据不足时返回INCONCLUSIVE。 - 结论必须引用 Evidence、Run 和 Artifact digest;不能只复述模型输出。
- 不把 API key、token、私钥、绝对用户路径或 credential value 放入 Spec、Workspace、CAS 或工具参数;只使用设备本地配置或 credential reference。
- 多设备 revision 冲突必须显式呈现,禁止静默选择最后写入版本。
标准流程
- 识别任务:把问题改写成可证伪 question;明确候选方案与 Baseline。
- 建立 Spec:若用户已有 Spec,调用
research_validate;否则生成便携 JSON 文件后调用research_create。 - 执行前检查:用
research_inspect检查 revision、数据集、Adapter 和决策阈值。缺关键条件时停止执行并返回结构化缺口。 - 执行或恢复:调用
research_execute。已有 run 时传入 run id;中断后调用research_continue,不要创建重复研究冒充恢复。 - 检查状态:用
research_status查看已完成/失败/待执行项,不进行热轮询;等待真实执行反馈。 - 比较与证据:调用
research_compare,再用research_evidence获取支撑与限制。 - 返回结论:只使用 Runtime 的 Decision 枚举:
SUPPORTED、REJECTED、INCONCLUSIVE、UNSUPPORTED、INVALID。
LLM 对比最小 Spec
{
"protocolVersion": "1.0",
"researchId": "model-replacement-evaluation",
"question": "Candidate 是否可替代 Baseline 完成指定任务?",
"taskType": "llm-comparison",
"hypotheses": [{"id": "h1", "statement": "candidate 质量不低于 baseline 且成本更低"}],
"baseline": {"id": "baseline", "adapter": "command"},
"candidates": [{"id": "candidate", "adapter": "command"}],
"dataset": {
"cases": [{"id": "case-1", "input": "...", "expected": "..."}]
},
"metrics": ["exact_match", "latency_ms", "cost"],
"decisionRules": {
"minDelta": 0.0,
"preferred": "candidate"
},
"adapters": {
"command": {
"type": "command",
"command": ["python", "path/to/adapter.py"]
}
}
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 92 lines · 115 tokens per session scan A 5b85ddd36cba
research-lab is a skill published in the GitHub repository ooooooooooooooooooop/agent-tools (3 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 1,145 once invoked, about $0.0005 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 2026-08-31.
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