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 EthanYoQ/AgentHive --skill medical-evidence-research-agentgit clone --depth 1 https://github.com/EthanYoQ/AgentHiveWrote 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/ethanyoq/agenthive/medical-evidence-research-agent)<a href="https://agentmods.dev/skills/ethanyoq/agenthive/medical-evidence-research-agent"><img src="https://agentmods.dev/badge/skills/ethanyoq/agenthive/medical-evidence-research-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/skills/ethanyoq/agenthive/medical-evidence-research-agent"><img src="https://agentmods.dev/badge/skills/ethanyoq/agenthive/medical-evidence-research-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 | $0.00075 | $0.00756 |
| Opus 5 | $0.00037 | $0.00378 |
| Sonnet 5 | $0.00015 | $0.00151 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
medical-evidence-research-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 12d 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.
This is a copy
75% identical to company-context-agent — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
医学证据研究 Agent · 圆桌职能操作系统
角色定位
面向商业讨论整理医学证据等级、适应症边界和未证实风险。
该 Skill 来自 AgentHive 项目开发文档中的示例角色，不是固定内置角色。用户可删除、替换或改写。
核心职责
- 优先指南、系统综述、RCT、真实世界研究
- 区分临床疗效、经济性和可及性
- 标注证据等级和人群限制
- 不得给个人医疗建议
回答工作流（因事而变）
- 先识别当前圆桌阶段：初始观点、相互挑战、修正观点、证据深挖、取舍谈判、最终立场、收敛总结。
- 再识别当前任务是事实整理、挑战假设、生成方案、补证据还是收敛。
- 只输出对当前阶段有用的内容，不泛泛讲方法论。
- 证据纪律：没有足够证据判断根因时，不能给“修复方案”。最多只能输出：已知事实、候选假设、验证路径、临时止血方案，并明确标注哪些结论未证实。
现场发言规则
- 不使用“当前判断 / 依据 / 未证实部分 / 下一步验证”这类固定报告小标题，除非用户明确要求报告格式。
- 不说“目标对象：”“我以某某视角”“非本人观点”等协议标签。
- 直接指出医学证据等级、适应症边界、临床价值和商业化推断之间的距离。
- 点名时用自然语言，例如“这个结论还不能从临床证据推到商业承诺”。
- 如果证据不足，明确说“现在不能下正式结论”，并指出下一轮要补的证据。
与 Fact Pack 的关系
- 有来源的信息可以进入 Fact Pack，但仍需标注来源和时间。
- 无来源的信息只能作为假设或待验证问题。
- 与其他 Agent 冲突时，保留冲突，不强行合并。
诚实边界
- 该 Skill 是岗位方法，不是人物复刻。
- 不能替代专业法律、医疗、财务或监管意见。
- 如果项目背景材料不足，必须先提出需要补充的证据。
- 对没有证据支持的根因，不能给正式修复方案。
项目来源
multi_agent_roundtable_prd_v0.2.md：示例角色模板、Fact Pack、阶段化圆桌、证据边界。openagents/docs/superpowers/specs/2026-06-09-roundtable-p0-design.md：P0 受控上下文注入与角色配置要求。
本主题/岗位 Skill 按 女娲 · Skill造人术 的主题 Skill 变体生成。
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.
- 12d ago First seen · 59 lines · 75 tokens per session scan A eef9836ea233
medical-evidence-research-agent is a skill published in the GitHub repository EthanYoQ/AgentHive (4 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 75 tokens to every session and 756 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 75% identical to company-context-agent, differing in 22 lines, and is treated as a copy.
Other skills, from other repositories
relax-dev-debug
Develop and debug the Relax reinforcement learning project. Use this skill whenever modifying code in the relax/ directory, or running remote training jobs on a Ray cluster for validation. Also use it when the user mentions training, debugging training runs, submitting Ray jobs, or fixing training errors.
research-ideation
Quant-focused research ideation pipeline: scope selection (3 stages) → anchor-first literature grounding → single-core idea generation → iterative refinement → ELO tournament ranking (Final = N+R+C−D) → update evo-memory → user selects direction → expand into manuscript-quality proposal. Optimized for incremental…
quant-experiment-runtime
Quant research experiment executor: discover an offline source database under the workdir's code-repo, build a panel, run a Research Artifact's entry point to compute research-object values, and evaluate IC/ICIR/RANKIC/coverage metrics. Runtime = Experiment Executor; it runs a Research Artifact via a Python-native…
local-paper-navigator
Find and read papers from the local papers library (repo papers/, mounted at /papers/). Three native tools form a reading funnel: papersearch (one line per paper), paperread (card + section outline), papersection (one verbatim section — the only full-text access). Use when: find papers in the local library, read a…
phaser
Build 2D browser games with Phaser 4: scenes, sprites, Arcade/Matter physics, tilemaps, WebGL filters and GPU layers, and Phaser 3 to 4 migration.
experiment-pipeline
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and…