X Openai Research Router Skill 6563d16

---
name: x-openai-research-router-skill-6563d16
description: Route broad or ambiguous life-sciences research requests to the right skills, normalize core entities, optionally parallelize independent evidence gathering with subagents when available, and synthesize a concise evidence-backed answer. Use when a user asks a general life-sciences question that could span multiple sources or analysis types.
---

### 导出副本边界与技能别名

迁移、安装、修复和经验回写只作用于当前加载的目标宿主技能副本。原 Codex 技能/MCP、下载的源码和 `original-skills` 均为来源材料，不因执行本副本而写入；只有用户另行明确要求配置 Codex 产品本身时，才按该产品流程处理指定目标。脚本需要安装目录或状态目录时，显式提供目标路径；不得从 `CODEX_HOME` 隐式推断写入位置。

跨技能名称可能沿用源库别名。先查 [技能别名表](../../skill-aliases.json)，优先选择同来源、同插件的条目，再使用其 `id` 调用或读取对应入口；不得假定旧 `$skill`、`superpowers:skill` 名称在新宿主直接注册。不能确定目标时先核对，不替换真实产品命令或 Shell 环境变量。

## 跨宿主使用约定

本副本供支持 Agent Skills 的客户端使用。先检查当前宿主实际提供的工具、权限和已连接服务，再执行正文工作流。原文中的宿主工具标识是能力参考；只有实际发现该工具时才可调用，其他宿主应使用经核验的等价 MCP、CLI 或 API。没有等价能力的步骤必须报告不可用，不能虚构调用或完成状态。

技能资源以本技能目录为基准；输出写入当前项目中用户指定的位置。通过技能名称或自然语言触发，具体快捷命令由宿主决定。涉及真实产品、模型 API、协议字段和官方网址的名称保持原意。授权、审批、隐私和工具范围限制在迁移后仍然有效；不得为兼容而扩大权限。具体依赖与迁移说明见同目录 `PORTABILITY.md`。

## Research Router

Use this skill as the default orchestration layer for broad life-sciences research requests.

Do not use it for narrow single-source lookups when a more specific skill already matches the request cleanly.

## Primary Responsibility

Turn an open-ended research question into a small, defensible retrieval plan:

1. understand the research objective
2. normalize the main entities
3. select the minimum useful set of downstream skills
4. gather evidence
5. synthesize the answer for the user

The router owns the framing and the final synthesis. It should not dump raw source payloads unless the user explicitly asks for them.

## When To Use This Skill

Use this skill when any of the following are true:

- the user asks a broad question such as `what is known about ...`
- the question could require more than one evidence type
- the right source is unclear at the start
- the request mixes entities, for example gene plus disease, variant plus phenotype, protein plus ligand, or pathway plus dataset
- the user wants a synthesized answer rather than a single database lookup

## Research Task Classification

Start by classifying the request into one or more lanes:

- human genetics and variant interpretation
- locus-to-gene prioritization
- expression, tissue, or cell-type context
- pathway, network, or functional biology
- protein structure and mechanism
- chemistry, ligands, and pharmacology
- clinical, translational, or cancer evidence
- literature, preprints, and public dataset discovery
- metabolomics, proteomics, or microbiome context

Prefer 1 to 3 lanes. Only expand further if the user explicitly asks for a broad landscape review.

## Entity Normalization

Normalize the key entities before deep retrieval.

Common patterns:

- gene or protein: `ncbi-clinicaltables-skill`, `ensembl-skill`, `uniprot-skill`
- disease or phenotype: `efo-ontology-skill`, `opentargets-skill`
- variant: `clinvar-variation-skill`, `ensembl-skill`, cohort-specific PheWAS skills
- compound or metabolite: `chembl-skill`, `pubchem-pug-skill`, `chebi-skill`, `hmdb-skill`
- pathway or function: `reactome-skill`, `quickgo-skill`, `string-skill`
- accession or dataset identifier: `ncbi-datasets-skill`, `biostudies-arrayexpress-skill`, `pride-skill`, `metabolights-skill`

Do not start broad evidence collection until the important entities are stable enough to route correctly.

## Skill Selection Heuristics

Choose the smallest set of skills that can answer the question well.

Examples:

- target or disease evidence review:
  `opentargets-skill`, `gwas-catalog-skill`, `gtex-eqtl-skill`, `human-protein-atlas-skill`
- variant interpretation:
  `clinvar-variation-skill`, `gnomad-graphql-skill`, `ensembl-skill`, one or more cohort PheWAS skills
- locus-to-gene mapping:
  `locus-to-gene-mapper-skill`, or its component genetics skills when the user wants a custom workflow
- structure and mechanism:
  `alphafold-skill`, `rcsb-pdb-skill`, `uniprot-skill`, `reactome-skill`
- chemistry and pharmacology:
  `chembl-skill`, `bindingdb-skill`, `pubchem-pug-skill`, `pharmgkb-skill`
- clinical and translational:
  `clinicaltrials-skill`, `cbioportal-skill`, `civic-skill`
- literature and dataset discovery:
  `ncbi-entrez-skill`, `ncbi-pmc-skill`, `biorxiv-skill`, `biostudies-arrayexpress-skill`, `ncbi-datasets-skill`

Prefer direct lookups before expensive multi-step chains.

## Subagent And Parallelization Guidance

If the current host exposes verified subagent capabilities, use them only when the work cleanly decomposes into independent lanes.

Good candidates for subagents:

- genetics, expression, structure, chemistry, and clinical evidence can be gathered independently for the same question
- multiple loci, variants, genes, compounds, or datasets need parallel comparison
- a broad landscape review requires separate evidence summaries before synthesis

Keep these steps with the coordinating agent:

- initial interpretation of the user request
- entity normalization and final scope decisions
- conflict resolution across evidence sources
- final synthesis and recommendation writing

Avoid subagents when:

- one specific skill already answers the question
- later steps depend tightly on earlier intermediate outputs
- the work is mostly identifier resolution or narrow follow-up lookup
- the extra coordination cost is likely to exceed the retrieval benefit

When delegating, give each subagent a bounded read-only objective such as one evidence family or one comparison unit. Each subagent should return:

- what it checked
- the key findings
- the main caveats
- which skills or sources it used
- any artifact paths it produced

The coordinating agent is responsible for reconciling overlaps, contradictions, and evidence gaps.

## Output Contract

Return a concise answer structured around the user's question, not around the tools.

Unless the user asks for a different format, include:

1. direct answer or working conclusion
2. key evidence by lane
3. main caveats or unresolved questions
4. recommended next analyses or follow-up lookups

If the task is exploratory, explicitly distinguish:

- evidence that supports a conclusion
- evidence that is only suggestive
- evidence that is missing or contradictory

## Operating Rules

- prefer concise source-backed synthesis over large raw dumps
- escalate to multi-skill workflows only when the question requires synthesis
- state important cohort, ancestry, assay, tissue, and study-design limitations
- do not overstate causality from association-only evidence
- if a downstream skill can answer the request directly, hand off to it instead of keeping the router in the foreground