Scientific Agent Skills
Scientific Agent Skills 架构：标准、Skill 契约与证据边界
跟踪 SKILL.md、plugin.json、数据库适配器与人工复核如何组成可移植的研究代理架构。
你将学会
- Trace discovery, contract, adapter, and evidence layers
- Classify failures at the correct boundary
- Design a governed, portable research-agent deployment
开始前需要
- Basic Git and command-line usage
- Comfort reading a project README
You can explain the project, run its documented first step, and decide what to verify before adopting it.
先看结论
- Discovery, skill contracts, capability adapters, and evidence review are separate layers.
- Host-specific paths and metadata make portability a claim to verify, not assume.
- Model output becomes useful research only when source identifiers and human accountability travel with it.
The architecture in one sentence
Scientific Agent Skills 可以分成发现与打包、SKILL.md 工作流契约、工具/数据库能力、证据与人工复核四层。宿主的发现路径和元数据支持各不相同，必须固定客户端与版本后再讨论可移植性。
That separation is valuable because it makes a workflow inspectable before execution. It also means portability is not magic: each host decides where it searches, which metadata fields it honors, and how it exposes tools or credentials. Architecture claims should therefore name the host and revision rather than treating every adapter as equivalent.
Four layers to trace
First is discovery and packaging: `skills/` folders, YAML frontmatter, `plugin.json`, and host-specific install paths. Second is the skill contract: a `SKILL.md` describes purpose, inputs, steps, examples, and limits. Third is capability access: Python packages, database APIs, MCP servers, or integrations that a workflow invokes. Fourth is evidence and review: identifiers, citations, artifacts, uncertainty, and the qualified person who approves a conclusion.
The database layer is intentionally heterogeneous. The README groups unified database lookup with dedicated connectors and package skills, so a single natural-language question can cross several APIs. Preserve the boundary between orchestration and source interpretation: a connector can return a record, but it cannot decide whether that record supports a clinical or scientific claim.
Failure paths are part of the design
A missing `SKILL.md` or plugin entry is a discovery failure; a package import error is an environment failure; a rate limit or expired token is a provider failure; and an answer with no primary citation is an evidence failure. Handle these as different states in logs and user messaging. Do not silently fall back to an unreviewed API or invent a result when a capability is unavailable.
For multi-step research, pass a run context containing the skill revision, input hash, source identifiers, and timestamps. Keep transient credentials out of that context, and give each external call a bounded timeout and retry policy. A deterministic run record helps an operator reproduce a problem without replaying sensitive material.
Design implications for adopters
The portable standard lowers integration cost, while the collection's breadth raises governance cost. Teams should publish an allow-list of skills, network domains, packages, and output destinations; assign owners; and test host upgrades in a canary workspace. Use the repository's validation and security workflows as structural gates, then add domain-specific tests for the skills that matter to your lab.
The architecture supports rich research assistance, not autonomous authority. Keep human review at the edge where evidence becomes a recommendation, diagnosis, regulatory artifact, or release decision. That is the point where provenance, uncertainty, and professional accountability must be visible to the reader.
如何选择
| 比较维度 | 方案 A | 方案 B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
实施步骤
- 1
Map the host discovery path and pin the collection revision.
- 2
Read one SKILL.md and list its packages, APIs, MCP servers, and file writes.
- 3
Define run-context fields for revisions, identifiers, timestamps, and citations.
- 4
Canary host upgrades and require qualified review at the decision boundary.
可复制示例
discovery -> SKILL.md contract -> tools/databases -> evidence record -> qualified review
run_id: 2026-08-31-demo
skill_revision: <commit>
source_ids: [<primary identifiers>]
review_status: draft常见问题
Is plugin.json the same as a model or API gateway?
No. It packages discoverable skills for compatible clients; models, tools, credentials, and external data providers remain separate runtime dependencies.
Can one host guarantee identical behavior to another?
No. Discovery paths, metadata support, permissions, and tool adapters vary. Test the exact host and revision you plan to operate.
资料来源
- Scientific Agent Skills README (captured 2026-08-31)来源核查 2026-08-31
- Scientific Agent Skills repository来源核查 2026-08-31