Scientific Agent Skills
Scientific Agent Skills: futuro y proyecto de un copiloto de investigación reproducible
Una hoja de ruta práctica para bundles de evidencia portables, evaluación, gobierno y extensiones de bases de datos.
Qué aprenderás
- Design a provenance-aware capstone
- Define evaluation and governance gates
- Separate current evidence from future proposals
Antes de empezar
- 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.
Conclusiones clave
- Future value comes from portable evidence bundles and explicit capability manifests.
- A narrow provenance-aware copilot is a useful capstone project.
- Evaluation must include citation accuracy, abstention, adversarial fixtures, cost, and human review.
The next useful abstraction
La siguiente capa de Scientific Agent Skills es que cada skill produzca un bundle de evidencia con versión, identificadores, respuesta cruda, citas, señales de calidad y decisión humana. El proyecto propuesto mantiene un dominio estrecho, replayable y auditable, y separa claramente la evidencia actual de las ideas futuras.
A shared manifest could declare capability, data classification, allowed domains, expected latency, and failure modes. Hosts would then discover not only what a skill can do, but also what it is allowed to touch.
Capstone project: a provenance-aware research copilot
Build a small service that accepts one research question, selects one pinned skill, runs a public-data fixture, and stores an evidence bundle. Add a reviewer UI that shows the query, identifiers, source links, database version, raw response hash, model draft, and approval state side by side.
Keep the first release intentionally narrow: one domain, one database adapter, one model, and one export format. The project is valuable when a second person can replay the fixture and explain every field, not when it claims broad scientific coverage.
Evaluation and governance roadmap
Measure citation completeness, identifier accuracy, abstention on ambiguous results, p95 latency, cost variables, and reviewer time. Add adversarial fixtures for prompt injection, malformed records, rate limits, and stale schemas. Publish failures with the same visibility as successful summaries.
For team adoption, add signed manifests, scoped credentials, policy-as-code checks, and an audit log that separates raw evidence from generated interpretation. A future host adapter should pass the same contract and security suite before it is marked compatible.
What to imagine next
A mature ecosystem could exchange evidence bundles between agents, attach uncertainty and data lineage to every claim, and let a reviewer approve a specific citation rather than an entire opaque answer. Skills could advertise cost and latency budgets, while databases publish schema-change notices that automatically pause affected workflows.
Those ideas remain a roadmap, not promises of the current repository. The practical next step is to prototype one narrow bundle, test it with real reviewers, and contribute improvements that keep the contract inspectable and reversible.
Cómo elegir
| Criterio | Opción A | Opción 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 |
Pasos de implementación
- 1
Choose one domain, adapter, model, and public fixture.
- 2
Store inputs, identifiers, raw hashes, citations, drafts, and review state.
- 3
Add adversarial tests, signed manifests, scoped credentials, and policy checks.
- 4
Replay across hosts and contribute only changes that preserve the contract.
Ejemplo para copiar
{
"skill_revision": "<commit>",
"query_hash": "<sha256>",
"identifiers": [],
"sources": [],
"raw_response_hash": "<sha256>",
"draft": "<separate from evidence>",
"review": { "status": "pending", "reviewer": null }
}Preguntas frecuentes
Is a portable evidence bundle part of the current standard?
Treat it as a proposed project direction. The current repository provides skills and host guidance; a bundle schema still needs implementation and review.
What is a good first contribution?
Add a focused fixture, clearer provenance fields, safer credential boundaries, or host-compatibility tests for one existing skill.
Fuentes
- Scientific Agent Skills README (captured 2026-08-31)Fuente verificada 2026-08-31
- Scientific Agent Skills repositoryFuente verificada 2026-08-31