# Castorini Serve

> Use when serving Anserini retrieval together with any subset of rank_llm, ragnarok, nuggetizer, or umbrela over HTTP, especially for local port planning, direct request payload compatibility, curl or jq pipelines, or sequencing retrieval, reranking, generation, nugget creation, nugget assignment, and passage judging from an Anserini fatjar RestServer.

- **Type:** Skill
- **Install:** `agentstack add skill-castorini-castorini-skills-castorini-serve`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [castorini](https://agentstack.voostack.com/s/castorini)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [castorini](https://github.com/castorini)
- **Source:** https://github.com/castorini/castorini-skills/tree/main/skills/castorini-serve

## Install

```sh
agentstack add skill-castorini-castorini-skills-castorini-serve
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Castorini Serve

Serve the Castorini stack as cooperating local HTTP services.

Treat Anserini retrieval as the entry point, then add only the downstream services the user actually needs. Prefer `gpt-4o` as the default OpenAI-backed model for `rank_llm`, `ragnarok`, `nuggetizer`, and `umbrela` unless the user explicitly asks for a different model.

## Default Stack

Use this local port layout unless the user asks for different ports:

- `8081` — Anserini `RestServer`
- `8082` — `rank_llm serve http`
- `8083` — `ragnarok serve`
- `8084` — `umbrela serve`
- `8085` — `nuggetizer serve`

## Service Selection

- Retrieval only: serve Anserini through `io.anserini.api.RestServer`.
- Retrieval + rerank: add `rank_llm`.
- Retrieval + answer generation: use Anserini -> `ragnarok`, or Anserini -> `rank_llm` -> `ragnarok`.
- Retrieval + relevance judgment: use Anserini -> `umbrela`, or Anserini -> `rank_llm` -> `umbrela`.
- Retrieval + nugget creation: use Anserini -> `nuggetizer create`, or Anserini -> `rank_llm` -> `nuggetizer create`.
- Full answer-evaluation flow: keep the original retrieval or rerank payload for `nuggetizer create`, generate answers with `ragnarok`, then join the two envelopes for `nuggetizer assign`.

## Reference Files

- `references/service-recipes.md` — startup commands, port conventions, and per-service defaults
- `references/curl-pipelines.md` — direct curl, pipe, jq, and full multi-step examples

## Gotchas

- `rank_llm` serves HTTP as `rank-llm serve http ...`, not plain `rank-llm serve ...`.
- The Anserini fatjar skill documents standalone `RestServer` on `8080`, but the shared Castorini HTTP stack uses `8081` by convention so the other services can stay on `8082` through `8085`.
- `ragnarok`, `nuggetizer create`, and `umbrela` can accept raw Anserini search payloads directly because they normalize `{query, candidates}` inputs.
- `ragnarok`, `nuggetizer create`, and `umbrela` can also accept the `castorini.cli.v1` envelope returned by `rank_llm`, so a raw pipe from `rank_llm` usually works.
- `nuggetizer create` must consume the retrieval pool, not `ragnarok` answer output.
- `nuggetizer assign` does not consume a bare `ragnarok` envelope by itself; wrap `answer_envelope` plus `nugget_envelope`, or `answers_envelope` plus `nugget_envelope`, with `jq`.
- `umbrela` judges passage relevance, not answer quality. For answer quality, use the `ragnarok` plus `nuggetizer` path instead.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [castorini](https://github.com/castorini)
- **Source:** [castorini/castorini-skills](https://github.com/castorini/castorini-skills)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-castorini-castorini-skills-castorini-serve
- Seller: https://agentstack.voostack.com/s/castorini
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.