Install
$ agentstack add skill-castorini-castorini-skills-castorini-serve ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ● Network access Used
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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— AnseriniRestServer8082—rank_llm serve http8083—ragnarok serve8084—umbrela serve8085—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 withragnarok, then join the two envelopes fornuggetizer assign.
Reference Files
references/service-recipes.md— startup commands, port conventions, and per-service defaultsreferences/curl-pipelines.md— direct curl, pipe, jq, and full multi-step examples
Gotchas
rank_llmserves HTTP asrank-llm serve http ..., not plainrank-llm serve ....- The Anserini fatjar skill documents standalone
RestServeron8080, but the shared Castorini HTTP stack uses8081by convention so the other services can stay on8082through8085. ragnarok,nuggetizer create, andumbrelacan accept raw Anserini search payloads directly because they normalize{query, candidates}inputs.ragnarok,nuggetizer create, andumbrelacan also accept thecastorini.cli.v1envelope returned byrank_llm, so a raw pipe fromrank_llmusually works.nuggetizer createmust consume the retrieval pool, notragnarokanswer output.nuggetizer assigndoes not consume a bareragnarokenvelope by itself; wrapanswer_envelopeplusnugget_envelope, oranswers_envelopeplusnugget_envelope, withjq.umbrelajudges passage relevance, not answer quality. For answer quality, use theragnarokplusnuggetizerpath 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
- Source: castorini/castorini-skills
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.