Vespa.ai Skills

AI coding assistant skills for Vespa.ai. Works with Claude Code, OpenAI Codex, Google Gemini CLI, and Cursor.

Each skill is a self-contained folder with a SKILL.md as the single source of truth and a docs/ folder with detailed reference material.

Installation

Vespa CLI (recommended)

The Vespa CLI can install skills directly for Claude Code, Codex, Cursor and Antigravity CLI - no manual cloning required:

vespa skills install

Run vespa skills list to see available skills, and vespa skills update to refresh previously installed skills to the latest version.

npx skills

npx skills installs skills into any of 70+ supported agent harnesses (Claude Code, Cursor, Codex, and just about every other agent harness):

npx skills add vespa-engine/skills

Skills

Skill	Description
app-package	Scaffold and configure Vespa application packages, including services.xml, schemas, deployment.xml, query profiles, and embedder components.
elasticsearch-migration	Migrate from Elasticsearch to Vespa — map ES indices and mappings to Vespa schemas, translate Query DSL to YQL, plan reindexing, and bridge ranking differences. Use when the user mentions migrating from Elasticsearch, ES→Vespa, porting an ES index, or replacing Elasticsearch with Vespa.
feed-operations	Vespa document CRUD operations and bulk feeding — covers document ID format, JSON wire format for put/update/remove, REST API endpoints, CLI commands, partial updates, conditional writes, bulk feeding, and document visiting/export.
pyvespa	Python API for Vespa.ai — define schemas, deploy applications, feed documents, query, and manage Vespa from Python using pyvespa.
query-builder	Build Vespa YQL queries and design rank profiles. Covers YQL syntax, operators, grouping, rank-profile phases, ML model integration, and query tensor inputs.
schema-authoring	Writing, validating, and evolving Vespa .sd schema files — covers field types, indexing pipelines, match modes, tensors, rank profiles, structs, fieldsets, and common pitfalls.
vespa-cli	Vespa CLI for deploying, managing, and debugging Vespa.ai applications -- covers target configuration, authentication, deployment lifecycle, production pipelines, document operations, log inspection, testing, and CI/CD integration.

Example Prompts

Schema authoring:

"Create a Vespa schema for a product catalog with title, description, price, category, and a 384-dim embedding for semantic search."

Application package:

"Scaffold a Vespa application package with a HuggingFace embedder for the e5-small-v2 model."

Query building:

"Write a hybrid search query that combines BM25 text matching with nearest-neighbor vector search, using reciprocal rank fusion."

Feed operations:

"Generate a JSONL feed file for 3 sample products and show me the vespa feed command to load them."

Development

Generating artifacts

A single script generates all platform-specific manifests from the SKILL.md files:

python generate.py          # Generate AGENTS.md, cursor/plugin.json, README table
python generate.py --check  # CI mode — exits 1 if any generated file is out of date

Evaluations

Run the skill benchmark suite with uv run vespaskills eval / eval-discovery / aggregate. See evals/README.md for the commands and an example report.

Adding a new skill

1. Create a new folder at the root: my-skill/
2. Add a SKILL.md with YAML frontmatter (name and description)
3. Add reference docs in my-skill/docs/ as needed
4. Add an entry to .claude-plugin/marketplace.json
5. Run python generate.py

Contributing

Contributions are welcome! Please:

1. Keep SKILL.md files under 500 lines — use docs/ for detailed references
2. Run python generate.py --check before submitting a PR
3. Verify technical accuracy against docs.vespa.ai

License

Apache 2.0 — see LICENSE.

Links

• Vespa Documentation
• Vespa GitHub
• Sample Applications

Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.