Stand up a self-hostable, typo-tolerant search environment with Typesense — the open-source Algolia / ElasticSearch alternative (single C++ binary, <50ms instant search, no runtime deps). One routing-first skill: pick a server mode (binary download, official Docker image, or managed Typesense Cloud), install an API client (Python/JS/PHP/Ruby official; Go/Dart/C# community), design a collection schema, index documents, and run searches with typo tolerance, faceting/filtering, geo-search, sorting, grouping, synonyms, curation, scoped API keys, and federated multi-search — then wire an InstantSearch.js UI and a Raft-based HA cluster for production. Use when the user wants to build or operate an installable search backend, add site/app/product search, or migrate off Algolia/Elasticsearch. Triggers on: typesense, search engine, typo-tolerant search, algolia alternative, elasticsearch alternative, instantsearch, faceted search, geo search, vector search, self-hosted search, site search, product search.
Instrucciones de origen · Vista previa de solo lectura
name
typesense
description
Stand up a self-hostable, typo-tolerant search environment with Typesense — the open-source Algolia / ElasticSearch alternative (single C++ binary, <50ms instant search, no runtime deps). One routing-first skill: pick a server mode (binary download, official Docker image, or managed Typesense Cloud), install an API client (Python/JS/PHP/Ruby official; Go/Dart/C# community), design a collection schema, index documents, and run searches with typo tolerance, faceting/filtering, geo-search, sorting, grouping, synonyms, curation, scoped API keys, and federated multi-search — then wire an InstantSearch.js UI and a Raft-based HA cluster for production. Use when the user wants to build or operate an installable search backend, add site/app/product search, or migrate off Algolia/Elasticsearch. Triggers on: typesense, search engine, typo-tolerant search, algolia alternative, elasticsearch alternative, instantsearch, faceted search, geo search, vector search, self-hosted search, site search, product search.
allowed-tools
Bash Read Write Edit Glob Grep WebFetch
compatibility
Cross-platform search-backend wrapper usable from Claude Code, Codex, Gemini CLI, and OpenCode. Wraps the upstream Typesense server (binary / Docker / Cloud) and official API clients. Routes LLM-trace/eval observability to `opik`/`langsmith`, agent-facing code search to `semble`, and broad service telemetry to `monitoring-observability`.
Typesense is a fast, typo-tolerant
open-source search engine — an Algolia alternative and an easier-to-use
ElasticSearch alternative. It is a single C++ binary with no runtime
dependencies, architected for low-latency (<50ms) instant search. This skill
is the routing-first wrapper: choose how to run the server, wire a client,
model the data, and drive search + UI + production hardening.
When to use this skill
The user wants to stand up a search backend for a site, app, catalog,
docs, or product browsing experience
The user asks to install/run Typesense (binary, Docker, or Typesense Cloud)
The user wants typo tolerance, faceting/filtering, geo-search, sorting,
grouping, synonyms, curation, scoped API keys, or federated multi-search
The user wants to migrate off Algolia or Elasticsearch to a self-hosted
or managed open-source engine
The user wants an InstantSearch.js UI or a Raft HA cluster in front
of / around Typesense
When not to use this skill
The user wants LLM trace/eval observability (hallucination, prompt
scoring) → use opik / langsmith
The user wants token-efficient code search for agents over a repo →
use semble
The user wants generic service dashboards / uptime alerts (non-search
telemetry) → use monitoring-observability
The user wants a vector database purpose-built for embeddings only —
Typesense does vector + hybrid search, but a dedicated store may fit better
for pure ANN at extreme scale; confirm the workload first
Prerequisites
Requirement
Notes
Docker (recommended)
Simplest local + prod path via the official image
or a binary host
Linux (x86-64) / macOS binary packages from typesense.org/downloads
or Typesense Cloud
Zero-ops managed cluster (fixed hourly + bandwidth, not per-record)
An API client
Python / JS / PHP / Ruby official; Go / Dart / C# community
An API key
Set at server start (--api-key); generate scoped keys per tenant
Prefer an official client over raw CURL — they ship a smart retry strategy
for HA setups. See references/commands.md for the
full client + integration matrix.
Step 3 — Design the collection schema
A collection is an index with a typed schema. Mark fields facet: true to
filter/drill-down, and set default_sorting_field for ranking:
Unlike Algolia, most settings (searchable fields, facets, ranking) are set at
query time, so one collection serves many sort orders — less memory, more
flexibility.
client.collections["companies"].documents.search({
"q": "stork", # typo of "stark" — handled out of the box"query_by": "company_name",
"filter_by": "num_employees:>100",
"sort_by": "num_employees:desc",
"facet_by": "country",
})
Capabilities to reach for: faceting/filtering, geo-search (sort by distance),
grouping & distinct, synonyms, curation/merchandizing (pin records),
federated multi-search across collections in one request, and vector /
hybrid search. Details in references/commands.md.
This skill folder is plugin-installable through the standard jeo-skills
flow so the wrapper, references, and installer land on disk for any supported
agent runtime:
# Project install (writes into .agents/skills/typesense/)
npx skills add https://github.com/akillness/jeo-skills --skill typesense
# Global install for every detected agent
npx skills add -g https://github.com/akillness/jeo-skills --skill typesense
# Target specific agents
npx skills add -g https://github.com/akillness/jeo-skills --skill typesense -a claude-code -a codex -y
Output format
When the user asks typesense for help, return a compact brief:
# typesense Routing Brief## Scope- Server mode: docker | binary | cloud | undecided
- Client: python | js | php | ruby | community
- Stage: install-server | install-client | schema-design | index | search | ui | production-ha
## Recommended next move- start-docker-server | install-client | create-collection | import-docs | run-search | wire-instantsearch | scoped-keys | cluster
## Why- 2-3 bullets grounded in the user's packet
## Route-outs-`opik` / `langsmith` for LLM trace/eval observability
-`semble` for agent-facing code search over a repo
-`monitoring-observability` for non-search service telemetry
Best practices
Pin a version tag, never latest — typesense/typesense:27.1, and
keep the data dir on a real volume so restarts don't lose the index.
Set settings at query time — searchable fields, facets, sort, and
ranking are per-query; you rarely need multiple collections for sort orders.
Mark facets in the schema — facet: true is required for filtering /
drill-down on a field.
Use scoped API keys for clients — the admin key stays server-side;
scoped keys enforce per-tenant record access.
Bulk import as JSONL with upsert — far faster than per-document
creates for large datasets; size RAM to the index (memory-resident).
License awareness — the server is GPL, the client libraries are
Apache-2.0; run the server as a separate daemon (the intended use).
