Results for “algodocs”

Apply world-class developer documentation principles (Stripe, Snowflake, Databricks, TiDB Cloud) to structure, write, review, or refactor technical documentation. Use this skill whenever the user mentions documentation, docs, sidebar or navigation, information architecture, restructuring a section, writing or editing a guide, reviewing docs, where content belongs, English doc prose, headings, code comments, link text, docs home pages, section landing pages, long-form guides mixing content types, cross-referencing, or making docs readable for AI agents and LLMs. Covers VeloDB Cloud docs work (Monitoring restructure, sidebar, EN/中文 alignment, writing style, landing pages, LLM-friendly docs) and any SaaS or database documentation task. Trigger broadly: if the conversation touches doc organization, page structure, doc quality, doc sentences, landing pages, or AI-readable docs, consult this skill rather than answering from intuition.

Generates a Node.js build script that converts VitePress/GFM markdown to Azure DevOps Wiki format, handling Mermaid syntax, front matter, and links.

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.

Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

ByteDance's open-source super agent harness — spawns parallel sub-agents, Docker sandbox execution, persistent long-term memory, modular skills (research/report/slides). Built on LangChain + LangGraph. Triggers on: 'deerflow', 'deer-flow', 'bytedance agent', 'super agent harness', 'LangGraph agent orchestration', 'multi-hour agent tasks', 'spawn sub-agents', 'agent sandbox docker', 'persistent agent memory', 'agent skills system', 'AI research orchestrator', 'long-running agent tasks', 'agent with memory', 'langgraph orchestrator', 'IM channel agent integration'.