LightState Registry / Portable Agent Skill (SKILL.md)s / Research & Intel / skill-qdrant-hybrid-dense-sparse-search
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Qdrant Hybrid Dense + Sparse BM25 Vector Search Playbook
Configure Qdrant collections with HNSW quantization, payload indexing, and Reciprocal Rank Fusion (RRF).
⚡ 1-Click Agent & Developer Install
Zero Auth · Ready for Grok Bot, Claude Code & Cursornpx lightstate install skill-qdrant-hybrid-dense-sparse-search
Copy-Paste System Prompt / Routine Blueprint:
Load the Qdrant Hybrid Dense + Sparse BM25 Vector Search Playbook (skill-qdrant-hybrid-dense-sparse-search) playbook. First inspect our current configuration and codebase for Qdrant, Vector Search, RAG. Then propose a concrete, step-by-step implementation plan following the skill-qdrant-hybrid-dense-sparse-search guardrails, run the non-destructive verification commands (curl -s http://localhost:6333/collections), and summarize any findings, risks, or required environment variables before making production changes.
📘 Complete SKILL.md Playbook Specification
--- name: qdrant-hybrid-dense-sparse-search description: Configure Qdrant collections with HNSW quantization, payload indexing, and Reciprocal Rank Fusion (RRF). Use when working with Qdrant, Vector Search, RAG. triggers: - qdrant hybrid dense sparse search - qdrant - vector search --- # Qdrant Hybrid Dense + Sparse BM25 Vector Search Playbook ## 1. When to Use This Skill Load `skill-qdrant-hybrid-dense-sparse-search` whenever an engineering, architecture, or operations task involves **Qdrant, Vector Search, RAG**. Specifically: - Configure Qdrant collections with HNSW quantization, payload indexing, and Reciprocal Rank Fusion (RRF). - Designing, reviewing, refactoring, or debugging production configurations without guesswork or deprecated flags. - Running deterministic pre-flight validation and post-execution verification checks before reporting completion. ## 2. Prerequisites & Required Environment - **Target Stack:** Qdrant, Vector Search, RAG - **Permissions Boundary:** Read-only inspection by default; ask for explicit human confirmation before executing destructive mutations or production deployments. - **Reference Specification:** `https://github.com/qdrant/qdrant` ## 3. Step-by-Step Execution Playbook 1. **Context & Configuration Discovery:** Inspect existing project manifests (`package.json`, `tsconfig.json`, `pyproject.toml`, `Cargo.toml`, `wrangler.jsonc`, or schema declarations) before proposing changes. Never overwrite existing bindings or credentials blindly. 2. **Deterministic Implementation:** Apply the canonical production patterns for **Qdrant**: - Enforce strict schema and type validation at every external boundary. - Include explicit error handling, structured telemetry, and idempotency keys on all mutating calls. - Keep secrets in environment variables or managed secret stores—never hardcode tokens in source files. 3. **Verification & Regression Gate:** Run the verification commands below and inspect both exit codes and output payloads before marking the task complete. ## 4. Reference Commands & CLI Workflow ```bash curl -s http://localhost:6333/collections ``` ## 5. Edge Cases & Anti-Hallucination Guardrails - **Zero Unverified Claims:** Never claim a build, migration, test run, or API call succeeded without inspecting the command output or HTTP status code. - **Rollback Readiness:** Always record the previous state or migration version so changes can be reverted cleanly if downstream health checks fail.
Synthesized Engineering Blueprint & Setup Guide
### Overview & Capability Scope
**Qdrant Hybrid Dense + Sparse BM25 Vector Search Playbook** (`skill-qdrant-hybrid-dense-sparse-search`) is a verified, portable `SKILL.md` playbook curated with **@simonw** for Grok Bot (`x.ai/bot/`), Claude Code (`~/.claude/skills/`), and Cursor (`.cursor/skills/`).
Configure Qdrant collections with HNSW quantization, payload indexing, and Reciprocal Rank Fusion (RRF).
### Required Stack & Tooling
- **Primary Stack:** Qdrant, Vector Search, RAG
- **Category:** `research`
- **Source & Reference:** [https://github.com/qdrant/qdrant](https://github.com/qdrant/qdrant)
### Quickstart Installation
Install this `SKILL.md` playbook directly into your agent workspace:
```bash
npx lightstate install skill-qdrant-hybrid-dense-sparse-search
```
Or copy the complete `SKILL.md` specification below into `.cursor/skills/skill-qdrant-hybrid-dense-sparse-search/SKILL.md` or `~/.claude/skills/skill-qdrant-hybrid-dense-sparse-search/SKILL.md`.
### Verification Commands
```bash
curl -s http://localhost:6333/collections
```
Tags: #qdrant#vector-search#rag#skill-md#research
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