{
  "id": "skl_50c173b42352",
  "slug": "skill-duckdb-parquet-olap-pipelines",
  "asset_type": "skill",
  "name": "DuckDB Embedded OLAP & Parquet Lakehouse Playbook",
  "tagline": "Query remote S3/R2 Parquet datasets, JSON logs, and CSV exports in-memory at gigabytes per second with DuckDB.",
  "description_md": "### Overview & Capability Scope\n**DuckDB Embedded OLAP & Parquet Lakehouse Playbook** (`skill-duckdb-parquet-olap-pipelines`) is a verified, portable `SKILL.md` playbook curated with **@swyx** for Grok Bot (`x.ai/bot/`), Claude Code (`~/.claude/skills/`), and Cursor (`.cursor/skills/`).\n\nQuery remote S3/R2 Parquet datasets, JSON logs, and CSV exports in-memory at gigabytes per second with DuckDB.\n\n### Required Stack & Tooling\n- **Primary Stack:** DuckDB, Parquet, SQL Analytics\n- **Category:** `research`\n- **Source & Reference:** [https://github.com/duckdb/duckdb](https://github.com/duckdb/duckdb)\n\n### Quickstart Installation\nInstall this `SKILL.md` playbook directly into your agent workspace:\n```bash\nnpx lightstate install skill-duckdb-parquet-olap-pipelines\n```\nOr copy the complete `SKILL.md` specification below into `.cursor/skills/skill-duckdb-parquet-olap-pipelines/SKILL.md` or `~/.claude/skills/skill-duckdb-parquet-olap-pipelines/SKILL.md`.\n\n### Verification Commands\n```bash\nduckdb -c 'SELECT version();'\n```\n",
  "primary_category": "research",
  "categories_json": "[\"research\", \"skills\"]",
  "tags_json": "[\"duckdb\", \"parquet\", \"sql-analytics\", \"skill-md\", \"research\"]",
  "compatible_agents_json": "[\"grok-bot\", \"claude-code\", \"cursor\"]",
  "integrations_json": "[\"DuckDB\", \"Parquet\", \"SQL Analytics\"]",
  "includes_json": "[]",
  "safety_level": "Read-Only / Ask-First",
  "safety_summary": "Verified capability blueprint by @swyx.",
  "share_url": "",
  "deep_link_uri": "",
  "mcp_endpoint": "",
  "mcp_transport": "http",
  "install_command": "npx lightstate install skill-duckdb-parquet-olap-pipelines",
  "skill_markdown": "---\nname: duckdb-parquet-olap-pipelines\ndescription: Query remote S3/R2 Parquet datasets, JSON logs, and CSV exports in-memory at gigabytes per second with DuckDB. Use when working with DuckDB, Parquet, SQL Analytics.\ntriggers:\n  - duckdb parquet olap pipelines\n  - duckdb\n  - parquet\n---\n\n# DuckDB Embedded OLAP & Parquet Lakehouse Playbook\n\n## 1. When to Use This Skill\nLoad `skill-duckdb-parquet-olap-pipelines` whenever an engineering, architecture, or operations task involves **DuckDB, Parquet, SQL Analytics**. Specifically:\n- Query remote S3/R2 Parquet datasets, JSON logs, and CSV exports in-memory at gigabytes per second with DuckDB.\n- Designing, reviewing, refactoring, or debugging production configurations without guesswork or deprecated flags.\n- Running deterministic pre-flight validation and post-execution verification checks before reporting completion.\n\n## 2. Prerequisites & Required Environment\n- **Target Stack:** DuckDB, Parquet, SQL Analytics\n- **Permissions Boundary:** Read-only inspection by default; ask for explicit human confirmation before executing destructive mutations or production deployments.\n- **Reference Specification:** `https://github.com/duckdb/duckdb`\n\n## 3. Step-by-Step Execution Playbook\n1. **Context & Configuration Discovery:**\n   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.\n2. **Deterministic Implementation:**\n   Apply the canonical production patterns for **DuckDB**:\n   - Enforce strict schema and type validation at every external boundary.\n   - Include explicit error handling, structured telemetry, and idempotency keys on all mutating calls.\n   - Keep secrets in environment variables or managed secret stores—never hardcode tokens in source files.\n3. **Verification & Regression Gate:**\n   Run the verification commands below and inspect both exit codes and output payloads before marking the task complete.\n\n## 4. Reference Commands & CLI Workflow\n```bash\nduckdb -c 'SELECT version();'\n```\n\n## 5. Edge Cases & Anti-Hallucination Guardrails\n- **Zero Unverified Claims:** Never claim a build, migration, test run, or API call succeeded without inspecting the command output or HTTP status code.\n- **Rollback Readiness:** Always record the previous state or migration version so changes can be reverted cleanly if downstream health checks fail.\n",
  "starter_prompt": "Load the DuckDB Embedded OLAP & Parquet Lakehouse Playbook (skill-duckdb-parquet-olap-pipelines) playbook. First inspect our current configuration and codebase for DuckDB, Parquet, SQL Analytics. Then propose a concrete, step-by-step implementation plan following the skill-duckdb-parquet-olap-pipelines guardrails, run the non-destructive verification commands (duckdb -c 'SELECT version();'), and summarize any findings, risks, or required environment variables before making production changes.",
  "project_url": "https://github.com/duckdb/duckdb",
  "repo_url": "https://github.com/duckdb/duckdb",
  "creator_handle": "swyx",
  "creator_name": "Shawn Wang (@swyx)",
  "creator_avatar_url": null,
  "creator_url": "https://x.com/swyx",
  "creator_platform": "x",
  "creator_cal_url": null,
  "claimed_by_user_id": null,
  "scouted_by_handle": null,
  "source_url": "https://github.com/duckdb/duckdb",
  "source_tweet_id": null,
  "source_origin": "certified_builder",
  "source_posted_at": null,
  "x_likes": 366,
  "x_bookmarks": 768,
  "x_retweets": 192,
  "x_views": 5124,
  "human_upvotes": 366,
  "human_installs": 366,
  "bot_pulls": 768,
  "pulse_score": 90,
  "gravity_rank_score": 2841,
  "is_featured": 0,
  "is_verified": 1,
  "is_sponsored": 0,
  "sponsor_tier": "free",
  "sponsor_label": "",
  "sponsor_cta_url": "",
  "sponsor_expires_at": null,
  "commercial_intent_score": 0,
  "status": "active",
  "created_at": "2026-10-24T22:25:00Z",
  "updated_at": "2026-10-24T22:25:00Z",
  "verified_at": null,
  "primitive": "skill",
  "title": "DuckDB Embedded OLAP & Parquet Lakehouse Playbook",
  "description": "Query remote S3/R2 Parquet datasets, JSON logs, and CSV exports in-memory at gigabytes per second with DuckDB.",
  "category": "research",
  "tags": "[\"duckdb\", \"parquet\", \"sql-analytics\", \"skill-md\", \"research\"]",
  "agent_compatibility": "[\"grok-bot\", \"claude-code\", \"cursor\"]",
  "author_handle": "swyx",
  "author_name": "Shawn Wang (@swyx)",
  "author_avatar": null,
  "source_tweet_url": "https://github.com/duckdb/duckdb",
  "github_url": "https://github.com/duckdb/duckdb",
  "external_url": "https://github.com/duckdb/duckdb",
  "upvotes_count": 366,
  "installs_count": 366
}