Borrowing it
Nothing to install: this file belongs to justmytwospence/strava-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/justmytwospence/strava-mcp/main/.agents/skills/duckdb-docs/SKILL.mdgit clone --depth 1 https://github.com/justmytwospence/strava-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/justmytwospence/strava-mcp/duckdb-docs)<a href="https://agentmods.dev/skills/justmytwospence/strava-mcp/duckdb-docs"><img src="https://agentmods.dev/badge/skills/justmytwospence/strava-mcp/duckdb-docs/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/justmytwospence/strava-mcp/duckdb-docs"><img src="https://agentmods.dev/badge/skills/justmytwospence/strava-mcp/duckdb-docs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.01482 |
| Opus 5.5 | $0.00014 | $0.00593 |
| Sonnet 5.5 | $0.00007 | $0.00296 |
| Haiku 4.5 | $0.00003 | $0.00148 |
Grade A, and why
duckdb-docs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to duckdb-docs — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are helping the user find relevant DuckDB or DuckLake documentation.
Query: $@
Follow these steps in order.
Step 1 — Check DuckDB is installed
command -v duckdb
If not found, delegate to /duckdb-skills:install-duckdb and then continue.
Step 2 — Ensure required extensions are installed
duckdb :memory: -c "INSTALL httpfs; INSTALL fts;"
If this fails, report the error and stop.
Step 3 — Choose the data source and extract search terms
The query is: $@
Data source selection
There are two search indexes available:
| Index | Remote URL | Local cache filename | Versions | Use when |
|---|---|---|---|---|
| DuckDB docs + blog | https://duckdb.org/data/docs-search.duckdb |
duckdb-docs.duckdb |
lts, current, blog |
Default — any DuckDB question |
| DuckLake docs | https://ducklake.select/data/docs-search.duckdb |
ducklake-docs.duckdb |
stable, preview |
Query mentions DuckLake, catalogs, or DuckLake-specific features |
Both indexes share the same schema:
| Column | Type | Description |
|---|---|---|
chunk_id |
VARCHAR (PK) |
e.g. stable/sql/functions/numeric#absx |
page_title |
VARCHAR |
Page title from front matter |
section |
VARCHAR |
Section heading (null for page intros) |
breadcrumb |
VARCHAR |
e.g. SQL > Functions > Numeric |
url |
VARCHAR |
URL path with anchor |
version |
VARCHAR |
See table above |
text |
TEXT |
Full markdown of the chunk |
By default, search DuckDB docs and filter to version = 'lts'. Use different versions when:
- The user explicitly asks about
current/nightly features →version = 'current' - The user asks about a blog post or wants background/motivation →
version = 'blog' - The user asks about DuckLake → search the DuckLake index with
version = 'stable' - When unsure, omit the version filter to search across all versions.
Search terms
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 152 lines · 35 tokens per session scan A e6388de62576
duckdb-docs is a skill published in the GitHub repository justmytwospence/strava-mcp (0 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 1,482 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to duckdb-docs, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
usage
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics…
dlt-connector
Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever…
generate-mdl
Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or…
wren
Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the wren CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue…
analytical-databases-expert
Design and query columnar analytical stores: DuckDB, ClickHouse and cloud warehouses, including file formats, partitioning, sort keys and cost control. Use when the user mentions DuckDB, ClickHouse, Parquet, columnar storage, OLAP, a data warehouse or lakehouse, analytical queries over large tables, or when the task…
duckdb
Use when analytical SQL must run in-process with no server: Parquet/CSV/JSON/Arrow queried in place, OLAP embedded in an app or notebook, a slow pandas groupby on multi-GB data, or S3/lakehouse data read without downloading. NOT a multi-user analytics server (that is clickhouse-analytics), NOT an app's transactional…