Querying Big Datasets
flyrank-bih/flyrank-ml-internship-starter
Works with datasets far too big to download or load in pandas — SQL over remote Parquet with DuckDB, aggregate-then-model, iterate on samples.
Investigates geocoder data quality issues by querying live S3 parquet files via MotherDuck MCP.
$ npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install walkthru-earth/geocoding-playground investigate-data --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/walkthru-earth/geocoding-playground.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/investigate-data .claude/skills/investigate-data && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "investigate-data" agent skill from https://github.com/walkthru-earth/geocoding-playground/tree/main/.claude/skills/investigate-data into .claude/skills/investigate-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investigate-data", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/walkthru-earth/geocoding-playground/tree/main/.claude/skills/investigate-dataType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install walkthru-earth/geocoding-playground investigate-data --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/walkthru-earth/geocoding-playground.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/investigate-data .agents/skills/investigate-data && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "investigate-data" agent skill from https://github.com/walkthru-earth/geocoding-playground/tree/main/.claude/skills/investigate-data into .agents/skills/investigate-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investigate-data", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install walkthru-earth/geocoding-playground investigate-data --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/walkthru-earth/geocoding-playground.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/investigate-data .cursor/skills/investigate-data && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "investigate-data" agent skill from https://github.com/walkthru-earth/geocoding-playground/tree/main/.claude/skills/investigate-data into .cursor/skills/investigate-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investigate-data", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/walkthru-earth/geocoding-playground.git --path .claude/skills/investigate-data--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install walkthru-earth/geocoding-playground investigate-data --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/walkthru-earth/geocoding-playground.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/investigate-data .gemini/skills/investigate-data && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "investigate-data" agent skill from https://github.com/walkthru-earth/geocoding-playground/tree/main/.claude/skills/investigate-data into .gemini/skills/investigate-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investigate-data", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install walkthru-earth/geocoding-playground investigate-dataInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/walkthru-earth/geocoding-playground.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/investigate-data .github/skills/investigate-data && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "investigate-data" agent skill from https://github.com/walkthru-earth/geocoding-playground/tree/main/.claude/skills/investigate-data into .github/skills/investigate-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investigate-data", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install walkthru-earth/geocoding-playground investigate-data --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/walkthru-earth/geocoding-playground.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/investigate-data .opencode/skills/investigate-data && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "investigate-data" agent skill from https://github.com/walkthru-earth/geocoding-playground/tree/main/.claude/skills/investigate-data into .opencode/skills/investigate-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investigate-data", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
investigate-dataInvestigates geocoder data quality issues by querying live S3 parquet files via MotherDuck MCP.
Investigate Data is an agent skill from walkthru-earth/geocoding-playground. Investigates geocoder data quality issues by querying live S3 parquet files via MotherDuck MCP. Use this skill when the user reports a bug in autocomplete results, duplicate entries, missing data, wrong city/street/postcode results, or asks to check data for a specific country. Also use when the user says 'check the data', 'investigate', 'query S3', 'what does the index look like', 'how many streets/cities/postcodes', or 'verify the data'. Do NOT use for frontend UI bugs, build issues, or code refactoring.
Its SKILL.md is about 940 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering DataFrames, File uploads and storage and Frontend development. It works with Model Context Protocol and DuckDB. The repository describes itself as: Browser-based geocoder powered by DuckDB-WASM and Overture Maps, 469M+ addresses, zero backend. The licence is CC-BY-4.0.
7 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d16cf53. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadGrepGlobmcp__motherduck__execute_queryFrom allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are sql).
From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
s3.us-west-2.amazonaws.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Investigate Data loads about 935 tokens when it runs. Until then it costs about 132 tokens; SKILL.md has 260 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from walkthru-earth/geocoding-playground at commit d16cf53, republished under its CC-BY-4.0 licence (© walkthru-earth). 260 words, ~935 tokens.
.claude/skills/investigate-data/SKILL.md (or your agent's skills folder).Investigate geocoder data issues by querying live parquet files on S3.
https://s3.us-west-2.amazonaws.com/us-west-2.opendata.source.coop/walkthru-earth/indices/addresses-index/v4/release=<RELEASE>/Substitute the current release, newest is 2026-04-15.0 as of 2026-04-18. The app auto-discovers releases, so never hard-code an old one.
Understand the issue: What country, what index, what unexpected behavior?
Check the schema first if unsure about columns:
DESCRIBE SELECT * FROM read_parquet('...BASE_URL.../city_index/country=XX/data_0.parquet') LIMIT 1;Query the relevant index using MotherDuck MCP (mcp__motherduck__execute_query):
city_index/country=XX/data_0.parquetstreet_index/country=XX/data_0.parquetpostcode_index/country=XX/data_0.parquetnumber_index/country=XX/data_0.parquetgeocoder/country=XX/h3_res4=HEXHASH/bucket=NN/data_0.parquetmanifest.parquet, tile_index.parquetDiagnose the root cause using these patterns:
GROUP BY key_cols HAVING count(*) > 1SELECT count(*) FILTER (col IS NULL)list_has_any(tiles, [expected_tiles])(bbox_max - bbox_min) for unreasonably large valueslen(tiles) distribution for a street nameSimulate the fix by running the corrected query and verifying output.
Cross-validate by checking 2-3 other countries for the same pattern.
Report with:
full_address, the only differentiator is the unit column. 195 Clearview AVE in Ottawa = 328 rows. Always SELECT unit when investigating apparent duplicatesAfter pipeline rebuilds, verify Parquet optimization with:
-- number_index: should have many small row groups + bloom filters
SELECT num_rows, num_row_groups, file_size_bytes
FROM parquet_file_metadata('...BASE_URL.../number_index/country=NL/data_0.parquet');
-- Bloom filters present?
SELECT path_in_schema, bloom_filter_offset, bloom_filter_length
FROM parquet_metadata('...BASE_URL.../number_index/country=NL/data_0.parquet')
WHERE path_in_schema = 'street_lower' LIMIT 5;
-- Geocoder tile bloom filters on street?
SELECT path_in_schema, bloom_filter_offset
FROM parquet_metadata('...BASE_URL.../geocoder/country=NL/h3_res4=841969dffffffff/bucket=_/data_0.parquet')
WHERE path_in_schema = 'street' LIMIT 3;© walkthru-earth, CC-BY-4.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .claude/skills/investigate-data of walkthru-earth/geocoding-playground.
Open the folder on GitHubat commit d16cf53
Investigate Data next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Investigate Data this skillwalkthru-earth/geocoding-playground | 153 | — | ~935 | Automated safety check: Pass | CC-BY-4.0 | |
| Querying Big Datasetsflyrank-bih/flyrank-ml-internship-starter | 140 | — | ~750 | Automated safety check: Pass | Custom licence | |
| Data Cleaningericrisco/rsc-harness | 180 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Retentioneering Product Analyticsretentioneering/retentioneering-tools | 927 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Webapp Buildersidequery/sidemantic | 129 | — | ~5.5k | Automated safety check: Pass | AGPL-3.0 |
flyrank-bih/flyrank-ml-internship-starter
Works with datasets far too big to download or load in pandas — SQL over remote Parquet with DuckDB, aggregate-then-model, iterate on samples.
ericrisco/rsc-harness
A skill your agent uses when a raw table is too dirty to trust — nulls, sentinels, duplicate rows, category sprawl, mixed types, bad dates — and you need a re-runnable clean() plus a schema gate…
Jeffallan/claude-skills
Handles pandas DataFrame work: cleaning, merging, groupby aggregation, pivots, time-series resampling and memory tuning, with checks on dtypes, shapes and nulls.
retentioneering/retentioneering-tools
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering.
sidequery/sidemantic
Build interactive analytics webapps, demos, dashboards, or embedded app surfaces from Sidemantic semantic models using copyable component primitives and deterministic query inspection.
monarchjuno/vibe-investing
Fetch financial, market, economic, fundamental, news, options, crypto, ETF, index, and macro data through the OpenBB Python interface instead of the OpenBB MCP server.
walkthru-earth/geocoding-playground
Tests the smart autocomplete engine against known test scenarios from the study docs.
Works with
Categories
Investigates geocoder data quality issues by querying live S3 parquet files via MotherDuck MCP. Investigate Data is an agent skill from walkthru-earth/geocoding-playground. Investigates geocoder data quality issues by querying live S3 parquet files via MotherDuck MCP.
Investigate Data fits situations like: the user reports a bug in autocomplete results; duplicate entries; wrong city/street/postcode results; asks to check data for a specific country.
Run `npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a claude-code`. Or copy the skill folder (.claude/skills/investigate-data in walkthru-earth/geocoding-playground) into .claude/skills/investigate-data in your project. Claude Code loads it when a task matches its description.
Run `npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a codex`. Or copy the skill folder (.claude/skills/investigate-data in walkthru-earth/geocoding-playground) into .agents/skills/investigate-data in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add walkthru-earth/geocoding-playground --skill investigate-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/investigate-data, .gemini/skills/investigate-data, .github/skills/investigate-data and .opencode/skills/investigate-data in your project.
SKILL.md names no scripts, command-line tools or credentials: Investigate Data is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Grep, Glob, mcp__motherduck__execute_query.
SKILL.md names 1 domain. In commands or code: s3.us-west-2.amazonaws.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Investigate Data is published under the CC-BY-4.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 935 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Investigate Data: Querying Big Datasets (flyrank-bih/flyrank-ml-internship-starter, 140 stars), Data Cleaning (ericrisco/rsc-harness, 180 stars), Pandas Pro (Jeffallan/claude-skills, 12k stars) and Retentioneering Product Analytics (retentioneering/retentioneering-tools, 927 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
walkthru-earth (a GitHub organization) maintains it in walkthru-earth/geocoding-playground, which has 153 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on June 24, 2026.
Source: walkthru-earth/geocoding-playground on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.