Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-datacommonsgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote 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/alterlab-ieu/alterlab-academic-skills/alterlab-datacommons)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-datacommons"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-datacommons/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/alterlab-ieu/alterlab-academic-skills/alterlab-datacommons"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-datacommons.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 | $0.00081 | $0.02755 |
| Opus 5.5 | $0.00032 | $0.01102 |
| Sonnet 5.5 | $0.00016 | $0.00551 |
| Haiku 4.5 | $0.00008 | $0.00276 |
Grade A, and why
alterlab-datacommons scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Read WebFetch Bash(curl:*) Bash(python:*) This is a copy
84% identical to datacommons-client — 138 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 — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Commons Client
Overview
Provides comprehensive access to the Data Commons Python API v2 for querying statistical observations, exploring the knowledge graph, and resolving entity identifiers. Data Commons aggregates data from census bureaus, health organizations, environmental agencies, and other authoritative sources into a unified knowledge graph.
Verified against datacommons-client 2.1.6 (current as of 2026-09; Python ≥ 3.10). This is the V2 client (package datacommons_client), not the legacy datacommons (V1) package — the two have different APIs; do not mix them. Keyword names matter: the place-hierarchy helpers take place_dcids, fetch_entity_names takes entity_dcids, and fetch_property_values takes properties — passing node_dcids to them raises TypeError.
When to Use This Skill
- Pulling population, economic, health, education, or environmental statistics for places (countries, states, counties, cities)
- Building cross-place or time-series comparisons from harmonized public sources (census, BLS, WHO, World Bank, EPA, …)
- Resolving place names, coordinates, or Wikidata IDs to Data Commons DCIDs
- Exploring the Data Commons knowledge graph (place hierarchies, properties, statistical-variable definitions)
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| A specific FRED economic series (GDP, CPI, interest rates) with vintage/revision data | alterlab-fred |
| U.S. Treasury fiscal data (debt, spending, revenue) | alterlab-usfiscaldata |
| NCI Imaging Data Commons (CT/MR/pathology images) — a different "Data Commons" | alterlab-imaging-data-commons |
| Spatial joins, buffers, or mapping of geometries | alterlab-geopandas |
Installation
Install the Data Commons V2 client with Pandas support (extra is lowercase pandas):
uv pip install "datacommons-client[pandas]"
For basic usage without Pandas:
uv pip install datacommons-client
Core Capabilities
The Data Commons API consists of three main endpoints, each detailed in dedicated reference files:
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · +37 lines 133396ce3bdb
- 1mo ago First seen · 290 lines · 81 tokens per session scan A 188f2f69a046
alterlab-datacommons is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (78 stars, last pushed 2d ago), licensed MIT. It adds 81 tokens to every session and 2,755 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 84% identical to datacommons-client, differing in 138 lines, and is treated as a copy.
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