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 datacommonsorg/agent-toolkit --skill data-commons-child-places-researchergit clone --depth 1 https://github.com/datacommonsorg/agent-toolkitWrote 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/datacommonsorg/agent-toolkit/data-commons-child-places-researcher)<a href="https://agentmods.dev/skills/datacommonsorg/agent-toolkit/data-commons-child-places-researcher"><img src="https://agentmods.dev/badge/skills/datacommonsorg/agent-toolkit/data-commons-child-places-researcher/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/datacommonsorg/agent-toolkit/data-commons-child-places-researcher"><img src="https://agentmods.dev/badge/skills/datacommonsorg/agent-toolkit/data-commons-child-places-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00047 | $0.03860 |
| Opus 5 | $0.00023 | $0.01930 |
| Sonnet 5 | $0.00009 | $0.00772 |
| Haiku 4.5 | $0.00005 | $0.00386 |
Grade A, and why
data-commons-child-places-researcher 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Foundational Knowledge: Data Commons Graph Structure
Data Commons organizes data into two main structural hierarchies. Understanding these is key to choosing your place names and variables:
- Topics (Variable Hierarchy): A taxonomy of categories (e.g.,
Health->Clinical Data->Medical Conditions). Topics contain sub-topics and individual variables. - Places (Geographic Hierarchy): A taxonomy of spatial containment (e.g.,
World->Continent->Country->State->County).
Data Availability & Efficiency Tips:
- Direct Containment Efficiency: Querying the direct child places of a parent (e.g., all counties inside California) is highly optimized and returns faster than querying arbitrary cross-border place sets.
- Single-Place Routing: If the user's query asks for statistics about a single specific place (e.g., "population of France" or "GDP of California"), you MUST read the base skill resource at 'skill://data-commons-researcher/SKILL.md' instead.
1. The Three-Step Tool Pipeline
When researching statistics across child places within a parent entity, always separate your work into three distinct phases to avoid context bloat:
- Discovery (
search_child_indicators): Use this to find candidate variables matching the user's concept that are available at the sub-national/child level. - Assessment (
get_variable_metadata): Pass candidate variables and target child locations to retrieve structural metadata, ensuring the dataset matches the required temporal range, granularity, and source trust. - Retrieval (
get_child_observations): Fetch the actual timeseries arrays across all child places of a specified type once the variables and facets have been qualified.
CRITICAL: Always validate variable-place combinations first
- You MUST call
search_child_indicatorsfirst to verify that the variable exists for the specified child places. - You MUST call
get_variable_metadatato verify dataset facets (source, dates, coverage) for sampled child places before retrieving heavy observation arrays. - Only use DCIDs returned by
search_child_indicators- never guess or assume variable-place combinations. - This ensures data availability and prevents errors from invalid combinations.
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.
- 10d ago First seen · 281 lines · 47 tokens per session scan A 492817ed711c
data-commons-child-places-researcher is a skill published in the GitHub repository datacommonsorg/agent-toolkit (139 stars, last pushed 6d ago), licensed Apache-2.0. It adds 47 tokens to every session and 3,860 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…