data-finder

data-finder is a skill for Claude Code from Felpix-Studios/social-science-research. It costs 49 tokens per session (1,969 once invoked), scanned A, original, MIT.

A research tool for finding and assessing datasets, or organized collections of research data, for a specific research question. It checks whether candidate datasets fit the study's variables, method, time period, location, and unit of observation.

In plain words
What is it for?
Use it to find datasets for a research project, evaluate their suitability, and produce a ranked list with feasibility grades.
Why use it?
It reduces the work of searching many data sources and helps identify practical data choices before you begin analysis.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the social-science-research plugin — 13 skills, 9 agents, 3 hooks shipped together

Good fit Use it to find datasets for a research project, evaluate their suitability, and produce a ranked list with feasibility grades.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/felpix-studios/social-science-research/data-finder
Install

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.

Any agent
npx skills add Felpix-Studios/social-science-research --skill data-finder
Clone the repo
git clone --depth 1 https://github.com/Felpix-Studios/social-science-research

Made for: Claude Code.

Or install social-science-research, the plugin that ships this one along with the rest of its 13 skills, 9 agents, 3 hooks.

Wrote 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.

agentmods badge for data-finder

README.md
[![agentmods](https://agentmods.dev/badge/skills/felpix-studios/social-science-research/data-finder/github.svg)](https://agentmods.dev/skills/felpix-studios/social-science-research/data-finder)
Your own site
<a href="https://agentmods.dev/skills/felpix-studios/social-science-research/data-finder"><img src="https://agentmods.dev/badge/skills/felpix-studios/social-science-research/data-finder/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.

agentmods 80×15 button for data-finder

Your own site · 80×15
<a href="https://agentmods.dev/skills/felpix-studios/social-science-research/data-finder"><img src="https://agentmods.dev/badge/skills/felpix-studios/social-science-research/data-finder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,969 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.01969
Opus 5 $0.00024 $0.00984
Sonnet 5 $0.00010 $0.00394
Haiku 4.5 $0.00005 $0.00197

Measured 10d ago against content hash cd32cc43ddee, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

data-finder 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.

skills/data-finder/SKILL.md · 213 lines

How it starts

The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Finder

Find and assess datasets for your research question. Two Explorer agents search in parallel across data source categories; an Explorer-Critic then stress-tests each candidate against the research design.

Input: $ARGUMENTS — a topic, or from spec to read the research question from quality_reports/.

Step 1: Read Research Context

  1. Find the most recent quality_reports/project_spec_*.md or quality_reports/specs/*.md — extract:

    • Research question
    • Empirical strategy (DiD, RDD, IV, etc.)
    • Treatment variable (what varies)
    • Outcome variable (what we measure)
    • Controls needed
    • Time period of interest
    • Geography (national, state, county, individual)
    • Unit of observation (individual, household, firm, establishment)
  2. Read references/domain-profile.md if it exists — extract the Common Datasets section (domain-specific datasets to check first).

  3. If no research spec exists, extract the variables and strategy from $ARGUMENTS directly. If the request is vague, ask: "What are the treatment and outcome variables, and what empirical strategy did you have in mind?"

Step 2: Dispatch Two Explorer Agents in Parallel

Split the source categories between two Explorer agents to parallelize the search.

Explorer A — Institutional Data:

Task prompt: "You are an Explorer agent. Research question: [question].
Empirical strategy: [strategy].
Variables needed — Treatment: [X], Outcome: [Y], Controls: [list],
Time period: [period], Geography: [geo], Unit: [unit].
Domain datasets (check first): [list from domain-profile if available].

Your source categories to search:
1. Public microdata (CPS, ACS, NHIS, MEPS, SIPP, QWI)
2. Administrative data (Medicare/Medicaid, IRS, SSA, vital statistics, court records)
3. Survey panels (PSID, HRS, Add Health, NLSY97/79, BHPS/UKHLS)

For each dataset found, produce the full Explorer report format.
Follow the Explorer agent instructions."

Explorer B — Broader and Alternative Sources:

Task prompt: "You are an Explorer agent. Research question: [question].
Empirical strategy: [strategy].
Variables needed — Treatment: [X], Outcome: [Y], Controls: [list],
Time period: [period], Geography: [geo], Unit: [unit].
Domain datasets (check first): [list from domain-profile if available].

Your source categories to search:
1. International data (World Bank, OECD, Eurostat, IMF, IPUMS International)
2. Novel/alternative (satellite, web scraping, proprietary, RCT registries)
3. Any field-specific datasets not covered by Explorer A

For each dataset found, produce the full Explorer report format.
Follow the Explorer agent instructions."

Read the full file on GitHub · 213 lines

Changes

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.

  1. 10d ago First seen · 213 lines · 49 tokens per session scan A cd32cc43ddee

Subscribe to this mod's changes

data-finder is a skill published in the GitHub repository Felpix-Studios/social-science-research (8 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 1,969 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 2026-08-31.

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