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 Felpix-Studios/social-science-research --skill data-findergit clone --depth 1 https://github.com/Felpix-Studios/social-science-researchWrote 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/felpix-studios/social-science-research/data-finder)<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.
<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>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 | $0.00049 | $0.01969 |
| Opus 5 | $0.00024 | $0.00984 |
| Sonnet 5 | $0.00010 | $0.00394 |
| Haiku 4.5 | $0.00005 | $0.00197 |
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.
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
-
Find the most recent
quality_reports/project_spec_*.mdorquality_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)
-
Read
references/domain-profile.mdif it exists — extract the Common Datasets section (domain-specific datasets to check first). -
If no research spec exists, extract the variables and strategy from
$ARGUMENTSdirectly. 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."
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 · 213 lines · 49 tokens per session scan A cd32cc43ddee
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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