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/.

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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?"

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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."

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Step 3: Dispatch Explorer-Critic

After both Explorer agents complete, dispatch the Explorer-Critic with the full combined dataset list.

Task prompt: "You are an Explorer-Critic agent. Research question: [question].
Empirical strategy: [strategy].
Variables needed — Treatment: [X], Outcome: [Y], Controls: [list],
Time period: [period], Geography: [geo], Unit: [unit].

Here is the combined dataset list from the Explorer agents:
[paste all Explorer findings]

Apply the 5-point critique to each dataset:
1. Measurement validity
2. Sample selection
3. External validity
4. Identification compatibility
5. Known issues

Produce adjusted feasibility grades and deal-breaker flags.
Follow the Explorer-Critic agent instructions."

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Step 4: Produce Ranked Output

After the Explorer-Critic completes, compile the final ranked report:

1. Sort datasets by adjusted feasibility grade (A first, then B, then C, then D).
2. Within each grade, sort by identification compatibility score (highest first).
3. Separate out deal-breaker datasets into the rejection table.

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Step 5: Save Report

Save to quality_reports/data_exploration_[sanitized_topic].md:

# Data Exploration: [Topic]

**Date:** [YYYY-MM-DD]
**Research question:** [one sentence]
**Empirical strategy:** [method]
**Variables sought:** Treatment = [X], Outcome = [Y], Controls = [list]

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## Top Candidates (Grade A–B)

### 1. [Dataset Name] — Grade: A/B

**Provider:** [Name] | **Access:** [Public/Restricted/etc.] | **URL:** [link]

**Coverage:** [time period] | [geography] | [unit of observation] | N ≈ [size]

**Key Variables:**
- Treatment proxy: [variable]
- Outcome: [variable]
- Controls available: [list]

**Explorer-Critic Assessment:**
- Measurement validity: [1-2 sentences]
- Sample selection: [1-2 sentences]
- External validity: [1-2 sentences]
- Identification compatibility: [focused on the proposed strategy]
- Known issues: [specific documented problems]

**Bottom line:** [1-2 sentences — viable and under what conditions]

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[Repeat for all A and B grade datasets]

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## Accessible With Effort (Grade C)

[Brief summaries — name, access path, main limitation, why C not B]

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## Rejection Table

| Dataset | Reason for Rejection | Deal-breaker? |
|---------|---------------------|---------------|
| [Name] | [Explorer-Critic finding] | YES/NO |

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## Recommended Path Forward

1. **Best dataset:** [Name] — [one sentence why]
2. **Fallback if [best] unavailable:** [Name] — [why it's second choice]
3. **Access steps for [best]:** [specific actions needed — download link, application URL, IRB requirements]

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## Next Steps

- **`/data-analysis [dataset]`** — begin analysis with the recommended dataset
- **`/lit-review [topic]`** — check if papers in the literature use these datasets (helps validate choice)

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Important

• Identification compatibility is the most important criterion. A perfectly accessible dataset that can't support the proposed empirical strategy is useless. The Explorer-Critic's grade on this dimension should drive the recommendation.
• Access level affects timeline. An FSRDC dataset may take 1-2 years to access. A public download can start today. Make this tradeoff explicit.
• Don't reject C-grade datasets outright. A FSRDC dataset with perfect identification fit may be the right choice for a dissertation. Present the access path clearly.