research-economic-frontiers

name

research-economic-frontiers

description

Find, select, design, execute, and independently audit frontier economics research using verified literature gaps, claim-specific contracts, diverse theory and empirical routes, reproducible analysis, and conservative contribution gates. Use for open or underexplored economics questions, paper ideas, research-gap searches, theoretical model development, causal or predictive empirical designs, replication projects, pre-analysis planning, long-horizon multi-agent economics research, referee reports, and claims that a contribution is novel or publication-ready.

Research Economic Frontiers

Explore ambitiously and make claims conservatively. Economics contributions do not share one proof standard, so classify the claim before selecting methods.

Non-negotiable rules

• Verify central references from opened records. Never invent citations.
• Scope every novelty claim to sources, queries, and a search date.
• Freeze the question, claim, population, timing, estimand, and outcome before confirmatory analysis.
• Keep confirmatory, exploratory, and post-hoc work visibly separate.
• Never upgrade association to causation.
• Never fabricate data, results, access rights, consent, ethics approval, or disclosure.
• Do not access confidential, restricted, or personal data without explicit authorization and the required agreements.
• Record null results, deviations, failed routes, and unresolved threats.
• Require an independent audit for a candidate contribution and external human review for an externally validated contribution.

1. Define the run

Infer or obtain:

• field, topic, population, and decision context;
• whether to find a question or evaluate a supplied one;
• allowed claim lanes: theory, causal, predictive, descriptive, policy, or mixed;
• available data, model, compute, time, and agent capacity;
• research mode: literature, replication, or blind-benchmark;
• registration, ethics, privacy, disclosure, and data-access constraints; and
• desired output: ranked questions, design, analysis, replication package, paper, or referee report.

Use literature mode by default. Blind work cannot pass the novelty gate until a post-hoc literature audit is complete.

2. Initialize persistent state

From this skill directory, run:

python3 scripts/init_workspace.py <project-directory> \
  --title "<short title>" \
  --claim-type <claim-type> \
  --mode literature \
  --budget "<resource budget>"

Do not overwrite an existing project unless the user explicitly requests it. Maintain:

• QUESTION.md: frozen question, scope, outcomes, and decision relevance;
• NOVELTY.md: reproducible search and closest-work ledger;
• CONTRIBUTION_CONTRACT.md: claim lane, success test, and non-counting output;
• ROUTES.md: question, theory, data, and design route registry;
• THEORY.md: mechanism, assumptions, propositions, and predictions;
• DATA.md: provenance, access, legality, measurement, and sample construction;
• IDENTIFICATION.md: estimand, assumptions, threats, and falsification;
• ANALYSIS_PLAN.md: locked and exploratory analyses;
• RESULTS.md: complete results, deviations, nulls, and diagnostics;
• REPRODUCIBILITY.md: environment, master command, inputs, and outputs;
• REFEREE_REPORT.md: independent objections and verdict;
• PAPER.md: claim-aligned draft; and
• STATE.json: machine-readable gate status.

Before setting reproducibility_status to passed, create and verify a content-addressed receipt:

python3 skills/research-economic-frontiers/scripts/research_receipt.py create <project-directory>
python3 skills/research-economic-frontiers/scripts/research_receipt.py verify <project-directory>

The receipt covers every workspace file except its own output and local cache artifacts. It rejects symlinks and records only relative paths, sizes, SHA-256 hashes, and a deterministic tree hash. Regenerate it after any auditable file changes; do not treat a stale receipt as reproducibility evidence.

Validate periodically:

python3 skills/research-economic-frontiers/scripts/validate_workspace.py <project-directory>

Use --strict before any candidate-contribution claim. Validation requires a current RESEARCH_RECEIPT.json whenever reproducibility_status is passed.

3. Search and select a question

Read discovery-and-novelty.md.

Build a candidate pool before choosing. Search exact questions, mechanisms, outcomes, data settings, methods, and adjacent-field terminology across:

• recent working papers and registered studies;
• publisher and journal records;
• economics indexes and series;
• conference programs and research networks; and
• citation chains around the closest verified papers.

Verify every central paper with two independent records when available, including one durable identifier or authoritative record.

Score each candidate 1–5 on:

1. economic or policy importance;
2. verified novelty distance;
3. theory or design tractability;
4. data access and measurement quality;
5. identification or model discipline;
6. falsifiability and time-to-signal;
7. reproducibility; and
8. ethical, legal, and disclosure feasibility.

State the strongest rejection case for every candidate. Prefer a smaller clean question over a broad question that the available evidence cannot answer.

4. Freeze the contribution contract

Classify the primary claim and read claim-gates.md. Write:

1. the exact question and primary claim;
2. the closest prior work and the incremental contribution;
3. what would count as support, contradiction, or an informative null;
4. outcomes that do not count, including weaker or different claims;
5. the model, data, estimand, design, or prediction target;
6. load-bearing assumptions and rival explanations;
7. confirmatory analyses and the lock date;
8. allowed exploratory work and labeling rules;
9. registration, ethics, privacy, and disclosure obligations;
10. reproducibility outputs; and
11. the independent-audit standard.

If the question changes materially after locking, create a versioned contract and label the change. Do not silently rewrite success after seeing results.

5. Build diverse routes

Read orchestration.md when multiple agents or isolated contexts are available.

Create genuinely different route families across:

• mechanisms and theoretical primitives;
• datasets, populations, and measurement strategies;
• assignment or quasi-experimental sources;
• structural, reduced-form, experimental, descriptive, and predictive designs;
• alternative estimands and falsification tests; and
• replication or reanalysis routes.

Require a concrete artifact from each route: a verified gap, proposition, counterexample, data audit, estimand, design diagram, power calculation, simulation, falsification result, or executable specification.

Use route states:

• active
• promising
• blocked
• refuted
• merged
• design-ready
• evidence-ready

Block a route when it depends on inaccessible data, an assumption as strong as the conclusion, an underpowered design, or a measurement strategy that cannot support the claim. Reopen only for a materially new mechanism or source.

6. Lock, register, and execute

Before confirmatory analysis:

• complete the data and identification audits;
• declare exclusions, transformations, outcomes, specifications, inference, clustering, multiple-testing adjustments, and stopping rules;
• separate the untouched evaluation sample for predictive work;
• document power or minimum detectable effects when applicable;
• register field experiments as required and use an appropriate registration for other designs when useful; and
• record the lock or registration identifier in STATE.json.

Execute from raw inputs through a master command when legally possible. Preserve immutable raw data, transformation logs, environment versions, seeds, and machine-readable outputs. For restricted data, document the access path and provide a synthetic or test fixture where allowed.

Run confirmatory analyses first. Label every deviation and all exploratory work. Do not drop null or adverse results.

7. Apply the claim gate

Read claim-gates.md and downgrade the claim when a gate fails:

• theory: prove the proposition and audit assumptions and boundary cases;
• causal: defend identification, diagnostics, falsification, uncertainty, and external-validity limits;
• predictive: pass locked out-of-sample evaluation against a declared benchmark with no leakage;
• descriptive: verify provenance and uncertainty and use no causal language;
• policy: add incidence, equilibrium, distributional, and normative sensitivity;
• mixed: pass every lane used in the headline claim.

An interesting estimate is not enough. Match the words in the conclusion to what the gate supports.

8. Audit independently

Read referee-checklist.md. Give a fresh reviewer only the frozen contract, source ledger, paper, results, code and reproducibility record—not the authors' advocacy.

Require line-specific objections and one verdict:

• REJECT
• MAJOR-REVISION
• MINOR-REVISION
• CANDIDATE

Repair and re-audit every downstream claim affected by a change. A different agent can support CANDIDATE; only external human review can support externally-validated.

9. Package the outcome

For a candidate contribution, deliver:

• exact contribution and claim lane;
• verified novelty ledger;
• theory or identification dependencies;
• complete results including nulls and deviations;
• reproducible code, environment, and data instructions;
• independent referee report;
• limitations, external validity, ethics, and disclosures; and
• an explicit request for human expert review.

For an incomplete run, deliver:

• strongest supported result;
• exact unresolved gap;
• failed and blocked routes;
• reusable data, code, propositions, or diagnostics;
• what changed from the original contract; and
• the most informative next test.

Never force a publishable story from an uncooperative result.

Research integrity reference

Read sources-and-ethics.md before using registries, third-party data, restricted data, human-subjects material, or policy recommendations.