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Business judgment on AI products

applied-micro-skills

Insufficient evidence

Helps applied economics researchers go from data and regressions to paper tables, and checks that the numbers actually reproduce.

Not a business yet Early AI + DevOpen-source traction 48
Team / maker
kennethkhoocy
First tracked here
2026-07-22
Last updated here
2026-08-11
Product site
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01

Why this would be needed

Start inside the user's day · Public facts + observable behavior · 2026-09-23

Use case

Applied-micro researchers (PhD students, junior faculty, RAs) take restricted data such as WRDS, run event studies and other regressions in Stata, produce publication-grade tables, and must ensure the paper's numbers actually reproduce.

The old approach is researchers maintaining their own do-files, hand-assembling tables, or relying on personal script libraries, colleagues' code, and generic LLM chats for Stata/WRDS details; none of these enforce end-to-end reproducibility auditing or publication-grade tables.

The public materials point to a pain that is inferred from product capability and workflow structure: the path from raw data to paper tables is highly manual in Stata, and reproducibility audits often surface inconsistencies only at referee or coauthor review, forcing costly rework. No user complaint or case directly confirms this yet.

xOcto's call

This is a guardrail system for AI-assisted research, with a real insight but no evidence of traction yet.

The trend is journals asking whether published numbers really come from the data. The entry is reproducibility audits in empirical economics: check tables and code before submission, paid from grants — not a generic paper writer.

Reason to use it

Why users would choose it

Inference: versus writing do-files by hand or asking a generic LLM, it packages reproducibility auditing, LLM-pipeline methods, event studies, WRDS, Stata, and publication-grade tables into callable skills, so a researcher can generate tables and check the numbers in one flow, cutting the manual table-assembly and later rework step; researchers writing empirical papers who must hand referees or coauthors reproducible results would pick it during writing and review.

Where the easy answer breaks down

The tension worth following

① Whether forks and real adoption cases appear in three months — someone actually using it in; their paper workflow; ② Whether skills are added or updated (a moving project means the author is still using it); ③ Whether it appears in a known lab's or course's recommended list — academic tools spread…

If this is your job

Worth dissecting. Inference: versus writing do-files by hand or asking a generic LLM, it packages reproducibility auditing, LLM-pipeline methods, event studies, WRDS, Stata, and publication-grade tables into callable skills, so a researcher can generate tables and check the numbers in one flow, cutting the manual table-assembly and later rework step; researchers writing empirical papers who must hand referees or coauthors reproducible results would pick it during writing and review.

Entry and what to borrow

when building AI-assisted tooling, make verification an explicit product feature, not an implicit promise. Three moves to copy: add a reproducible checkpoint to every output step (canary tests); when a gate fails, diagnose whether it is input quality or model quality before deciding what to change; and demand byte-level evidence parity, leaving no room for "approximately consistent."

Evidence and risk

Not disclosed. MIT open source, no paid service, no SaaS, no sponsor page. ① Whether forks and real adoption cases appear in three months — someone actually using it in; their paper workflow; ② Whether skills are added or updated (a moving project means the author is still using it); ③ Whether it appears in a known lab's or course's recommended list — academic tools spread…

What this judgment rests on
Public fact

Helps applied economics researchers go from data and regressions to paper tables, and checks that the numbers actually reproduce.

Workflow reasoning

Inference: versus writing do-files by hand or asking a generic LLM, it packages reproducibility auditing, LLM-pipeline methods, event studies, WRDS, Stata, and publication-grade tables into callable skills, so a researcher can generate tables and check the numbers in one flow, cutting the manual table-assembly and later rework step; researchers writing empirical papers who must hand referees or coauthors reproducible results would pick it during writing and review.

The unknown that could change the call

An English validation note will follow from the public evidence.

01 · Value Insufficient evidence

The product claims to help users complete: “Helps applied economics researchers go from data and regressions to paper tables, and checks that th”. User evidence has not yet verified pain intensity or the cost of doing without it.

02 · Consensus Insufficient evidence

The assessment is recorded; an English explanation is pending.

03 · Model Insufficient evidence

The assessment is recorded; an English explanation is pending.

04 · Truth Insufficient evidence

The assessment is recorded; an English explanation is pending.

02

Chinese and English ecosystems

Market comparison

The Chinese–English market comparison is not complete yet. A conclusion follows only after its coverage and verifiable evidence are recorded.

03

60-second business read

The call and next move come first; the full read retains the evidence and counterevidence.

What it is in one line

A skills pack for Claude Code and Codex built for empirical applied-microeconomics research, covering the full pipeline from data acquisition and LLM-assisted classification to econometrics, publication tables, and delivery — where the selling point is not "publish faster" but "make sure the numbers you publish actually reproduce from the data."

Who built it

Solo author kennethkhoocy. MIT license, repo created 2026-07-22, 47 stars / 0 forks. Primarily Python scripts plus Markdown skill definitions. No company, no team, no funding.

Read: this is the "researcher builds tooling for fellow researchers" shape. Zero forks and no issues mean no community yet; but the quality of the skills — a whole method built around reproducibility verification — reads like solutions written by someone who got bitten by the problem in their own research.

What it actually does

Eighteen self-contained skills in five groups:

  • Reproducibility and verification → flagship adversarial-empirical-review: adversarially audits whether every number in a paper's tables can be reproduced from the underlying data, via deterministic checks, cross-model reviewers, and a blinded review panel; also supports full-reproduction-from-raw-data mode
  • LLM-assisted classification → annotator-input-parity-check (confirm the model sees the same evidence a human annotator did), llm-gold-bound-failure-check (when a gate fails, first diagnose whether the gold-standard labels are the problem, instead of burning money on prompt tweaks), llm-campaign-drift-gate (cheap canary tests to stop model-alias drift corrupting multi-day labeling), adjudication-sheets (human adjudication tables with byte-level evidence parity), asyncopenai-concurrency-httpx-pool (fixes the ~100-concurrency ceiling in batch scorers)
  • Econometrics and data infrastructure → event-study-cars (cumulative abnormal returns engine, validated against Stata's eventstudy2 to floating-point precision), wrds (WRDS query patterns: Compustat/CRSP/FactSet etc.), stata (drives Stata through pystata with in-memory pandas exchange), two pyfixest debugging skills
  • Tables, figures, citations → latex-empirical-tables (estout-style regression tables), stata-style-figures (matplotlib styled to Stata aesthetics), cite-placement (verified references only, explicitly "never fabricates citations")
  • Sources, documents, delivery → lit-review-orchestrator (document-driven literature search), latex-to-word, markdown-to-pdf, download-gated-pdfs (pulls real PDF binaries from bot-blocked sites via the Wayback Machine)

What old behavior it replaces

In empirical economics, "can this number be reproduced" used to be answered by human eyeballs and patience: a reviewer or RA opens the paper, lines up data and code, and manually verifies every table cell. LLM-assisted labeling was checked by human spot-checks. "Can we trust last week's model-run classification" was answered by rerunning and hoping.

This pack replaces the repetitive half of that human review chain — turning adversarial reviews, labeling quality gates, and table-transformation checks into repeatable automated steps. It also replaces a more subtle default: in AI-assisted research, the default was to trust model output; this inserts an explicit verification gate at every step.

Read: the most valuable thing here is the author's obsession with verification. Most people using LLMs for research are accelerating generation; this author is designing for proving that every step after generation did not silently break. That is a perspective only someone who has been educated by top-journal reviewers writes.

Business model

Not disclosed. MIT open source, no paid service, no SaaS, no sponsor page.

Read: the currency of academic tooling has always been reputation and methodological influence, not direct revenue. If these skills enter real lab workflows, the author's scholarly reputation is the more tangible return.

Hard numbers

  • 47 stars / 0 forks / 0 open issues. Repo created 2026-07-22, MIT
  • 18 skills across reproduction, LLM classification, econometrics infrastructure, tables, and literature delivery
  • Verifiable engineering details in the core skills (validation against Stata to floating-point precision, a real httpx connection-pool concurrency fix)
  • Users and lab adoption: not disclosed

Four-way read

Dimension Call
Founder-product fit High. The author has clearly been bitten by "the number won't reproduce" repeatedly
Product insight Nails the real pain in empirical research — verification — with unusually careful method design (canary tests, byte-level evidence parity)
Execution quality Solid skill definitions with reproducible engineering detail, not prompt stacking
Timing Early. AI-assisted research is just starting; most researchers are still at "make AI do the work," not "prove the AI's work is right"

The call

This is a guardrail system for AI-assisted research, with a real insight but no evidence of traction yet.

It hits a genuine problem: the biggest controversy in LLM-assisted empirical research is not speed but trustworthiness. The author answers with an explicit verification apparatus — not "I guarantee the results are right," but "every step can be checked." A skill like llm-gold-bound-failure-check — diagnose whether the gold standard is the problem before tweaking prompts — is the kind of detail that only comes from having lost real money to bad classification. That cannot be faked.

But the evidence is thin. 47 stars, zero forks, solo author, two weeks old, and zero adoption signals. A skills pack only proves itself inside real research workflows — until then it is one person's well-organized notes.

The transferable rule: design a verification layer for AI output rather than optimizing prompts. When the cost of error is high — retracted papers, contested labels — what users actually buy is "provably not broken," not "usually right." Any high-error-cost domain (research, finance, healthcare) should be built this way.

What to watch next

① Whether forks and real adoption cases appear in three months — someone actually using it in their paper workflow ② Whether skills are added or updated (a moving project means the author is still using it) ③ Whether it appears in a known lab's or course's recommended list — academic tools spread by word-of-mouth endorsement, not rankings

What you can take from it

Product logic: when building AI-assisted tooling, make verification an explicit product feature, not an implicit promise. Three moves to copy: add a reproducible checkpoint to every output step (canary tests); when a gate fails, diagnose whether it is input quality or model quality before deciding what to change; and demand byte-level evidence parity, leaving no room for "approximately consistent."

Pricing structure: none. An open-source academic tool has no transferable pricing model.

Verdict

Unproven. The pain is real and the method is meticulous, but 47 stars, zero forks, and zero adoption evidence put this at "personal project" stage. No doubt the author is a genuinely informed researcher; the open question is whether this moves from personal notes to a used tool. Revisit in three months against the checks above.

05

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