「eth」の検索結果

Set up, format, fix, and clean up LaTeX regression and estimation tables in empirical economics or finance papers. Use when (a) creating a .tex, results.tex, main.tex, or preamble to display regression or estimation output from Python (pyfixest), Stata, or R — including any mention of estout, esttab, estauto, estwide, or table fragments; (b) formatting or repairing estout-style regression tables that overflow the right margin, have cramped or boxed notes, misaligned columns, or poor significance stars, or when asked to match a reference paper, make the note fill the table width, or choose among \estwide, \estauto, and landscape layouts; (c) diagnosing "Misplaced \omit" errors from \input-ed table fragments inside tabular environments. Covers \@@input, \estwide, \estauto, \estautoLT, \specialcell, \sym, minipage notes below tabulars, and the convert-and-verify workflow. Do NOT trigger for general LaTeX documents like beamer presentations, problem sets, letters, or CVs.

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pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing pf.feols/fepois calls changes the printed coefficient table, (2) a regression report suddenly gains rows with absurd estimates (e.g. coef 435.8, SE 7106) for controls absorbed by the fixed effects, (3) diffing outputs before/after a backend change, or (4) anything parses a pyfixest text report by line position.

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Use this skill whenever the user asks to run Stata commands, estimate econometric models, work with .dta files, run a .do file, generate Stata output, or do any statistical analysis where Stata is involved. Also trigger when the user mentions Stata variables, Stata syntax, or econometric tasks where Stata is the natural tool, including regressions, IV estimation, diff-in-diff, RDD, panel data, clustering, summary statistics, and margins. Stata runs through pystata on StataNow 19.5 BE; configure once with stata_setup, then drive everything with stata.run() and exchange data directly with pandas. Prefer this skill over subprocess calls or .do-file shelling for Stata work, including cases where the user does not say pystata.

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Download the actual PDF binary from bot-gated sites (taxpolicycenter.org, urban.org, SSRN-hosted mirrors, think-tank/publisher sites) via the Wayback Machine id_ URL form. Use when: (1) curl/WebFetch of a .pdf URL returns HTML instead of a PDF even with a browser User-Agent, (2) pypdf fails with "invalid pdf header: b'<!DOC'" or "EOF marker not found" on a freshly downloaded file, (3) Firecrawl can parse the PDF to markdown but you need the original file on disk (e.g., filing a reference copy).

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Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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Connect to and query WRDS (Wharton Research Data Services) from any research project. Use this skill whenever the user needs to download, query, or explore data from WRDS — including Compustat, CRSP, FactSet, I/B/E/S, or any other WRDS-hosted database. Also trigger when the user mentions WRDS tables, WRDS libraries, or wants to look up variable definitions or coverage in WRDS datasets. Do NOT trigger for general SQL or database questions unrelated to WRDS.

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Gate resumption of any multi-day LLM batch-scoring campaign that calls an unpinned model alias (deepseek-chat, gpt-*-latest, gemini-*-preview, any provider alias without a pinned version). Use when: (1) resuming a paused or credit-exhausted scoring run days after its last chunk, (2) topping up credits to finish a campaign, (3) extending a cached scoring pipeline with new items. Prevents silently splicing two model versions or serving revisions into one measure. Verified 2026-07-16: for $0.30 caught a serving-revision drift WITHIN DeepSeek v4-flash (same alias, same family, litigation scores systematically shifted across a 2-day gap) before an $83 resume spend.

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Convert between LaTeX and Microsoft Word for academic manuscripts in either direction, and assemble .tex from mixed sources. Fires on: converting .tex/LaTeX to Word/.docx ("tex to docx", "latex to word", "tex2docx", "convert to word", "pandoc convert"); converting .docx manuscripts to LaTeX for editing and back ("convert to latex", "manuscript", "footnotes", "reference doc", the docx-to-tex-to-docx round-trip / academic paper editing pipeline); high-fidelity delivery where plain pandoc loses tables, mangles cross-references, or fails on custom macros — booktabs/regression tables, OMML equations, cleveref, \estauto, \@@input, \thanks, TikZ, native Word tables, longtable, siunitx; and building .tex from mixed PDF/docx/LLM-generated sources. Replaces and reroutes the retired skills manuscript-editing-template-latex, latex-to-docx-fidelity, tex2docx, and latex-from-mixed-sources.

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Convert Markdown (.md) files to a polished PDF with ALL images preserved and scaled to the page. Use whenever the user asks to "save this markdown as a PDF", "convert README.md to pdf", "export the .md as a pdf", "turn these notes/docs into a PDF", or wants a PDF deliverable of any GitHub-flavored Markdown document (README, design doc, report, notes) — especially when it contains images or diagrams (remote or local), tables, code blocks, or a linked table of contents. Pipeline: pandoc (GFM → standalone HTML with embedded resources) → headless Chrome print-to-pdf with a GitHub-like print stylesheet → pypdf verification that every referenced image is embedded. Do NOT use for .tex → PDF (use a LaTeX toolchain) or for .docx work (use word-docx / tex2docx).

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Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.

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Style every matplotlib figure like the Stata 18/19 default (stcolor) scheme — Arial embedded as TrueType, white background, recessive light-gray grid below the data, no top/right spines, unframed legends, and the validated blue/red/gray palette. Use whenever a task generates or restyles charts, plots, or figures for papers, reports, or slides, even if the user doesn't mention Stata — this is the house style for all publication figures. Also use when asked to make figures "look like Stata", match the stcolor scheme, or restyle existing matplotlib output.

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Generates EditMode and PlayMode tests using NUnit, mapped from GDD Gherkin scenarios. Use when writing tests, validating features, ensuring code quality, or verifying behavior after changes. Triggers on requests like "write tests for this", "test the health system", "add unit tests", "verify this works", "add PlayMode tests", "test this feature", "write integration tests", "make sure this doesn't break", "add test coverage", "TDD this feature", or any task involving test creation, test-driven development, or quality assurance. Maps Given/When/Then scenarios directly to test methods using NUnit assertions.

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Systematic 4-phase debugging framework for Unity projects with GameDebug wrapper generation. Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Triggers on requests like "this doesn't work", "there's a bug", "it crashes when", "fix this error", "why is this happening", "null reference exception", "test is failing", "unexpected behavior", "something broke", or any debugging task. Four-phase framework (gather evidence, isolate, hypothesize, fix) that ensures understanding before attempting solutions.

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Brownfield onboarding — audits existing project artifacts for template format compliance (not just existence), classifies gaps by impact, and produces a numbered migration plan. Run this when joining an in-progress project or upgrading from an older template version. Distinct from /project-stage-detect (which checks what exists) — this checks whether what exists will actually work with the template's skills.

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Use when a task needs the judgment of an environmental engineer — designing or sizing a wastewater/water treatment process, drafting or reviewing an NPDES or Title V air permit application, scoping a contaminated-site remediation (RI/FS), evaluating whether a process change triggers a permit modification, or checking a discharge/emissions dataset against regulatory limits.

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Use when a task needs the judgment of an Economist — estimating a demand elasticity or causal treatment effect from observational or experimental data, building a cost-benefit or welfare analysis for a policy or pricing decision, forecasting a macro or market aggregate under stated assumptions, or stress-testing whether a "the data shows X" claim survives a confounding-variable check.

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Use when a task needs the judgment of an English Language and Literature Professor — backward-designing a first-year composition or literature syllabus against a shared outcomes rubric, deciding whether an AI-detection or plagiarism flag on a student essay warrants an integrity case, reading where a tenure case's monograph actually sits in a university press's pipeline, or handling a content complaint about an assigned literary text.

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Use when a task needs the judgment of an electric-motor and power-tool repairer — reading a megohmmeter/polarization-index test to catch a winding fault before failure, deciding rewind vs. replace on a failed induction motor, isolating a bearing fault from a broken-rotor-bar fault, judging brush/commutator wear on a universal motor, or evaluating whether a rewind was actually done to spec.

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Use when a task needs the judgment of an Elementary School Teacher — interpreting a running record or fluency check against grade-level benchmarks, deciding whether a struggling reader needs a Tier 2 intervention, running a self-contained classroom's daily schedule across multiple subjects, implementing (not writing) an IEP/504 accommodation with fidelity, or writing a parent-conference summary grounded in assessment data.

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Use when a task needs the judgment of an Environmental Compliance Inspector — reviewing discharge monitoring reports or emissions data against permit limits, classifying a facility's hazardous waste generator status, calculating a penalty for a documented violation, deciding whether an exceedance warrants a Notice of Violation versus informal correction, or scoping a multimedia (air/water/waste) inspection.

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Use when a task needs the judgment of a licensed elevator mechanic — diagnosing a governor or safety-circuit fault, planning or witnessing a Category 1/Category 5 periodic test, sequencing an elevator or escalator installation/modernization to code, or deciding whether equipment can stay in service pending a repair.

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Use when a task needs the judgment of a tenure-track/tenured Education Professor in a teacher-preparation program — designing a licensure course unit against InTASC/CAEP standards, deciding whether a candidate's borderline edTPA score needs a targeted or full retake, triaging a failing student-teaching placement, or assembling CAEP self-study evidence for reaccreditation.

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Use when a task needs the judgment of an eligibility interviewer for government benefit programs — calculating household income against a means-tested program's limits, determining household composition for benefit purposes, judging whether submitted verification documents are sufficient, or distinguishing a documentation gap from a fraud indicator.

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Use when a task needs the judgment of an Energy Engineer working on building and industrial energy efficiency — running an ASHRAE Level 1/2/3 energy audit, calculating envelope heat loss/gain with the degree-day method, comparing HVAC equipment on SEER/EER/IEER/COP rather than nameplate alone, designing an IPMVP measurement and verification (M&V) plan, or ranking energy conservation measures (ECMs) by savings-to-investment ratio for a capital plan. Distinct from a wind-energy-engineer or solar/PV engineer (own generation-asset design and yield) and from an electrical-engineer (owns utility-scale power delivery and protection) — this role owns the demand side: how much energy a building or process actually needs, and which retrofit reduces that number with numbers that hold up after the fact.

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