'formal' 검색 결과

Create task-oriented READMEs, API references, architecture documentation, and security reports. Organize information from quick starts to deeper explanations, and turn completed investigations into formal Markdown reports with supporting evidence, reproducible steps, and appropriately masked sensitive information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing a README with a clear quick start
• Organizing API and architecture documentation
• Reporting reverse engineering or penetration test findings

Edit Finnish text to remove formulaic phrasing associated with AI writing, including excessive passive voice, translated sentence structures, and inflated claims. Preserve factual content and formality while improving rhythm and bringing out the writer’s voice.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Making AI-drafted Finnish text sound more natural
• Revising awkward Finnish phrasing influenced by English
• Reducing formulaic phrasing while preserving formality

Ask data questions in everyday language, from checking a single metric to investigating trends or preparing a formal report. Receive findings supported by queries, tables, or charts, with data validation and clear limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check new user signups for a specific month
• Investigate a decline in conversion rates
• Compare support tickets by category and priority

Find alternative words and phrases that fit a sentence, audience, and tone. Suggestions distinguish close synonyms from related expressions, explain differences in meaning or formality, and include brief usage examples when helpful.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Vary repeated words in a piece of writing
• Choose wording for formal or conversational contexts
• Compare subtle differences between similar words

Find unanswered questions in a designated 知识星球 community and draft replies in 沉默王二’s established style. Review past answers, verify project details against source code, and publish comments or formal answers after user approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find community questions awaiting a reply
• Draft advice on technical study or job hunting
• Reply to a specified post or comment

Develop mathematical proofs for machine learning and AI theory by checking assumptions, intermediate results, and edge cases. Produce a complete proof, a corrected claim with its proof, or a report explaining the unresolved gap.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft a proof of a theorem or lemma
• Expand a proof sketch into justified steps
• Check for hidden assumptions and counterexamples

Turn a rough invention idea into a structured disclosure for patent preparation. Organize the technical problem, mechanism, and benefits, distinguish essential and optional features, and plan claims—the scope of protection sought—and supporting drawings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a rough invention idea into a disclosure
• Separating core features from supporting features
• Planning claim dependencies and supporting drawings

Clarify the next step in a mathematical research task by identifying what is blocking progress. Select one specialist skill for discovery, derivation, computation, proof, or formal verification, with required inputs and clear stopping conditions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Decide where to start with an open mathematical question
• Find the next step when papers, formulas, and code are mixed
• Choose between derivation, computation, proof, and formalization

Model distributed systems and concurrent algorithms in TLA+ or PlusCal, then check possible states with TLC. Use the results to investigate deadlocks, broken rules, and differences between specifications and implementation code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Model a distributed protocol before implementation
• Create a specification from existing concurrent code
• Investigate deadlocks and race conditions

Create a brief, polite opening when users say hello, request a self-introduction, or ask for a more formal tone. It greets first, checks the user's goal, and moves directly to any task already stated.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Responding briefly and politely to a greeting
• Introducing the assistant at the start of a conversation
• Making an opening more polite

Manage a professor’s review queue for student work before it reaches clients or courts. View pending documents by urgency and student, then record approvals, edits, or returns with feedback while preserving the original version when edited.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking student drafts awaiting professor review
• Prioritizing reviews for approaching deadlines
• Approving documents or returning them with feedback

Turn code patterns, task procedures, or informal documentation into reusable SKILL.md files for OpenSpace. The workflow guides descriptions, actionable instructions, dependency references, and format checks so agents can select skills for relevant tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Package a code pattern as a reusable skill
• Turn informal documentation into SKILL.md
• Define a repeatable workflow for an agent

Writes and reviews Lean 4 proofs, machine-checked arguments for mathematics or specifications. Follows Mathlib conventions to organize definitions and lemmas, fill proof gaps, refactor lengthy proofs, and establish automated checks for unproved claims and style.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Formalizing mathematics or specifications in Lean 4
• Filling sorry placeholders in proofs
• Splitting long proofs into reusable lemmas

Draft or review formal investment memos for listed stocks. Organize evidence, valuation, downside scenarios, and conditions for revisiting a recommendation into documents suited to investment committees, portfolio managers, clients, or research teams.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing an investment committee memo
• Combining earnings and valuation analysis into a memo
• Reviewing evidence and downside risks in an existing memo

Audits existing tests in any language using formal, research-backed test smell names and the testsmells.org 19-smell academic taxonomy. Use when the caller asks for an academic or citable test-smell review, named smell categories, or a formal severity-ranked smell assessment. Covers Assertion Roulette, Conditional Test Logic, Mystery Guest, Eager Test, Sleepy Test, Unknown Test, Sensitive Equality, and the rest of the catalog across .NET, Python, JavaScript/TypeScript, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, and C++. DO NOT USE FOR a quick pragmatic test review (use test-anti-patterns), writing or running tests, framework migration, coverage, or assertion-diversity metrics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Structured conversational interview to formalise a research idea or extension into a concrete specification with hypotheses and empirical strategy. This skill should be used when asked to "interview me", "help me think through an idea", "formalise this idea", or "start fresh" on a new research direction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Start a new research project by conducting a structured interview to formalize a research idea, then generates research questions with identification strategies and a project spec. Make sure to use this skill whenever the user wants to develop or document a new research idea — not to search for literature or data. Triggers include: "new project", "start research", "I have an idea", "help me develop this", "I want to study X", "help me formalize this idea", "what's my research question", "what identification strategy should I use", "write up my project idea", or when the user describes a topic they want to turn into a paper.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas. Use this skill BEFORE medical-research-gap-finder — it provides the structured landscape that makes formal gap analysis more rigorous. Do not use for formal gap identification, study design, or protocol planning directly.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Interactive interview that formalizes a fuzzy research idea into a structured spec (RQ, hypotheses, identification, data needs, empirical strategy). Use when user says "interview me", "help me think through this idea", "I have a half-baked idea", "formalize this into a project", "walk me through framing a study". Multi-turn Q&A; saves spec to disk. NOT for lit review (`/lit-review`) or ideation from scratch (`/research-ideation`).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Write formal board meeting minutes from an agenda, notes, transcript, or discussion summary. Use when asked to draft board minutes, governance minutes, meeting minutes for a board, or a formal record of decisions and actions. Produces structured board minutes with attendees, agenda items, resolutions, decisions, action register, and approval-ready wording.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Unified skill for all MATLAB and Simulink report generation tasks. Covers report generation, PDF report, document generation, mlreportgen, Report Generator, template, TitlePage, Chapter, multipage HTML, HTMLPage, html-multipage, custom reporter, customizeReporter, template hole, page header, page border, company style package, branded reporters, buildTemplates, Simulink report, model documentation, slreportgen, block diagram report, model report, subsystem documentation, SystemDiagramFinder, SystemIO, ExecutionOrder, LookupTable, template style, page footer, TemplateSrc, report package, table variants, multiple HTML pages, PDF report generation, Word report, DOM API, FormalTable, BaseTable, Figure, FormalImage, Section.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.