테스트·보안 스킬

When a claim, doc, test, metric, or assumption conflicts with observed behavior, stop theorizing from the map and verify the live code or data; let territory overrule.

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For authorized security review of code, auth, or APIs you control, model the attacker, map the attack surface, and report only findings with a reproducible exploit path and verified mitigation.

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When a real test is too rare, large, or irreversible, run a controlled counterfactual: isolate one variable, fix conditions, trace the mechanistic chain, and bound what the result implies.

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Qualify a check before it is allowed to clear anything — prove it can detect the failure it is meant to catch. Seeds known defects into a copy of a real artifact plus a clean control, runs the checker, and reports recall and false-positive rate into a qualification ledger. Use when the user says "does this check work", "qualify the gate", "test my reviewer", "seed defects", "vaccinate", "qualify the checks", "can I trust this review", "does it pass for the right reason", or before relying on any automated check or referee simulation for a decision that matters. NOT a code fixer and NOT a reviewer itself — it grades the grader.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prove that the file you are about to send, publish, or hand off IS the thing you mean — before it leaves your hands. Rebuild from source, check integrity, diff the derived artifact against its source, and require the recipient to echo what they received. Use before sending a paper/PDF for review, shipping a release or replication package, handing files to a coauthor or collaborator, uploading anything to an external reviewer or model, or publishing. Prevents an entire review/QA cycle from being spent on defects that exist only in the artifact, not in the work.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Adversarial visual-layout audit of a Quarto `.qmd` or Beamer `.tex` deck. Flags overflow, font inconsistency, box fatigue, spacing, and alignment issues. Use when user says "visual audit", "check the layout", "does this overflow?", "look for visual issues", "audit the slides", or after reworking a deck's appearance. Does NOT check writing or pedagogy — pair with `/proofread` or `/pedagogy-review`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Translate a Beamer `.tex` lecture to a Quarto RevealJS `.qmd` mirror. Multi-phase: TikZ extraction → slide-by-slide translation → citation conversion → automatic QA parity check. Use when user says "translate to quarto", "port to revealjs", "make an html version", "convert this beamer to quarto", "mirror this lecture in qmd", or after a Beamer deck is ready for web publication. Output lands in `Quarto/`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Extract a written voice profile from your own prior papers, then use it to keep new drafts sounding like you. Reads a corpus one document at a time via subagents, produces a reusable profile on disk (lexicon, sentence rhythm, how you open and close, hedging habits, deliberate quirks), and audits a draft against it. Use when the user says "make this sound like me", "match my voice", "profile my writing", "why does this draft not sound like my papers", or before drafting prose that will carry their name. The positive counterpart to /humanize, which only detects AI tells.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Adversarial 5-7 question challenge to a deck's pedagogical choices — ordering, prerequisites, cognitive load, motivation. Use when user says "devil's advocate", "poke holes in this deck", "push back on my slides", "stress-test the design", "what would a skeptical student ask?". Read-only; surfaces questions to force rethinking. Lighter than `/pedagogy-review`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.

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Stress-test a finding against the choices you did not make. Enumerates the discrete forks a competent analyst could have taken (measure definition, sample filter, control set, clustering level, weighting, functional form), runs the specification grid, and reports the distribution rather than a point estimate — then attacks the identifying assumption with named, computable sensitivity statistics. Use when the user says "is this robust", "challenge this result", "specification curve", "multiverse", "how sensitive is this", "what if I'd used a different measure", "stress-test my estimate", or before a result becomes a headline claim. NOT a reviewer of prose or code — it challenges the CLAIM.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Keeps a project's to-do list and memory in GitHub issues — files each finding (from a review, an audit, or the user) as its own issue without duplicates, lists the open issues that bear on the current work, and closes an issue with a comment recording what was wrong, what was tried, what worked, and how it was fixed. Use when review findings or a list of problems should become tracked work (a paper's misleading claims, a codebase's bugs, improvements for later), when starting or resuming work and deciding what to do next, or when a fix has landed and its issue should be closed with a record. Needs the GitHub CLI; warns before posting to a public repository.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Read-only R code review protocol for `.R` scripts. Checks code quality, reproducibility, domain correctness, tidyverse idioms, and professional standards; produces a report without editing. Use when user says "review this R script", "check the R code", "audit the analysis code", "code review on the R", or when an R file is touched as part of a paper submission. NOT for running the code — pair with `/audit-reproducibility` for numeric verification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assemble a submission-ready replication package to the AEA Data and Code Availability Standard (DCAS) / openICPSR / Social Science Reproduction Platform expectations — standard replication README, dataset manifest, computational-requirements capture, a Table/Figure → script:line map, and a confidential-data deposit plan. Use when user says "build the replication package", "prepare the openICPSR deposit", "make the AEA data and code package", "DCAS compliance", "assemble the deposit for the journal", or after a paper is accepted and the journal's data editor needs the package. NOT a numeric verifier — it calls /audit-reproducibility to confirm claims reproduce before packaging.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Adversarial Quarto-vs-Beamer parity QA. A critic agent compares the Quarto HTML render to the Beamer PDF benchmark for content/visual parity; a fixer agent applies fixes; loops until APPROVED or two consecutive rounds turn up nothing new (fallback cap 5 rounds). Use when user says "qa the quarto", "check parity", "does the html match the pdf?", "quarto matches beamer?", or after a translate-to-quarto run. Requires both the `.qmd` rendered and a `.pdf` benchmark.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run the full R package release gate — regenerate docs, run the test suite, run R CMD check --as-cran, and triage every ERROR / WARNING / NOTE against CRAN policy before a release or submission. Use when the user says "check my R package", "R CMD check", "is this package CRAN-ready", "run devtools::check", "prepare for CRAN submission", or points at a directory containing a DESCRIPTION file. Produces a check report + CRAN-submission checklist in `quality_reports/`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive adversarial audit of a theory, proof, math/econ paper, codebase, or set of claims — decompose into components, fan out independent skeptics that must return CONCRETE defects, adjudicate every finding with a separate judge, fix all confirmed defects, then re-verify. Use when correctness must be bulletproof and single-pass or round-by-round review is too slow and too shallow. Invoke for "audit this rigorously", "find ALL the bugs/gaps", "make this rock solid", "converge faster on correctness".

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Compare two implementations of the same thing — a port (R↔Python↔Stata), a reimplementation, a replication package, a refactor, or a new version against the old — so that agreement means something. Freeze inputs first, inventory every expected output, test the comparator itself, compare every channel (not just the headline number), and give each divergence a stable ID and a smallest witness. Use for cross-language parity, replication, upgrade/regression gates, or whenever "the numbers match" is about to license a claim.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Read-only audit of `.tex`, `.qmd`, or `.md` text for AI-voice tells — boilerplate transitions ("Moreover", "Furthermore", "It is important to note that"), AI-cliché lexicon ("delve", "navigate the complexities", "tapestry", "robust framework"), em-dash overuse, symmetric paragraph shapes, tricolon abuse, hedging stacking, "not only X but also Y" frames, and formulaic openers. Produces a report; does NOT rewrite. Use when user says "humanize", "does this sound like AI?", "check for AI tells", "de-AI this draft", "remove AI voice", "audit my prose for sycophancy", or before journal submission / posting a working paper.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn an incoming set of findings — from an AI reviewer, a referee report, a code review, a linter, or a second model — into verified fixes, without letting a confident misread damage correct work. Every finding is a CANDIDATE until checked against the actual source. Use whenever you receive review comments, audit findings, or a critique you did not write yourself, especially when the reviewer is a model or when the volume is too large to check by feel.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AI DevKit · Smoke-test the user flows a change affects (CLI, API, UI) end to end with a disposable sandboxed script against the real app. Use before calling a feature or fix done, when unit tests pass but the flow is untested, or when users ask for a smoke, e2e, or impact check.

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AI DevKit · Use when the user asks to brainstorm, ideate, generate ideas, expand options, challenge ideas, pressure-test ideas, compare concepts, narrow choices, name something, plan content angles, explore strategy, evaluate product ideas, technical approaches, experiments, or decisions.

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AI DevKit · Orchestrator for structured SDLC phase skills. Use when the user wants to run the full lifecycle or choose the next phase across requirements, design, planning, implementation, testing, and review.

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AI DevKit · Enforce evidence-based completion claims — require fresh command output before reporting success. Use when completing any task, fixing a bug, finishing a phase, running tests, building, deploying, or making any "it works" claim.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.