421건 · 관련도순
Use when drafting the response letter and revision plan after an American Economic Journal: Microeconomics (AEJ: Micro) R&R. Structures the reply to theory referees (correctness, generality, exposition) and editor; it does not re-derive the result (see aejmic-theory-model).
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Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.
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Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and correctness (reference-free Merqury QV, Inspector/CRAQ structural errors, asmgene false-duplication/collapse). Covers why N50 is the most-gamed metric, why QV measured on the polishing reads is circular, distinguishing uncollapsed haplotigs from real WGD, and the EBP/VGP 6.C.Q40 standard. Use when judging whether an assembly is good enough to annotate or publish, comparing assemblers, diagnosing a fragmented or duplicated assembly, or assessing a phased diploid assembly.
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Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.
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Reviews MATLAB fixed-point (fi) code for performance, code generation efficiency, and correctness. Identifies antipatterns and suggests idiomatic improvements. Use when reviewing fi, fimath, numerictype, or quantizenumeric code.
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This skill should be used when the user asks to "simplify", "clean up the diff", "run simplify", "simplify the changes", "review changed code for cleanup", explicitly invokes "/simplify", or asks to "commit without simplify", "skip simplify for this commit", or "commit this but skip simplify". Reviews changed code across reuse, simplification, efficiency, and altitude, or grants an explicit one-commit bypass. Not for correctness bugs.
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Audits existing table permissions on a Power Pages site by analyzing them against site code and Dataverse metadata, then GRADES the configuration with deterministic 1–5 scores across 3 security/usability/correctness categories (9 dimensions). Detection is anchored by a recurring-issue catalog so findings and severities stay consistent run-to-run. Generates an HTML report with an at-a-glance verdict and per-category scores in the Overview plus a single list of **major/minor** issues (each major issue labelled permissions-only or upstream-caused with a short justification), and suggested fixes. Use when the user wants to review, verify, check, score, or grade table permissions for security issues.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Perform a structured maintainer-style PR review for the mariadb-operator repository. Gathers PR context, triages the change, and evaluates correctness, safety, pitfalls, backwards compatibility and code quality against the project conventions documented in AGENTS.md. Use whenever the user mentions a mariadb-operator PR number or URL and wants any kind of judgment on it — "review this PR", "what do you think of #1234", "is this safe to merge", "assess this diff", "take a look at this change" — even if they don't literally say "review".
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Audit skill configurations for correctness and freshness
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Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip). Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics, psychology, environmental_science, mathematics, social_sciences. Use when the user asks to review, evaluate, critique, or assess a research paper, check references, or verify a BibTeX file.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run fable-mode execution discipline on Claude Fable 5.1 — the top of the escalation ladder and the strongest staged run available. Routes the task to the @fable-orchestrator agent with its model overridden to Fable (`model: "fable"`), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on fable", "stage this on fable", "deep work mode, fable", "run this on 5.1"), or when a fable-opus run reports the task exceeds Opus's ceiling. Benchmarked 2026-09-02: zero correctness delta on Fable 5.1 at ~1.1× tokens, ~2× wall — a provenance runner, not an accuracy runner. Do NOT use for ordinary single-pass tasks — prefer fable-opus, fable-sonnet, or fable-haiku when the task doesn't need peak reasoning. Fable is plan-included; bulk work still routes down-tier.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement, review, or improve SwiftUI features using the iOS 26+ Liquid Glass API. Use when asked to adopt Liquid Glass in new SwiftUI UI, refactor an existing feature to Liquid Glass, or review Liquid Glass usage for correctness, performance, and design alignment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output quality.
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Verify UI *workflow* correctness that a task list, code review, and static screenshots miss. Drives end-to-end user flows in the Simulator via XCUITest with per-step screenshots, statically audits the navigation graph for dead-ends and missing edit paths, and emits a human discoverability checklist. Use after building any phase/slice that adds or changes UI, or when a user reports "I could only figure out the flow by running it."
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Static config-correctness linter for this instance - catches the silent-failure class (unquoted schedules, duplicate keys, unconfigured skills, mode typos, broken requires/MCP refs) that no run-based health skill can see. Notifies only on problems.
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Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching trial loop), runtime CPU dispatch, OpenMP threading, and brgemm integration for GEMM-heavy kernels.
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Review SQL and ORM queries for correctness, safety, and performance before they ship.
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TLA+ formal verification for design correctness and PBT pipeline integration
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Review criteria for the nw-diverger-reviewer — validates JTBD rigor, research quality, option diversity, taste application correctness, and recommendation coherence in DIVERGE wave artifacts
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Compares tools or implementations through controlled benchmarks and independent correctness checks.
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Reviews Swift code for concurrency correctness, modern API usage, and common async/await pitfalls. Use when reading, writing, or reviewing Swift concurrency code.
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Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user asks to review or clean up R code for tidyverse style, standardize formatting or names, remove redundant comments or wrappers, or make a behavior-preserving style pass. Also use when writing substantial new R package code if the user requests tidyverse style or the package already follows it. Not for routine small edits or general correctness, security, or test-quality reviews.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Screenshot comparison QA for frontend development. Takes a screenshot of the current implementation, scores it across multiple visual dimensions, and returns a structured PASS/REVISE/FAIL verdict with concrete fixes. Use when implementing UI from a design reference or verifying visual correctness.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.