366건 · 관련도순
Execute dynamic instrumentation utilizing Frida to inject custom JavaScript into running iOS applications (IPAs) on jailbroken devices. Hook native functions, bypass SSL Pinning, bypass Jailbreak Detection, and manipulate in-memory data at runtime.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Land a ready branch - push, PR, pinned CI watch, squash-merge, issue verification, cleanup. Use for every PR landing; encodes the run-ID pinning and cleanup discipline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill whenever the user wants to build scroll animations, scroll effects, parallax, scroll-triggered reveals, pinned sections, horizontal scroll, text animations, or any motion tied to scroll position — in vanilla JS, React, or Next.js. Covers GSAP ScrollTrigger (pinning, scrubbing, snapping, timelines, horizontal scroll, ScrollSmoother, matchMedia) and Framer Motion / Motion v12 (useScroll, useTransform, useSpring, whileInView, variants). Use this skill even if the user just says "animate on scroll", "fade in as I scroll", "make it scroll like Apple", "parallax effect", "sticky section", "scroll progress bar", or "entrance animation". Also triggers for Copilot prompt patterns for GSAP or Framer Motion code generation. Pairs with the premium-frontend-ui skill for creative philosophy and design-level polish.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure. Uses PyGithub and YAML parsing for automated audit. Use when hardening CI/CD pipelines or investigating compromised build systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you need to control Slack from SolanaOS via the slack tool, including reacting to messages or pinning/unpinning items in Slack channels or DMs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reconstructed reference notes on US NRC software assurance guidance for digital computer software in nuclear power plant safety systems, built from seven public-domain sources (Regulatory Guide 1.168 Revision 2, July 2013; Regulatory Guides 1.169, 1.170, 1.171, 1.172, and 1.173 Revision 1, July 2013; NUREG-0800 Branch Technical Position 7-14 Revision 6, August 2016; pinned 2026-10-12). Use for software life cycle process questions, software requirements specifications, software configuration management plans, verification and validation with reviews and audits, test documentation and unit testing, and what NRC staff examine in a BTP 7-14 software review of plans, implementation, and design outputs. SCOPE LIMITS: NRC Regulatory Guides and the BTP 7-14 staff review position only; the endorsed IEEE standards (IEEE Std 603-1991, 7-4.3.2-2003, 828-2005, 829-2008, 830-1998, 1008-1987, 1012-2004, 1028-2008, 1074-2006), IEC 60880, IEC 61513, IEC 62138, and IAEA guides are cited by designation and edition only, with no standard text; RG 1.152 Rev 4, BTP 7-18, BTP 7-19, other SRP Chapter 7 sections, and NUREG/CR contractor reports are outside the pack; reflects the pinned 2013 and 2016 editions, not later agency actions; synthesized reference notes, not legal or licensing advice and not a substitute for the source documents. LICENCE: Public Domain (US Government work, 17 U.S.C. § 105).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reconstructed reference notes on US FDA medical-device software regulation, built from seven public-domain FDA sources (the QMSR final rule 89 FR 7496, FDA device software guidance documents, and the eSTAR program page; pinned 2026-10-12). Use for regulatory pathways and premarket submissions (510(k), De Novo, PMA, eSTAR), premarket software documentation levels (Basic or Enhanced), software validation and Computer Software Assurance, the Quality Management System Regulation (QMSR, 21 CFR 820), medical-device cybersecurity (secure product development, SBOMs, FD&C Act section 524B, labelling), clinical decision support boundaries, off-the-shelf software, MDDS and other software-function scope questions, and the 510(k) software-change trigger. SCOPE LIMITS: US FDA device regulation only; no EU MDR or UKCA content (see `med-device-eu`); no ISO 13485, ISO 14971, or IEC 62304 text, which are named and summarized only as FDA obligations; no drug, biologic, or combination-product rules; reflects the pinned source dates, not later FDA actions; synthesized reference notes, not legal advice and not a substitute for the guidance documents. LICENCE: Public Domain (US Government work, 17 U.S.C. 105).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reconstructed reference notes on US FAA approval guidance for airborne software and airborne electronic hardware, built from four public-domain sources (FAA Order 8110.49A, AC 20-115D, AC 20-152A, AC 00-69; documents dated 2017-07-21 to 2022-10-07, pinned 2026-10-12). Use for how FAA certification staff plan and conduct software reviews (certification liaison, desk versus on-site review, review planning agreements), how the level of FAA involvement (LOI) is picked and scored with the Appendix A worksheets, how software conformity inspection works (software part and installation conformity, ASE and ASI tasks, Form 8120-10), what AC 20-115D recognizes and how DO-178B processes and tool qualification transition to DO-178C/DO-330 (supplements, FLS, UMS, legacy software changes), and what AC 20-152A adds for airborne electronic hardware (simple versus complex custom devices, robustness and HDL code coverage, tool assessment, previously developed hardware, COTS IP, COTS devices, circuit board assemblies), and what AC 00-69 adds as best practices for change impact analysis, data and control coupling, and design-level error handling (best practices, not guidance and not a means of compliance). SCOPE LIMITS: FAA guidance only, and guidance is not regulation (ACs are acceptable means, not the only means); no RTCA DO-178C or DO-254, EUROCAE ED-12C or ED-80, supplement, or SAE ARP text, which are copyrighted and named only (see the avionics-signpost pack); no AC 00-72 (named, not packed); no DO-178C software-level objective tables; no EASA AMC 20-115D or AMC 20-152A content (use easa-rules); reflects the pinned source dates, not later FAA actions; synthesized reference notes, not legal advice and not a substitute for the source documents. LICENCE: Public Domain (US Government work, 17 U.S.C. 105).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Solve partial differential equations — finite differences, spectral methods, and physics-informed neural networks (PINNs via DeepXDE). Supports 1D/2D/3D, steady/transient, linear/nonlinear PDEs with Dirichlet, Neumann, and periodic boundary conditions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assigns pathogen lineages (SARS-CoV-2 Pangolin via UShER mode; Nextclade clade + QC; pango-designation alias_key.json resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage fitness modelling (Wenseleers / Bedford-Figgins multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data version pinning (mandatory for reproducibility), Nextclade dataset versioning (lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme version churn (V3 / V4 / V4.1 / V5.3.2 / Midnight 1200) with documented dropout regions, recombinant X-prefix Pango designation lag, GISAID vs INSDC dual-deposition tensions, and the Karthikeyan 2022 wastewater early-detection signal with explicit reproducibility caveats. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater pooled samples into lineage frequencies with Freyja, tracking lineage frequencies and growth advantages over time, pinning pangolin-data / Nextclade dataset versions for reproducibility, handling ARTIC primer dropouts (V4.1 amplicons 64 / 76 / 88-90), or running variant surveillance for SARS-CoV-2 / influenza / Mpox / RSV / H5N1 / measles.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assigns pathogen lineages (SARS-CoV-2 Pangolin via UShER mode; Nextclade clade + QC; pango-designation alias_key.json resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage fitness modelling (Wenseleers / Bedford-Figgins multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data version pinning (mandatory for reproducibility), Nextclade dataset versioning (lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme version churn (V3 / V4 / V4.1 / V5.3.2 / Midnight 1200) with documented dropout regions, recombinant X-prefix Pango designation lag, GISAID vs INSDC dual-deposition tensions, and the Karthikeyan 2022 wastewater early-detection signal with explicit reproducibility caveats. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater pooled samples into lineage frequencies with Freyja, tracking lineage frequencies and growth advantages over time, pinning pangolin-data / Nextclade dataset versions for reproducibility, handling ARTIC primer dropouts (V4.1 amplicons 64 / 76 / 88-90), or running variant surveillance for SARS-CoV-2 / influenza / Mpox / RSV / H5N1 / measles.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit MCP (Model Context Protocol) server configurations for security issues. Use this skill when: - Reviewing .mcp.json files for security risks - Checking MCP server args for hardcoded secrets or shell injection patterns - Validating that MCP servers use pinned versions (not @latest) - Detecting unpinned dependencies in MCP server configurations - Auditing which MCP servers a project registers and whether they're on an approved list - Checking for environment variable usage vs. hardcoded credentials in MCP configs - Any request like "is my MCP config secure?", "audit my MCP servers", or "check .mcp.json" keywords: [mcp, security, audit, secrets, shell-injection, supply-chain, governance]
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill whenever the user wants to build scroll animations, scroll effects, parallax, scroll-triggered reveals, pinned sections, horizontal scroll, text animations, or any motion tied to scroll position — in vanilla JS, React, or Next.js. Covers GSAP ScrollTrigger (pinning, scrubbing, snapping, timelines, horizontal scroll, ScrollSmoother, matchMedia) and Framer Motion / Motion v12 (useScroll, useTransform, useSpring, whileInView, variants). Use this skill even if the user just says "animate on scroll", "fade in as I scroll", "make it scroll like Apple", "parallax effect", "sticky section", "scroll progress bar", or "entrance animation". Also triggers for Copilot prompt patterns for GSAP or Framer Motion code generation. Pairs with the premium-frontend-ui skill for creative philosophy and design-level polish.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
REA의 Git 이력을 특정 버전으로 고정해 변경 이력과 릴리스 노트를 정리합니다. 기여자 감사 표기를 검증하고 Release Please의 공개 본문과 일치하는지 확인합니다.
MCP 서버를 실행하지 않고 설정을 읽어 승인 후에도 다른 코드를 가져올 수 있는 참조를 확인합니다. npm, Python, Docker의 버전 지정을 분류합니다.
여러 기술로 구성된 코드를 감사하기 위해 외부 `repo-scan`을 고정 커밋에서 설치하도록 안내합니다. 소스를 검토하고 설치하는 절차이며, 이 스킬 자체는 감사를 실행하지 않습니다.
AI 에이전트의 반복 작업을 설계·점검합니다. 기계로 판정할 완료 조건, 변경 금지 경계, 독립 검증과 재시도 상한을 정해 헛돌거나 통제를 벗어나는 일을 방지합니다.