「sec」の検索結果

Use when wrangler deploys silently fail or produce wrong artifacts, secrets upload as empty strings, custom domain DNS is not resolving, route assignment broke after rename, observability/tail logs are needed, D1/KV/R2 bindings are missing, OAuth scope errors block a command, cookies on a redirect are not attaching, or the worker exceeds CPU time. Triggers: "Tenant or user not found", "Authentication error" on wrangler d1 push, secret length is 0 after upload, route returns origin not worker, "compatibility_date too old", 1101 errors, custom_domain assignment lost on script rename. NOT for AWS Lambda / Vercel Edge / Deno Deploy (different runtimes), Cloudflare Zero Trust, or R2-specific tuning.

日本語の概要は準備中です。原文の説明を表示しています。

Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.

日本語の概要は準備中です。原文の説明を表示しています。

A gate before git push / deploy — assembled from your own tools (secret-grep + slop-scan + code-review + build/compile + optional worktree isolation), so AI junk (slop / bugs / secrets / uncompiled code) never reaches a remote or a production server. Turns "I hope it's clean" into a verifiable criterion. Branches: web/OSS (git push/PR) vs deploy-to-server service. Triggers — "check before push", "clean push", "gate before push", "finalize before commit", "run before deploy".

日本語の概要は準備中です。原文の説明を表示しています。

Use when auditing Drupal 11 custom modules/themes for security issues such as unsafe input handling, XSS risks, SQL injection, and access control gaps.

日本語の概要は準備中です。原文の説明を表示しています。

Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.

日本語の概要は準備中です。原文の説明を表示しています。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

日本語の概要は準備中です。原文の説明を表示しています。

Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.

日本語の概要は準備中です。原文の説明を表示しています。

Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

Vulnerability analysis: OWASP Top 10, current threat landscape, supply chain security, attack surface mapping, risk prioritization. Use for security audits. NOT for STRIDE/CSO infra audits (mk:cso).

日本語の概要は準備中です。原文の説明を表示しています。

Secure skill template with prompt injection defenses. Copy this template when creating new skills.

日本語の概要は準備中です。原文の説明を表示しています。

Advanced vulnerability analysis principles. OWASP Top 10 baseline + current threat landscape awareness, Supply Chain Security, attack surface mapping, risk prioritization. Use when performing security audits, threat modeling, or OWASP reviews. NOT for STRIDE/CSO-mode infrastructure audits (see mk:cso).

日本語の概要は準備中です。原文の説明を表示しています。

Structured second-pass reasoning after code review or plan evaluation. Offers named reasoning methods (pre-mortem, inversion, red team, Socratic, etc.) to re-examine findings through a specific lens. Use after /mk:review verdict or any time deeper analysis is needed on an existing output. NOT for initial review (see mk:review); NOT for creating implementation plans (see mk:plan-creator).

日本語の概要は準備中です。原文の説明を表示しています。

Design and implement backend service changes: API contracts, service boundaries, auth integration, reliability and delivery checks. Use for backend/API handlers, services, webhooks, jobs, or backend architecture changes. Routes schema/migrations to mk:database; security review to the existing security workflow.

日本語の概要は準備中です。原文の説明を表示しています。

Routes agent management tasks to the correct manager agent. Use when user requests agent creation, updates, audits, git operations, or verification.

日本語の概要は準備中です。原文の説明を表示しています。

실시간 Google Calendar 조회·일정 등록·수정. 캘린더, 일정, 면접, 예약 변경 요청 시 사용. Second-Brain 검색은 조회 전용이지만 이 도구는 쓰기 가능하며 브라우저가 필요 없다.

日本語の概要は準備中です。原文の説明を表示しています。

ubuntu1 배포/재배포/롤아웃, second-brain 이미지 빌드·compose up·마이그레이션 배포를 할 때 사용. rsync 경로·이미지 태그·실행 이미지 ID 대조·백업까지 체크리스트로 강제한다.

日本語の概要は準備中です。原文の説明を表示しています。

Audit npm dependencies for security and updates

日本語の概要は準備中です。原文の説明を表示しています。

Analyze reviewed Grafana log evidence for event patterns, error-rate changes, and enrolled security markers. Use when a user needs bounded log analytics for an incident window, error-log comparison, anomaly triage, operational debugging, or a cautious check of approved security signals.

日本語の概要は準備中です。原文の説明を表示しています。

Audit the code for vulnerabilities, leaked secrets and risky dependencies before it ships.

日本語の概要は準備中です。原文の説明を表示しています。

Select established CTF and security-research tools for authorized artifact triage, web inspection, reversing, crypto, forensics, networking, and exploit validation. Use when a user provides a CTF-style tool catalog, asks which tools to use, or needs safe usage instructions for common offensive/security tools.

日本語の概要は準備中です。原文の説明を表示しています。

Passive code-review lane for authorized repositories, local checkouts, uploaded source archives, and diffs. Use when the security research agent needs deterministic candidate discovery before expensive AI investigation, scoped PR-style review, append-only evidence records, or independent revalidation of code findings.

日本語の概要は準備中です。原文の説明を表示しています。

Turn a specific vulnerability, patch, CVE, Version Diff, or Changelog Claim into a proactive security exploration plan. Use when the user wants research angles, lateral target discovery, patch/CVE strategy, new surface discovery, or hypothesis generation from vulnerability intelligence.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in SQLite embedded database development for Tauri/desktop applications with focus on SQL injection prevention, migrations, FTS search, and secure data handling

日本語の概要は準備中です。原文の説明を表示しています。

Apply npm/pnpm supply-chain hardening when adding a dependency, editing package.json/.npmrc/pnpm-workspace.yaml, reviewing a lockfile change, or configuring CI install steps. Covers the 17 practices from lirantal/npm-security-best-practices.

日本語の概要は準備中です。原文の説明を表示しています。