2.3万 件 ・ 関連度順
概要と使いどころ
ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling.
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。
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
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。
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
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。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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。
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible 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。
Automated cell behavior analysis from microscopy or XR lab recordings. Classifies cell motion phenotypes (migration, proliferation, apoptosis, division, quiescence), computes population-level quantitative metrics (growth rate, migration velocity, directionality index), and emits structured JSON for downstream reporting, plotting, or ELN integration.
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。
Fetch external sources (web/arXiv/GitHub) into scanner-gated wiki CANDIDATES, never canonical pages: guarded, capped, scanned. NOT for local knowledge (mk:wiki) or markdown fetch (mk:web-to-markdown).
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。
Pack an EXTERNAL repository into one AI-friendly file (markdown/xml/json) for third-party analysis, security audits, or handoff. Do NOT use for the current project — it's already read lazily.
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。
Domain-aware complexity routing: scans the task for domain keywords, matches domain-complexity.csv, and returns complexity level, workflow intensity, and execution tier. Extends Phase 0 orchestration.
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。
Reviews a GitHub PR with a shallow correctness/security/breaking/AI-slop checklist and emits a verdict; optionally posts via gh. NOT for own-diff audit (mk:review); NOT for replying (mk:respond-pr).
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。
Systematically QA-tests a web app and fixes bugs: test, then iteratively fix, re-verify, commit each fix. Tiers: Quick, Standard, Exhaustive. Produces before/after health scores and fix evidence.
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。
Coordinate staged or parallel jobs across coding-agent runtimes and subagents: routes by capability/risk, isolates writers in worktrees, arbitrates results. NOT for a single-agent task (mk:cook).
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。
Deep multi-source technical research with citations — tech evaluation, ecosystem/standards comparison. NOT for API docs (mk:docs-finder), codebase discovery (mk:scout), or project Q&A (mk:ask-me).
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。
Experimental behavioral evaluator. Drives a running artifact via browser/curl/CLI and records rubric evidence; the runner is not yet a fully implemented automated evaluation system.
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。
Ticket/PRD intake: product-area classification, completeness scoring, RCA, technical assessment. Works with Jira, Linear, GitHub, or manual paste. Use to analyze or triage a ticket.
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。
JIRA agile via jira-as: epics, sprints, backlog, ranking, story points, velocity. Board ID != project key. NOT issue CRUD (mk:jira-issue) or time tracking (mk:jira-time).
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。
Implementing features or writing code. TDD is opt-in via --tdd / MEOWKIT_TDD=1. Provides code patterns, skill loading, and coding standards.
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。
GitHub project management via gh CLI: issues, Projects v2 boards, labels, milestones, handoff summaries. NOT for code review (mk:review-pr), PR creation, or git ops (mk:resolving-merge-conflicts).
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 a review or plan verdict: pre-mortem, inversion, red team, Socratic lens, etc. NOT for initial review (mk:review) or new plans (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。
Turn a raw idea into ONE honest recommendation: interview one question at a time until confirmed, then give a verdict with trade-offs and a checklist. Not for options or debate.
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 a plan directory (plan.md + phase-*.md) needs a visual review. By DEFAULT this drives the STRUCTURED visual-plan artifact (visual-plan/plan.json) via the mewkit visual-plan CLI — generate if absent, validate, open the local studio (view/edit), and export plan.html from the approved artifact. Pass --static for the legacy prompt-only single-file plan.html template render. Triggers on "render this plan", "visualize the plan", "open the plan studio", "make a shareable plan page", "plan.html". This is the canonical owner of plan-as-visual-review. NOT for plan critique or scope review (see mk:plan-ceo-review / mk:validate-plan), NOT for generic code/diagram/diff visuals (see mk:preview), NOT for live media generation (see mk:multimodal).
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。
Experimental/manual harness canary procedure. It records benchmark manifests and scores but does not provide a fully automated runner.
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。