4,390 件 ・ 関連度順
概要と使いどころ
Fast parallel codebase scouting — searches many directories at once, returning a file map + architecture fingerprint. Use before planning or cross-dir work. NOT for deep single-file reads.
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。
Multi-agent collaboration bringing 2-4 perspectives into one discussion for architecture decisions and trade-off analysis. Use for 'should we X or Y', design review, or debate before deciding.
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。
Post-ship docs sync: cross-reference the diff and update README/ARCHITECTURE/CONTRIBUTING/AGENTS.md + CHANGELOG to match what shipped. Use on 'update/sync docs'; suggest after a PR opens.
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 error triage: detect language, load fix patterns, auto-retry. Chains into mk:verify. NOT for runtime errors (mk:fix); NOT for architecture debugging (mk:investigate).
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。
Answer factual project questions (codebase, architecture) with cited evidence. NOT for brainstorming, trade-offs (mk:party), debugging (mk:investigate), review (mk:review), or 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。
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。
JSON-canonical session memory. Use when capturing session learnings, extracting patterns, or tracking costs — persists to .meowkit/memory JSON stores; matching Markdown files are generated views. Activates during Phase 0 (Orient) to load context and Phase 6 (Reflect) to persist it. NOT for weekly-cadence engineering retrospectives (see mk:retro); NOT for arbitrary file storage.
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。
Post-ship documentation update. Reads all project docs, cross-references the diff, updates README/ARCHITECTURE/CONTRIBUTING/CLAUDE.md to match what shipped, polishes CHANGELOG voice, cleans up TODOS, and optionally bumps VERSION. Use when asked to "update the docs", "sync documentation", or "post-ship docs". Proactively suggest after a PR is created and before it merges.
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 error triage: detect language, load fix patterns, auto-retry. Use for 'build failed', 'fix compilation', 'type error'. Chains into mk:verify. NOT for runtime errors (see mk:fix); NOT for architectural debugging (see mk:investigate).
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。
DraftForge test infrastructure for writing backend unit tests, Playwright E2E tests, and managing test data population. This skill should be used when writing new tests, adding test fixtures, creating populate functions for new features, adding Playwright E2E specs, using login/auth fixtures, creating test users/orgs/leagues/tournaments, or understanding the test data architecture. Covers backend Django tests via Docker, Playwright config (projects, workers, sharding), test auth endpoints that expose backend login to frontend, and feature-isolated test data patterns.
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。
Redis best practices for caching, data structures, and in-memory data architecture
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。
Multi-agent structured debate with anti-groupthink mechanisms — Devil's Advocate, minority opinion protection, 2-round hard cap. Use when divergent perspectives matter more than consensus (architecture decisions, design tradeoffs, contested specifications).
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。
Pre-generation architecture check — detect R006 separation-of-concerns and compilation metaphor violations before code generation begins
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。
AWS patterns from Well-Architected Framework
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。
Document-driven workflow for implementing, reviewing, and verifying Chisel processor and memory-subsystem RTL. Use when Codex must work from maintained Architecture and Design, trace a complete integration surface, reason in synthesized-hardware terms, keep source-adjacent _codex.md summaries current, avoid redundant or overprotective logic, or run focused functional 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。
Develop, challenge, and document Chisel processor microarchitecture designs before implementation. Use when discussing or writing design documents for processor pipelines, queues, issue logic, rename, ROB, LSU, caches, MSHRs, forwarding, wakeup, replay, flush, privilege, exceptions, or other cycle-accurate hardware mechanisms; when converting design conversations into stable Markdown specifications; or when reviewing a proposed Chisel CPU design for correctness, timing, area, verification cost, and cross-document consistency.
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。
Diagnose and optimize timing-critical Chisel RTL for FPGA implementation while preserving cycle semantics. Use for Vivado timing bottlenecks, long ready or admission paths, queue and issue selection, free-list bank mapping, priority encoders, one-hot arbitration, wide muxes, late-arriving forwarding or override data, high-fanout controls, cross-module predicates, register-boundary changes, emitted-Verilog inspection, or routed-DCP A/B analysis. Also use when a source-level simplification needs proof that it changes timing without changing architectural state.
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。
Bridge Codex to web-based AI model products by packaging local task context, scrub-checking it, sending it through an approved browser surface to ChatGPT Pro, Claude, Grok, Gemini, or another web model, waiting for the answer, and returning the model response to the user or Codex. Also guide DevSpace-like MCP Connector Mode when the user wants ChatGPT Pro or another MCP-capable web host to access approved local workspaces without relying on Codex browser automation. Use when the user asks for GPT Pro, ChatGPT Pro, Claude web, Grok, Gemini web, web model bridge, external model consult, ask another model, second opinion, use a web AI model, browserless agent access to GPT Pro, MCP connector, DevSpace-like workflow, or send local repo context to a browser-based model for planning, review, debugging, architecture discussion, or implementation guidance.
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。
Rework a change as if the intended UX and architecture had existed from day one — deleting compatibility cruft, dead branches, and accidental complexity instead of patching around them. Use when refactoring, cleaning up after a feature lands, removing flags, collapsing legacy paths, or when the user says the code "should look like X from scratch".
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 when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture 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。
Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.
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。
Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.
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。
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of 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。
Resolves rsIDs, navigates RsMergeArch/SNPHistory merge chains, and converts between rsID, SPDI, HGVS, and VCF representations using the dbSNP Build 156 JSON architecture. Use when normalizing variant identifiers, joining variant databases by cluster ID, or tracking deprecated rsIDs through historical merges.
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。