1,893 件 ・ 関連度順
概要と使いどころ
Direct cinematic shots and coverage for AI-assisted content production. Use when an agent must translate story intent into framing, shot size, lens and perspective, camera position, movement, blocking, screen direction, lighting-aware coverage, continuity, shot specifications, or diagnose why generated or filmed shots do not cut together.
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。
Provider-independent production design direction for AI-generated media. Use when translating narrative, brand, advertising, product, social, film, or worldbuilding intent into sets, locations, props, color and material palettes, era research, graphic and environmental design, continuity bibles, image/video prompt planning, reference strategy, production constraints, iteration notes, handoff specs, and QA.
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。
Provider-independent lighting direction for AI-generated images, video clips, ads, film scenes, avatars, product shots, interviews, beauty work, and social content. Use when an agent must specify, translate, maintain, troubleshoot, or QA motivated lighting, key/fill/back/rim/practical lights, hard or soft light, color temperature, exposure, contrast, time-of-day, continuity, reference strategy, or safety/accessibility risks in generated media.
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。
Provider-independent dialogue editing and ADR direction for AI agents producing generated videos, films, ads, avatar clips, explainers, localization/dubbing, podcasts, recuts, and social content. Use for dialogue prep, repair, room tone, take comping, sync, ADR cueing, dubbing direction, pronunciation, de-essing, plosives, mouth clicks, voice continuity, synthetic voice consent boundaries, captions/transcripts handoff, mix handoff, loudness, intelligibility, accessibility, and QA.
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。
Provider-independent ecommerce product imagery production for marketplace listings, product pages, hero shots, lifestyle scenes, comparison charts, infographics, variant/packaging images, virtual try-on or placement mockups, ads, and AI-assisted product visuals. Use when an agent must plan, generate, edit, review, localize, package, or QA truthful commercial product images while managing SKU continuity, marketplace requirements, claims, rights, releases, accessibility, and approval.
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。
Provider-independent storyboard and previsualization direction for AI agents turning briefs, scripts, treatments, ads, films, social videos, and generated-media concepts into shot-by-shot boards, animatics, previs plans, camera/blocking/movement direction, reference packs, AI image/video prompt plans, review workflows, continuity checks, and production handoffs.
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。
Create a 60–90 second, collage-style HyperFrames explainer from a hiding-in-plain-sight topic or supplied documents and links. Use for requests such as 'make a plain-sight explainer', 'Vox-style history of X', 'why is X everywhere?', or 'turn this document into an explainer'. Covers topic or source routing, research, script, static design approval, voiceover timing, motion continuity, build, and numeric quality gates.
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。
Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
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 combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR 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。
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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。
Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
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。
Create and read bigWig browser tracks for visualizing continuous genomic data. Convert bedGraph to bigWig, extract signal values, and generate coverage tracks using UCSC tools and pyBigWig. Use when preparing coverage tracks for genome browsers or extracting signal at specific regions.
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。
codeck entry point. Scans local files for materials, shows pipeline dashboard with diagnostic intelligence, guides user to the next step. Use when the user says "codeck", "new deck", "make a presentation", "make a deck", "new slides", "build a presentation", "export", "speech", "script", or wants to start or continue a presentation project. /codeck is the user-facing entry; legacy sub-skills are internal modules.
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 this skill for guided MBSE work in MATLAB — starting a new project, resuming work mid-workflow on an existing project, or answering orientation questions about how the MBSE skills fit together. Trigger when the user says they want to create, start, or set up a new MBSE project; work on a model-based systems engineering / RFLPV project; or asks which skill covers which phase. Walks through phases one at a time — propose → approve → generate → run → confirm. Use proactively whenever someone mentions starting or continuing an MBSE project.
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 starting, continuing, updating, exporting, or sharing a running transcript of a MATLAB AI tutoring session, especially when the transcript will be shared with an instructor, attached to a learner session report, or passed to an evaluation workflow for quality review.
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。
Make kinetic-typography motion reels (MP4) in code: showreels, portfolio or work reels, product promos, intro films, "motion design"-style videos with bold condensed type, HUD micro-type, black/cream/lime palette, three.js generative layers (particle terrain, liquid marble, chrome knot, particle clouds), shape-continuity transitions and a synthesized, beat-locked score. Canvas 2D + WebGL rendered frame by frame in headless Chrome, encoded with ffmpeg. Use when the user asks for a reel, showreel, motion-graphics / kinetic-type video, a "Work Reel" in this style, or to extend/re-cut the Work Reel ’26.
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 CI/CD pipelines that run test suites. Covers GitHub Actions and GitLab CI templates, parallelism and sharding, artifact management, flaky-test quarantine, test-result publishing, coverage quality gates, OIDC keyless deploy, and copy-paste workflows for Playwright, Jest, and multi-stage pipelines. Use when: "CI/CD," "GitHub Actions," "pipeline," "test in CI," "GitLab CI," "continuous integration," "test automation pipeline," "shard tests in CI." Not for: per-test flaky healing at runtime — use test-reliability; go/no-go release decisions and smoke-test checklists — use release-readiness; test-result dashboards and trend reporting — use qa-metrics. Related: playwright-automation, qa-metrics, test-reliability, coverage-analysis, release-readiness.
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。
Produce a risk matrix or heatmap that quantifies what could break by business impact × probability, runs failure mode analysis on the top items, and maps test coverage to risk zones. Includes stakeholder interview frameworks and continuous reassessment. Run this BEFORE test-strategy or test-planning. Use when: "risk assessment," "risk matrix," "risk heatmap," "what could break," "critical paths," "failure modes," "where to focus testing." Not for: multi-quarter QA direction — use test-strategy. Not for: a single sprint/release test plan — use test-planning. Not for: hands-on session-based bug hunting — use exploratory-testing. Related: test-strategy, test-planning, release-readiness, qa-metrics.
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。
Safe-release techniques DURING rollout: feature flags, progressive rollouts, canary analysis, guardrail metrics, production smoke tests, and synthetic users. Bridges QA and SRE practices. Use when: "feature flag testing," "canary deploy," "progressive rollout," "guardrail metrics," "dark launch," "safe rollout." Not for: scheduled probes that run continuously after release — use `synthetic-monitoring`. Not for: designing tests from prod telemetry — use `observability-driven-testing`. Related: release-readiness, synthetic-monitoring, observability-driven-testing, qa-metrics.
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。
Validate release readiness with evidence-based go/no-go decisions. Covers go/no-go checklists, smoke test suite design, staged rollout validation, rollback criteria and procedures, and post-deployment verification. Ensures release confidence comes from data, not feelings. Use when: "release ready," "go/no-go," "smoke test," "release checklist," "rollback plan," "staged rollout," "canary deploy." Not for: safe-release techniques (flags, canary, dark launch) applied during the rollout itself — use testing-in-production; scheduled probes that run continuously after release — use synthetic-monitoring; designing new tests from prod telemetry — use observability-driven-testing. Related: testing-in-production, qa-metrics, ci-cd-integration, ai-system-testing.
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。
Scheduled probes that run CONTINUOUSLY after release. Covers probe design for critical user journeys, alerting integration, SLA validation, multi-region monitoring, and the boundary between QA and SRE. Use when: "synthetic monitoring," "uptime testing," "scheduled probes," "SLA validation," "availability monitoring," "post-deploy checks." Not for: safe-release techniques during rollout — use testing-in-production. Not for: designing tests from prod telemetry — use observability-driven-testing. Not for: a one-shot post-deploy smoke gate tied to a release — use release-readiness. Related: testing-in-production, release-readiness, performance-testing, qa-metrics.
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。
Logs AI thoughts and decisions for human observability. Applies continuously throughout all tasks to maintain transparency.
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。
Execute implementation within EPICs following test-first development, continuous SoT updates, and code traceability during PRD v0.7 Build Execution. Triggers on requests to start building, implement an epic, begin coding, or when user asks "start building", "implement epic", "coding", "development", "build execution", "implementation", "write code". Consumes EPIC- (context), TEST- (acceptance criteria). Updates existing IDs and creates code. Outputs working code with @implements traceability tags.
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。
E2B is a cloud platform that gives AI agents secure, isolated sandboxes — ephemeral Linux VMs with a filesystem, package managers, and internet access — to run AI-generated code safely. Use when a user wants an agent to execute Python or JavaScript, install packages, read/write files, or run a long process without risking the host machine, or wants to wire code execution into a LangChain, CrewAI, or custom agent loop.
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。