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概要と使いどころ
Facilitate and document Sprint Retrospectives per Scrum Guide 2020. Choose format by team maturity, structure the session, capture action items, and track improvement across sprints. Use after every sprint. Do not use for individual performance reviews, blame sessions, or annual assessments.
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, inspect, analyze, and edit spreadsheets (`.xlsx`, `.xlsm`, `.csv`, `.tsv`) with local code execution. Use for formulas, formatting, charts, validation, financial models, and spreadsheet deliverables.
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。
Hunts for anomalous authentication such as password spraying, brute force, and impossible-travel logons by aggregating Windows logon success/failure events per account and source over time. Activates for requests to hunt anomalous logons, detect password spraying or brute force, or find suspicious authentication 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。
Unity 6 2D game development guide. Use when building 2D games, working with sprites, sprite atlas, SpriteRenderer, tilemaps, 2D physics (Rigidbody2D, Collider2D), 2D lighting, sorting layers, or sorting groups. Based on Unity 6.3 LTS documentation.
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。
Spring Modulith 2.0 implementation for bounded contexts in Spring Boot 4. Use when structuring application modules, implementing @ApplicationModuleListener for event-driven communication, testing with Scenario API, enforcing module boundaries, externalizing events to Kafka/AMQP, building a modular monolith, defining module dependencies, managing inter-module events, or organizing DDD modules as packages. Covers package-based module boundaries, named interfaces, event publication, and Scenario API testing. For architecture decisions, see domain-driven-design skill.
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Decompose large epics into sprint-ready sub-epics using 9 splitting patterns. Use this skill when: - A spec is approved and you need to break a large feature into estimable work items for engineering - You want to split an epic that is too large for a single sprint - You need sized sub-epics with story point estimates for sprint planning - You are preparing for engineering handoff and need to identify work packages
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Manage Buzzsprout podcast hosting: episodes and players via its JSON API. Trigger phrases: buzzsprout, podcast episodes, publish episode, podcast hosting.
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。
Explicit-only last resort. Draws game art from code, with no image model - pixel sprites from a PixelSpec, flat vector props, UI and icons from portable SVG, rigged characters with planted feet, FX with a JS runtime, seam-proven autotiles, playable top-down layouts with collision, stylized parallax and ambient plate motion - palette-exact, with QA and review sheets. Use only when the user explicitly asks for code-drawn art, or when generate2dmedia route_media.py finds no image or video route (no-route, exit 3). Never the default - image generation (generate2dsprite, generate2dmap, video2dsprite) comes first for characters, props, maps and FX. Not for painterly, detailed or likeness art, HD-2D plates or realistic fire, smoke or water. Always disclose the result as code-drawn.
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。
The art-route layer of the Forge skills. route_media.py is the one command that generates an image or an image-to-video clip - through the paid API of any provider whose key is configured (OpenAI, Google Gemini, xAI, BytePlus ModelArk, fal.ai; the configured key is the owner's consent), else through the user's own signed-in Codex or Grok CLI - and prints one JSON line with the route, artifact, sha256 and cost estimate; with no route it answers no-route (codeart2d is then the last resort). Also the capability check (forge_doctor), API key configuration, the spend ledger, resume and Codex image adoption. Use when a Forge skill sends a generation here, for the capability check, or when the user asks about keys, routes or spend. Not for planning, processing or QA of sprites, video frames or maps (generate2dsprite, video2dsprite, generate2dmap) or for code-drawn art (codeart2d).
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 when prioritizing a Salesforce backlog with the MoSCoW method (Must / Should / Could / Won't) plus an effort + value lens, deciding what fits in the next sprint or release, and using WSJF as a tie-break when MoSCoW alone produces ties. Trigger keywords: MoSCoW prioritization, must should could won't, WSJF salesforce, prioritize salesforce stories, sprint commit, backlog grooming. NOT for story writing or splitting (use admin/user-story-writing-for-salesforce). NOT for release train planning (use agents/release-train-planner). NOT for sizing/estimation method debates — this skill assumes a sizing convention is already in place.
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 CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
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 Nat Commun 12:1302), 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。
Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
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。
Agile product ownership for backlog management and sprint execution. Covers user story writing, acceptance criteria, sprint planning, and velocity tracking. Use when writing user stories, creating acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing the backlog.
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 building or editing a spreadsheet or model — formulas over hardcoded values, read-back after writes, official-source data, pivot-first charts. Triggers on 'spreadsheet', 'build a model'.
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。
Before improving anything, show the spread of what can concretely be done on this specific input — a short menu of 3-5 interventions from lightest to heaviest touch, then wait for the user to pick. Domain-agnostic; works on any material the user hands over. Trigger when the user says "spread", "show me my options", "what can you do with this", or asks to improve or fix something without specifying how. Do NOT trigger when the user has already named a specific fix — then just do the fix.
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。
Agile product ownership for backlog management and sprint execution. Covers user story writing, acceptance criteria, sprint planning, and velocity tracking. Use for writing user stories, creating acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing ...
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。
Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.
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。
Autonomous GAS development skill — code, deploy, test, and fix in a self-driving loop. Uses clasp for code management, Web App + gas-run.sh for auto-deploy/execution, and gws for spreadsheet read/write. Triggers on "GAS", "Apps Script", "spreadsheet automation", "clasp", "gws sheets".
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。
Manage the NV oOS Pro Toolkit Project Management system — projects, tasks, events, sprints, task plans, task templates, and PM workflow rules. Covers complete project lifecycle (planning → active → completed), task tracking with priorities and effort estimation, sprint planning, event scheduling with attendees, and template-driven task plan generation. Use when managing projects, creating or tracking tasks, planning sprints, scheduling events, generating task plans from templates, assigning work to team members, or troubleshooting project management data.
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 any time a spreadsheet file is the primary input or output. Covers reading, editing, cleaning, modeling, formula repair, workbook generation, and converting tabular data into validated spreadsheet deliverables.
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。
Write and debug spreadsheet formulas (Excel/Google Sheets), pivot tables, and array formulas; translate between dialects; use when users need working formulas with examples and edge-case checks.
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。
Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.
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。
Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering 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。