2,601 件 ・ 関連度順
概要と使いどころ
Scan all GDDs against the entity registry to detect cross-document inconsistencies: same entity with different stats, same item with different values, same formula with different variables. Grep-first approach — reads registry then targets only conflicting GDD sections rather than full document 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。
Coordinates UDL and differentiation tools through a universal-first hierarchy: barrier removal before targeted differentiation before individualised accommodation. Use when planning accessible learning.
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。
Adapt a classroom task for specific learner needs while preserving the core learning objective intact. Use when differentiating for SEND, EAL, gifted, ADHD, dyslexia, or anxiety.
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。
Entity-aware code change analysis using the pi-sem tools. Use when the user asks what changed, wants blast radius or affected tests, needs focused context for a function/class, wants review help on a commit/branch/PR, or asks to compare semantic diff with raw git diff. Prefer sem_context and sem_impact; use sem_diff selectively for summaries and reviews.
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 user says "minimize this ty ecosystem change", "reproduce this ecosystem result", "investigate a primer difference", "investigate a mypy_primer difference", "investigate a mypy-primer difference", or asks to reproduce, investigate, or minimize behavior changes in ty ecosystem/primer/mypy_primer/mypy-primer projects.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for parser differentials, HTTP normalization gaps, ambiguous headers, path decoding drift, transfer-framing mismatches, and request smuggling routes. Use when the user asks to trace proxy and backend parse differences, conflicting path normalization, Host or forwarded-header ambiguity, CL/TE issues, or routing outcomes that differ across hops. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
跨版本符号迁移与二进制差分。当你有旧版本的符号/逆向结果，需要快速迁移到新版本时使用。 适用场景：内核缺 PDB 用旧版符号推导、程序更新后批量迁移函数名、游戏反作弊更新后快速定位新偏移。 核心方法：用 LLM 做结构化差异比对，程序化输入输出，成本极低（200 函数 ~1 元）。 触发关键词：符号迁移、bindiff、跨版本、PDB 缺失、函数偏移迁移、symbol migration、binary diff、版本对比。
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。
Discover keyword opportunities with MEASURED volume, keyword difficulty, CPC, and intent from OpenSEO, then write them into brand/keyword-plan.md. Use this skill whenever someone asks for keyword difficulty, KD, search volume, keyword ideas with metrics, striking-distance opportunities from Search Console, or SERP-validated keyword priorities. For qualitative research without an OpenSEO connection, use mktg's keyword-research instead (metrics will be unknown). Triggers: "keyword difficulty", "search volume", "keyword opportunities", "striking distance keywords", "measured keyword research".
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。
Generate 5 different narrative scripts for visual slideshows from a single positioning angle. Each script uses a different storytelling framework (AIDA, PAS, BAB, Star-Story-Solution, Stat-Flip) producing genuinely different stories, not layout variations. Make sure to use this skill whenever the user wants slideshow scripts, TikTok content scripts, carousel copy, narrative frameworks for visual content, or says anything about writing scripts for slides or social media storytelling. Even 'write me some TikTok content' or 'I need carousel copy' should trigger this. Outputs structured YAML content specs that chain directly to /paper-marketing for visual design.
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。
REQUIRED before declaring a task done when the diff touches user input, SQL, shell, auth, credentials, file paths, serialization, crypto, network endpoints, data deletion, or dependency surface. Judge by that surface, not the task label — research/experimental/local-only code with none of it can skip this. Walks OWASP/ASVS/MITRE questions the compiler and the test suite won't catch. Pair with `rafter run` on the same diff. When the diff has that surface, marking it complete without both has been under-delivered — do not claim done.
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 the Bass Diffusion Model (1969) to forecast innovation adoption using innovation and imitation coefficients. Use this skill when the user needs to forecast new product adoption curves, estimate market penetration timing, calibrate launch strategy based on diffusion dynamics, or when they ask 'how fast will this spread', 'when does adoption take off', or 'what is the expected S-curve'.".
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。
Scan all GDDs against the entity registry to detect cross-document inconsistencies: same entity with different stats, same item with different values, same formula with different variables. Grep-first approach — reads registry then targets only conflicting GDD sections rather than full document 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。
Adapt and convert AI prompts between different models and platforms. Translates between Midjourney, Flux, Leonardo AI, DALL-E, Sora, Imagen, Stable Diffusion, Adobe Firefly, Ideogram, and more. Handles syntax differences, parameter mapping, and model-specific optimizations. Use when user says "adapt prompt", "convert prompt", "translate prompt", "port to flux", "midjourney to dall-e", or "change 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。
Analyzes git diffs or staged changes and generates narrative commit messages that explain WHY a change was made, not just what changed — following Conventional Commits format. Use when asked to "write a commit message", "generate a commit", "describe my changes", "what should I commit this as", "commit this", "summarize my diff", or "help me commit". Works with git diff output, staged files, or plain descriptions of changes.
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 generating visual artifacts — explanations, diagrams, slides, or diff visualizations. Triggers on "explain X visually", "diagram this", "show as slides", "diff against main". NOT for rendering a plan as HTML (see mk:visual-plan), live media generation (see mk:multimodal), browser QA (see mk:qa), or plan critique (see mk:plan-ceo-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。
Build image generation pipelines with Stable Diffusion, FLUX, ControlNet, LoRA, and ComfyUI workflows. Activate on: image generation pipeline, ComfyUI workflow, ControlNet, LoRA training, diffusion model. NOT for: video generation (ai-video-production-master), image classification (computer-vision-pipeline).
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。
Cross-platform desktop development for macOS and Windows -- platform abstractions, UI convention differences, and multi-platform CI/CD. Activate on: cross-platform app, macOS and Windows, platform differences, DMG installer, MSI installer, NSIS installer, Cmd vs Ctrl, platform abstractions, menu bar differences, native conventions. NOT for: mobile apps, web-only apps, Tauri-specific internals (use rust-tauri-development).
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 differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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 choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
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。
Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.
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 a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.
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。
Assesses patient’s understanding of how CGM data (trends, rates of change) differ from discrete SMBG fingerstick values. Triggered when clinicians hear requests to explain lag and trend arrows on CGM or teach the difference between CGM glucose and fingerstick glucose.
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。
Review+fix protocol with optional pre-core git diff review and safety guardrails (unsoundness, invariants, footguns, incidental complexity). Use when prompts say "/tw-fix this PR", "fix current branch", "fix this diff", "repair CI red", or "apply a minimal patch", and when crash/corruption/invariant-break issues need correction with a validation signal. Stop only after self-review exhausts actionable changes, each full cycle reaches post-self-review rerun plus terminal diff-review closure, and two consecutive zero-edit full cycles are clean.
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。
Cross-vendor code review: this agent reviews the other agent's diff and the other agent reviews this one's, so the final check always comes from a different model. Verdicts are APPROVE or REQUEST_CHANGES with file:line references. Triggers: "/codex-review", "cross-review this diff", "have the other vendor check this".
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。