623 件 ・ 関連度順
概要と使いどころ
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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 user says "simmer this", "refine this", "hone this", "iterate on this", or asks to improve a specific artifact over multiple rounds. Runs an iterative refinement loop with investigation-first judges that read the code, understand the problem, and propose evidence-based improvements. Auto-selects single judge or multi-judge board based on complexity. Works on any artifact type: documents, prompts, specs, emails, creative writing, API designs, pipelines, codebases. Supports multi-file workspace targets, runnable evaluators, and open-ended optimization (model selection, pipeline topology, prompt tuning).
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 friction you just hit into a concrete change to the workflow, so the same problem doesn't recur.
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。
Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF calibration (BF >6 ≈ 90% posterior per Hart 2017; ~5% FDR by BAGEL convention). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.
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 recommends educating patients and families about the clinical features of remission in Cushing's syndrome when a patient achieves biochemical remission after treatment. Use when a CS patient has postoperative normal cortisol levels, reports symptom improvement, and requires guidance on long-term expectations and monitoring for recurrence.
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。
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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。
Get advice on app improvements and functionality from a Wasp expert. Takes optional arguments for more specific requests e.g. `/expert-advice how can I improve account management?`.
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。
Optimize web images for performance with Next.js Image, responsive srcset, AVIF/WebP, lazy loading, and blur placeholders. Activate on: image optimization, LCP improvement, responsive images, AVIF, blur placeholder, next/image. NOT for: image generation/editing (use image-gen skills), SVG icon systems (use design-system-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。
Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.
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。
CI/CD caching optimizer for dependency caching, Docker layer caching, and build speed improvements. Activate on: CI cache, build speed, dependency caching, Docker layer cache, turbo remote cache, GitHub Actions cache, pnpm store cache. NOT for: CI/CD pipeline creation (use github-actions-pipeline-builder), deployment strategy (use blue-green-deployment-orchestrator), Docker image building (use docker-multi-stage-optimizer).
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。
Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo 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。
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
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。
Optimizes application performance. Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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 learning, new skill, rule improvement, or bug fix from a consumer project should be contributed back to the shared agent-config package.
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 to drive a scalar metric down or up across bounded iterations — keep on strict improvement, revert otherwise, state on disk. Triggers 'minimize X', 'optimize until it stops improving'.
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。
Harness-first workflow for evaluating and improving terminal UIs through real interaction. Use this whenever the user wants to review a TUI experience, compare before/after behavior, identify UX friction, validate navigation or filtering flows, inspect visual stability, or turn observed interaction problems into concrete follow-up tests and improvement recommendations. Prefer this skill any time a TUI should be exercised through a reproducible PTY/session harness instead of only reading code.
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 reviewing, fixing, or verifying a pull request. Focus on correctness regressions, missing tests, permission mistakes, unsafe behavior, and minimal fixes.
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。
Run one safe self-improvement cycle: hypothesize → experiment → record → (maybe) federate.
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。
Builds and improves task-specific decision artifacts from application requirements and labeled records. Use for data assembly, primitive and base-model selection, fitting, export/reload, inference policy, or bounded data/model/program improvement. Includes rules and no-training outcomes; does not require a rung ladder or training a general Jev.
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。
Monthly delta tracking and progress reporting for GEO clients. Compares two GEO audits (baseline vs. current), calculates score improvements across all categories, tracks action item completion, and generates a "here's your progress" client report. Use when user says "compare", "delta", "monthly report", "progress", "confronta", "progressi", "report mensile", or when running a monthly client check-in.
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。
GStack retrospective method for completed workstreams, identifying what worked, what failed, process changes, and role-skill improvements.
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。
Durable categorized record-keeping for any predicate-enabled walk. Trigger when: - Classifying a step's output for recording (decision, finding, debt, feedback). - Promoting a scratch artifact to the ledger, to repo/docs, or to a category home (.ledger/tech-debt/, .ledger/process-feedback/). - Auditing whether a draft is structurally ready for its destination. - Running a zero-context sensibility read before committing to promotion. - Recording a known limitation as a tech-debt entry (not prose in the log). - Recording a friction, miss, improvement, or process amendment. - Prompt contains: /record, record-keeping, promote, ledger note, zettelkasten, scratch-to-ledger, zero-context read, over-strip, classification trigger, tech-debt, process-feedback, amendment.
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。
Bounded improvement loop: one artifact, fixed eval score, keep an edit only if strictly better. Use for AR_DESIGN/AR_FUNCTION/AR_ACCEPTANCE stages or standalone bounded iteration.
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。