「recommendation」の検索結果

Problem redefinition before problem solving. Takes a stated problem, extracts its premises, then spawns reframers applying different lenses (problem-vs-symptom, scope-shift, stakeholder-shift, level-of-abstraction, time-horizon, inversion, category-shift, constraints-shift) in isolation, and synthesizes the alternatives into a report with an orchestrator recommendation. Produces feedback only — no code, no tickets, no artifacts.

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。

Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.

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。

Reorganize the user's X and LinkedIn network with review-first pruning, add/follow recommendations, and channel-specific warm outreach drafted in the user's real voice. Use when the user wants to clean up following lists, grow toward current priorities, or rebalance a social graph around higher-signal relationships.

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。

Checks structured logging, health check endpoints, metrics collection, request tracing, log levels. Returns findings with severity, location, effort, recommendations.

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。

Checks hardcoded secrets, SQL injection, XSS, insecure dependencies, missing input validation. Returns findings with severity, location, effort, recommendations.

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。

Checks compiler/linter errors, deprecation warnings, type errors, failed tests, build config issues. Returns findings with severity, location, effort, recommendations.

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。

Generates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify cost requirements and constraints, and provide actionable recommendations for build, deploy, and manage the workload cost-efficiently in Google Cloud.

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。

You are an AI Automation Agency strategist. Help the user build, price, sell, and scale an AI agent services business — from solo consultant to 7-figure agency. Every recommendation must be specifi...

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。

Structured multi-criteria decision analysis for ranking options with weights, constraints, confidence, tradeoff reasoning, sensitivity analysis, and explainable recommendations. Use when the user asks for decision support, MCDA, weighted scoring, prioritization, vendor selection, route planning, hiring shortlist ranking, tool comparison, procurement decisions, or auditable agent decision logic.

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 Engine Optimization — get AI assistants to recommend your brand. Run AEO audits, build Answer Intent Maps, track AI recommendation positions, and maintain a 7-layer AEO infrastructure for any brand or product category.

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。

Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations

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。

Save any product to a universal wishlist. Converts any product URL into a one-click "save for later" link. Works with any retailer, any AI platform, zero setup.

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。

Evaluate an incoming workflow upgrade — a skill, MCP server, subagent, library, external service, prompt, or pattern — through one disciplined pipeline before adopting it. Security is a blocking gate. Records every verdict so the same thing is never evaluated twice. Use when considering a new tool from a post, repo, or recommendation. Triggers — "vet this", "should we adopt", "is this safe to install", "evaluate this skill/MCP", "what do you think of this tool".

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。

Runs a team of 50 LLM personas through a 30-round structured debate to predict market direction. Uses the Canvas Agent Swarm Service — the same shared infrastructure other skills use for smaller agent teams. Output is an influence-weighted consensus direction + trade recommendation with a transparent record of every argument, research query, and cross-examination.

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。

Produces internal case viability and conflict check memos for personal injury litigation intake. Screens conflicts, assesses liability and damages, checks statute of limitations, and outputs an accept/decline recommendation. Use when evaluating new PI intake, running conflict checks, assessing case merit pre-filing, or preparing acceptance memos.

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。

Produces U.S. litigation case chronologies with dated timelines, executive summaries, source attribution, gap analysis, and investigation recommendations. Use when asked for a chronology, timeline, case summary, or executive summary of a legal matter, or during case intake, deposition prep, or trial prep.

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。

Produces a Rule 506(d) bad actor disqualification review for private securities offerings, including a covered persons register, tailored questionnaires mapping to each disqualifying event category, a documented reasonable-care diligence record, and classification analysis of flagged events with remediation or disclosure recommendations. Use this skill when conducting Rule 506(d) diligence, identifying covered persons for a Regulation D offering, drafting bad actor questionnaires, or analyzing potential disqualifying events involving criminal convictions, SEC orders, SRO sanctions, or state regulatory actions. Also trigger when the user mentions bad actor screening, covered persons analysis, Rule 506(d) questionnaire, disqualification waiver, or reasonable care defense. Even if the user just says "bad actor check" or "506(d) diligence," use this skill.

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-ticket Fibonacci sizing for a paste-mode batch of user stories. Default mode is advisory-only (writes a Story Sizing Report). Opt-in `--auto-create` delegates ticket creation to mk:jira-issue + mk:jira-collaborate with mandatory dry-run + single batch confirmation gate. NOT for single-ticket estimation (mk:jira-estimator); NOT for sprint capacity (mk:planning-engine).

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 writing, reviewing, or refactoring Vue 3 code — components, composables, reactivity, Pinia state, Pinia Colada data-fetching, file-based routing, and forms. Targets Composition API + <script setup> + TypeScript. Auto-activates on .vue files. For deep best-practices review/recommendations or the full ordered workflow (built-in components, animations, performance pass), use mk:vue-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。

Use for Vue 3 testing best-practices review and recommendations — designing and auditing Vitest + Vue Test Utils tests for components, composables, Pinia stores, Vue Router, async/Suspense, Teleport, forms, and accessibility, plus Playwright E2E test-design strategy. Advisory/review only — recommends patterns and flags test smells; does not run tests. Complements mk:vue-best-practices (feature code). Invoke for "review my Vue tests", "how should I test this component/composable/store", or "Vue test 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。

Turn a raw idea into ONE honest recommendation: interview one question at a time until confirmed, then give a verdict with trade-offs and a checklist. Not for options or debate.

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。

Read-only audit of `.claude/` structural overhead. Reports prioritized "remove X save Y tokens" recommendations against the model context window. NOT for monetary cost tracking — that's /mk:budget. NOT for transcript size monitoring — long-session continuity defers to Claude Code native compaction. NOT for runtime context decisions (what to read, when to compact) — see mk:context-engineering. Use when planning to add context capacity, diagnosing perceived slowdowns, or auditing health.

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。

Recommend a conservative shadow-only review reference architecture from valid offline evidence. Use only when the user explicitly asks for a reference-architecture recommendation or uses the bundled synthetic fixture.

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。

Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations. Activate on 'learn patterns', 'execution patterns', 'what worked', 'optimize based on history', 'pattern analysis'. NOT for failure analysis (use dag-failure-analyzer) or performance profiling (use dag-performance-profiler).

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。