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概要と使いどころ
Analyze debt payoff software — avalanche vs snowball strategy engines, interest calculation accuracy, amortization schedules, payment scheduling automation, credit score impact modeling, hardship accommodation workflows, and progress visualization.
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。
Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.
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。
Analyze project dependencies for health, security, and bloat — audit outdated, deprecated, vulnerable, duplicate, heavy, and unused packages across npm, pip, cargo, go mod, and more. Produce a dependency health score, CVE inventory, license compatibility matrix, bundle size impact assessment, and a prioritized update plan with migration paths.
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。
Prioritize a feature backlog or list of ideas. Use when the user says "help me prioritize", "prioritize this backlog", "RICE score these", "MoSCoW", "opportunity solution tree", "what should we build first", "rank these features", or has a list of items and needs to decide order - even if they don't explicitly say "prioritization".
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 QA quality dashboards, calculate defect metrics, analyze escape rates, and produce quality trend reports. Use this skill whenever a QA lead or manager needs to measure and communicate quality outcomes. Trigger on phrases like "quality metrics", "defect density", "escape rate", "bug trends", "quality dashboard", "sprint quality report", "test coverage metrics", "QA KPIs", "how is quality trending", "defect leakage", "production bugs this quarter", "mean time to detect", "quality scorecard", or any request to quantify, visualize, or report on the quality of a product or testing process. Also trigger when a manager asks "how is QA performing?" or an engineering lead asks "what does our defect data tell us?".
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, score, and mitigate delivery risks for a program or release. Use when the user says "risk review", "what could go wrong", "risk register", "delivery risks for this release", "program risk assessment", "identify blockers before they hit", or wants to pressure-test a plan before committing - even if they don't say "risk review". Based on "The Phoenix Project" by Kim, Behr & Spafford (unplanned work, fragile systems, single points of failure) and "The Art of Project Management" by Scott Berkun (scoping risk, milestone pressure, hidden assumptions).
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。
Quality gate for product specifications before stakeholder review. Use this skill when: - A spec draft is ready and you want to validate it before sending to Engineering, Design, and QA - You want to check a spec against your template's completeness rules - You need to catch vague language, missing sections, or gaps before review - You want a pass/fail scorecard with specific fix suggestions
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 and improve marketing copy that already exists - a page, an email, an ad, a post, a deck slide - using a fixed six-pass edit. Produces a marked-up diagnosis, a rewritten version, and a per-pass score so the user can see what changed and why. Use when the user says "review this copy", "improve this", "make this better", "tighten this", "edit this page", "does this copy work", "why is this flat", "punch this up", "proofread", or pastes existing copy and asks for feedback. Also use when copy is on-brand but underperforming. For writing new copy from nothing, see content-writer or landing-page-writer. For diagnosing a page's conversion problem beyond its words, see page-cro. For voice consistency rules, see brand-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。
Analyze customer health signals, identify churn risk flags, and generate an intervention playbook. Use when user says "churn risk", "customer at risk", "red account", "at-risk customer", "health score dropped", "renewal at risk", "customer going dark", "cancel risk", or "what's wrong with this account" - even if they don't say "churn" explicitly. Applies to CSMs triaging accounts or building proactive retention workflows.
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。
Audit a repo's agent context — CLAUDE.md files, codebase docs, skills, and tool/MCP designs — against Anthropic's Claude 5 context-engineering guidance ("unhobbling": Anthropic cut ~80% of Claude Code's system prompt with no eval loss). Finds overconstraint, conflicting instructions, redundancy, stale facts, and missing "unknown knowns"; produces a scored findings report with concrete rewrites, then applies approved fixes. Use when someone says "audit my CLAUDE.md", "context audit", "unhobble this repo", "review our agent docs/skills/tools", or after upgrading to Claude 5-generation models.
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 a full-site SEO audit and return a scored, prioritized report. Use only for site-wide checks; use seo-page for one URL or seo-technical for a technical-only 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。
Detect keyword cannibalization across blog posts by extracting primary keywords from titles and headings, clustering semantically similar targets, and flagging posts competing for the same search intent. Supports local-only mode (grep-based) and DataForSEO API mode (Page Intersection endpoint at ~$0.01/call). Outputs severity-scored report with merge or differentiate recommendations. Use when user says "cannibalization", "keyword overlap", "competing pages", "duplicate keywords", "cannibalize".
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。
AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates evidence-backed citability, purpose clarity, entity clarity, structured data, and AI crawler accessibility. Suggests evidence-backed explanations and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation".
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。
Verify statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all load-bearing claims (statistics, product or policy claims, ranking and comparative claims, named sources), validates cited URLs before fetching, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED. Use when user says "fact check", "verify statistics", "check sources", "validate claims", "factcheck", "source verification".
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 Caveman Cloud evidence read-only: costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use when the user asks what Caveman found, where LLM spend goes, why cost or quality changed, which workflows need attention, or asks for a trace or analytics review. Prefer Caveman MCP tools; fall back to CLI JSON.
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 designing, configuring, distributing, or troubleshooting Salesforce Surveys and Feedback Management. Triggers: 'create a survey', 'NPS score', 'survey invitation', 'survey response report', 'customer feedback form', 'guest user survey access', 'SurveyInvitation', 'SurveySubject', 'SurveySettings', 'sendSurveyInvitation', 'survey not linked to case', 'deploy a survey between orgs'. NOT for choosing between CSAT, NPS, and CES as your CX metric — use architect/customer-effort-scoring. NOT for third-party survey tools like SurveyMonkey or Qualtrics.
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 gathering, documenting, or validating requirements for a Salesforce marketing automation program — covering MCAE (Account Engagement / Pardot) lifecycle stages, MQL/SQL threshold definitions, scoring model specifications (sources, weights, decay, ceiling), handoff notification design, CRM field updates on status change, and sales SLA. Trigger keywords: MQL criteria, marketing-to-sales handoff, lead lifecycle, scoring requirements, automation program requirements, Marketing Cloud Automation Studio scope. NOT for building the scoring and grading rules in MCAE — use admin/mcae-lead-scoring-and-grading. NOT for a Sales-Cloud composite score built from formula fields — use admin/lead-scoring-requirements.
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 scoring a list of business or solution requirements against the *actual* capabilities of a specific Salesforce org so each row can be classified Standard / Configuration / Low-Code / Custom / Unfit, given an effort tier (S/M/L/XL), a risk tag, and an AppExchange suggestion when applicable. Trigger keywords: fit gap salesforce requirements, score requirements against salesforce capabilities, salesforce capability matrix, classify requirement as standard config custom, fit-gap effort tier, AppExchange alternative for requirement. NOT for requirements elicitation (use admin/requirements-gathering-for-sf). NOT for architecture decisions on the GAP rows (use architect/solution-design-patterns and standards/decision-trees/). NOT for license or edition selection (use architect/license-optimization-strategy and architect/org-edition-and-feature-licensing). NOT for OmniStudio-specific fit-gap (use architect/omnistudio-vs-standard-decision).
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 to design, configure, and troubleshoot the Field Service Lightning (FSL) Optimizer — including selecting optimization type (Global, In-Day, Resource Schedule), understanding the priority-score model, configuring travel mode (Aerial vs Street-Level Routing), and tuning optimizer behavior for specific scheduling objectives. NOT for work rules and service-objective weights on a scheduling policy — use admin/fsl-scheduling-policies. NOT for ESO adoption and territory sizing — use architect/fsl-optimization-architecture.
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, score, and prioritize Salesforce AI use cases before any implementation begins — opportunity identification, Impact-Effort matrix scoring, feasibility evaluation across Technical/Operational/Data/Risk dimensions, and ROI framing. NOT for the license checks and enablement steps once a use case is approved — use agentforce/agentforce-service-ai-setup or agentforce/agentforce-sales-ai-setup. NOT for making the data model AI-ready — use architect/ai-ready-data-architecture.
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。
Timeboxed ideation on a topic where lens-driven proposers generate structurally different ideas, blind critics cull them, and pairwise judges rank the survivors on a tournament ladder instead of 1-10 scores, evolving the leaders until the clock runs out. Use when the user wants to brainstorm, explore ideas, discover features, generate options, or think through possibilities for a specified duration. Triggers on requests like "brainstorm X for 30 minutes", "ideate on X", "spend an hour thinking about X", "what features should we build", "explore options for X". Not for executing a task over time (timeboxed-iterating) or optimizing a measurable metric (autoresearch).
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。
Systematically QA test a web application. Use when asked to "qa", "QA", "test this site", "find bugs", "dogfood", or review quality. Four modes: diff-aware (automatic on feature branches — analyzes git diff, identifies affected pages, tests them), full (systematic exploration), quick (30-second smoke test), regression (compare against baseline). Produces structured report with health score, screenshots, and repro steps.
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-hop web research with explicit confidence gating — plan the inquiry, search (WebSearch/WebFetch), score your own confidence, and keep digging until the answer is well-supported or a hop cap is hit, then emit a cited synthesis. Learns across sessions by storing each research case to memory and reusing prior strategies. Use when a question needs more than one search — comparisons, current-best-practice questions, anything where a single lookup leaves you unsure.
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。
Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.
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。