「assessment」の検索結果

Run deep research on a single competitor. Produces a dossier with executive summary, position + messaging, status-quo alternatives, key differentiators, pricing position, where-they-win / where-they-lose, threat assessment, and what to watch. Writes to marketing/competitors/MMYY-{competitor-slug}.md. Triggers - "competitor research", "competitive analysis", "research competitor X", "battlecard prep", "analyze [competitor name]

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End-to-end research workflow skill for investment analysts and policy researchers. Three scope modes the user picks at trigger time — light (4-5 page decision memo, ~15 min, 0 charts), medium (12-15 page topic brief, ~1 h, 6-10 charts), heavy (flagship report 30-40 pages / 15k+ words, ~2-3 h, 25-35+ charts, multi-stage workflow, multi-LLM, PDF + Word + WeChat + HTML derivations). Reports default to English; the AI replies in the user's chat language. Battle-tested on real macro/policy/equity reports (e.g. Saudi Vision 2030 deep-dive). Triggers when user types /analyst-research, /flagship-research, or describes needs like "research report", "topic analysis", "investment research", "做研报", "投研报告", "主题分析", "深度分析", "policy assessment", "industry deep-dive". Not for: pure news commentary (use topic-brief), slide decks (use deckster-slide-generator), one-shot Q&A.

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。

Assess agentic layer maturity using the 12-grade classification system (Class 1-3). Use when evaluating codebase readiness, identifying next upgrade steps, or tracking progress toward the Codebase Singularity.

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Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.

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Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

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60-Second Token Snapshot — fast assessment with signal, key factors, and price levels without launching subagents

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Risk Assessment & Position Sizing — analyzes volatility, drawdown, correlation, liquidity, smart contract, regulatory, concentration, and unlock risks with crypto-specific position sizing calculators and a composite Risk Score (0-100, higher = safer)

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Execute comprehensive client-side injection vulnerability assessments on web applications to identify XSS and HTML injection flaws, demonstrate exploitation techniques for session hijacking and credential theft, and validate input sanitization and output encoding mechanisms.

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Manages ArgoCD application synchronization using the ArgoCD REST API and argocd CLI. Handles sync waves, hooks, and health assessments for GitOps-driven Kubernetes deployments.

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Automates ArgoCD application synchronization using the ArgoCD gRPC/REST API and argocd-autopilot CLI. Manages ApplicationSets, sync waves, and health assessments for Kubernetes deployments.

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 Mileva Method (CRISP) — Phase 4: Spec. Full implementation readiness package including solution design, UX direction, tech stack, backlog, MVP prioritization, risk assessment, and AI architecture for Claude Code projects — CLAUDE.md, skill mapping, AI specs, sprint planning, quality gates. Triggers on "spec", "phase 4", "build ready", "implementation plan", "CLAUDE.md", "sprint planning", "backlog", "AI spec", or after Phase I exit checklist is complete.

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Develop evidence-backed structural and architectural security hardening proposals from vulnerability disclosures, supplied findings, incident or assessment documents, source code, or a completed Codex Security scan. Use when a user asks for systemic improvements, alternatives beyond per-finding patches, before-and-after security architecture views, engineering tradeoff analysis, or an implementation-ready plan for a selected hardening option. Also use automatically after a Codex Security scan with reportable findings when the top-level scan workflow requests final-report hardening guidance.

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。

Architecture design skill: ADR records, system design checklists, scalability assessment, architecture patterns. Use for complex system design and architecture decisions.

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。

Comprehensive SEO analysis for any website or business type. Performs full site audits, single-page deep analysis, technical SEO checks (crawlability, indexability, Core Web Vitals with INP), schema markup detection/validation/generation, content quality assessment (E-E-A-T framework per Dec 2025 update extending to all competitive queries), image optimization, sitemap analysis, and Generative Engine Optimization (GEO) for AI Overviews, ChatGPT, and Perplexity citations. Analyzes AI crawler accessibility (GPTBot, ClaudeBot, PerplexityBot), llms.txt compliance, brand mention signals, and passage-level citability. Industry detection for SaaS, e-commerce, local business, publishers, agencies. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "page speed", "structured data".

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Render `.claude/diamonds/active.yml` as the decisions each diamond has recorded, in order, grouped by mode (discovery, delivery). Read-only. Default format Mermaid stateDiagram-v2, drawn by a script. Recommended at the end of `/mycelium:diamond-assess` so every assessment closes with a visual state-of-play. See `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` for shared render fleet conventions.

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Design the smallest viable test to validate or invalidate a critical assumption. Based on Torres's assumption testing framework, organized by Gilad's AFTER model (Assessment → Fact-Finding → Tests → Experiments → Release Results).

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Builds renewable energy yield models with resource assessment, capacity factor analysis, and P50/P90 production estimates. Use when modeling wind/solar yields, analyzing resource data, or evaluating production uncertainty.

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Analyzes event-driven opportunities with catalyst identification, pricing efficiency assessment, and risk/reward evaluation. Use when analyzing event-driven situations, evaluating catalysts, or assessing event timing.

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Builds pricing models incorporating J-curve positioning with blind pool risk, early-vintage assessment, and age-weighted adjustments. Use when pricing early-vintage funds, analyzing J-curve risk, or adjusting for fund maturity.

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 structural interest rate exposure with fixed/floating mismatch, basis risk, and cap/floor adequacy assessment. Use when modeling structural rate risk, analyzing basis risk, or evaluating interest rate hedging needs.

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Builds PIK and PIK toggle models with compound interest analysis, cash vs PIK election scenarios, and leverage impact assessment. Use when modeling PIK instruments, analyzing toggle mechanics, or evaluating accrued interest impact.

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Compares build vs buy alternatives with risk-adjusted returns, time-to-value, and execution probability assessment. Use when evaluating growth strategies, comparing M&A vs organic investment, or analyzing make-vs-buy decisions.

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Structures best execution monitoring with venue analysis, systematic internalization assessment, and regulatory compliance documentation. Use when managing best execution, documenting execution decisions, or conducting venue analysis.

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Structures book-building analysis with demand tiers, allocation methodology, and price sensitivity assessment. Use when managing book-building, analyzing investor demand, or optimizing allocation strategies.

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。