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概要と使いどころ
This skill should be used when comparing two videos to analyze compression results or quality differences. Generates interactive HTML reports with quality metrics (PSNR, SSIM) and frame-by-frame visual comparisons. Triggers when users mention "compare videos", "video quality", "compression analysis", "before/after compression", or request quality assessment of compressed videos.
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 portfolio analysis using Alpaca MCP Server integration to fetch holdings and positions, then analyze asset allocation, risk metrics, individual stock positions, diversification, and generate rebalancing recommendations. Use when user requests portfolio review, position analysis, risk assessment, performance evaluation, or rebalancing suggestions for their brokerage account.
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 should be used when analyzing sector and industry performance charts to assess market positioning and rotation patterns. Use this skill when the user provides performance chart images (1-week or 1-month timeframes) for sectors or industries and requests market cycle assessment, sector rotation analysis, or strategic positioning recommendations based on performance data. All analysis and output are conducted in English.
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 market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.
Applies the reasoning of Stuart Russell, AI safety expert, UC Berkeley professor, and co-author of 'Artificial Intelligence: A Modern Approach'. Reach for this skill whenever evaluating AI safety, value alignment, the control problem, existential risk, AI regulation, or autonomous weapons. Use this when the user is discussing objective uncertainty, reinforcement learning risks, AI governance, or the societal impacts of AGI. Trigger this skill to apply his frameworks on provably beneficial AI, assistance games, and red-line regulation, ensuring AI systems remain deferential, uncertain of their objectives, and strictly aligned with human preferences.
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。
Write idiomatic DSPy 3.2.x programs — typed Signatures, dspy.Module subclasses, Predict/ChainOfThought/ReAct/ProgramOfThought, and save/load. Use this when starting any new DSPy project or when fixing non-idiomatic DSPy code (hard-coded prompts, ad-hoc string templates, untyped outputs, non-serializable classes).
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 DSPy evaluation harnesses with rich-feedback metrics that are essential for GEPA optimization. Use when writing a metric function, calling dspy.Evaluate, splitting dev/val sets, debugging "why is my optimizer not improving?", or designing CI-ready DSPy eval suites.
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 only memory skill that watches on its own. No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.00/month (using free-tier models). While other memory skills ask you to remember to remember, this one just pays attention.
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。
60-Second Ad Readiness Snapshot — quick assessment without subagents
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。
Fix misleading Flutter/Dart test failures where expected data is empty ([]) or default values instead of the mocked response, caused by stale Mockito generated mocks or missing stubs being silently swallowed by try/catch in provider/repository code. Use when: (1) Test expects non-empty data but gets [], (2) Mock stubs for a method are set up but the code under test returns fallback/empty results, (3) A PR added new methods to a service class and tests that previously passed now fail with misleading "expected X, actual: empty" assertions, (4) "Generated files are out of date" CI error alongside test failures. The root cause is MissingStubError thrown by unstubbed mock methods, caught by production try/catch blocks, causing silent fallback to empty state.
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。
Fix PostgreSQL deadlock errors caused by concurrent schema initialization in worker processes. Use when: (1) psycopg2.errors.DeadlockDetected during CREATE INDEX/TABLE IF NOT EXISTS, (2) Multiple Cloud Run jobs, Kubernetes pods, or worker processes start simultaneously, (3) Error shows "Process X waits for RowExclusiveLock... blocked by process Y", (4) init_schema() or migration code runs at worker startup. The key insight: "IF NOT EXISTS" is NOT truly concurrent-safe - PostgreSQL still acquires locks that can deadlock.
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。
Fix incorrect priority ordering when using denormalized aggregate columns. Use when: (1) Records are processed in wrong order despite ORDER BY on count/sum columns, (2) Top items by some metric aren't being selected first, (3) Aggregate columns show 0 or NULL for records that should have high values, (4) Priority queue processes low-value items before high-value ones. The root cause is often that denormalized columns (vine_count, loop_count, total_orders, etc.) weren't backfilled or maintained properly. Solution: JOIN with source tables to compute actual aggregates at query time.
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。
Fix LRU-bounded Flutter/bloc state classes that silently lose insertion-order updates. Use when: (1) a state class uses `Equatable` with a `LinkedHashMap<K, V>` field to preserve insertion order (LRU, MRU, recent-items caches), (2) `Cubit.emit` appears to drop emissions when an existing key is re-inserted or moved to most-recent, (3) LRU eviction evicts the "wrong" entry after a refresh, (4) a test that reports the same key twice and then overflows the cap sees the refreshed entry evicted instead of the oldest. Root cause: Equatable's default map comparison is structural (unordered), so reordering without changing keys/values produces an "equal" state that `Cubit.emit` suppresses.
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。
Plan, build, place, and validate physically meaningful Blender object assemblies, including manufactured part boundaries, mechanical seams, mounting interfaces, pivots, hierarchy, constraints, instancing, modular grids, connector planes, and precision snapping. Use when several objects must form one product, machine, architectural module, vehicle, furniture item, mechanism, or environment structure without incorrectly welding independent parts, eyeballing alignment, or leaving continuous shells as overlapping primitives.
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。
Define production-ready visual intent for Blender scenes and standalone assets, including focal hierarchy, depth layers, visual flow, scale and repetition rhythm, camera mobility, representation fidelity, and performance budgets. Use for original or reference-guided Blender work before construction choices are executed, especially environments, architecture, products, hero assets, or scenes whose modeling quality depends on coordinated spatial design rather than isolated objects.
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。
Statically analyze code to detect security vulnerabilities including buffer overflows, injection risks (SQL, command, XSS), insecure deserialization, improper authentication, hard-coded credentials, and unsafe cryptography. Use when: (1) Performing security code review, (2) Analyzing code for OWASP Top 10 vulnerabilities, (3) Identifying CWE-classified weaknesses, (4) Generating security audit reports, (5) Reviewing code before deployment, or (6) Assessing third-party code security. Findings categorized by CWE ID and severity (Critical/High/Medium/Low).
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 automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property testing).
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 complete modules and components from interface contracts and specifications. Use when Claude needs to build larger software components, implement interfaces, create service layers, or generate complete modules with multiple related classes. Supports Python (with type hints and protocols) and Java (with interfaces and abstract classes). Trigger when users request "implement this interface", "generate a module for", "create a service layer", "build a component that implements", or provide interface definitions that need implementation.
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 complete, production-ready functions and classes from formal specifications, design descriptions, type signatures, or natural language requirements. Use this skill when implementing APIs from specifications, creating data structures from schemas, building classes from UML diagrams, generating code from contracts, or translating design documents into code. Supports multiple programming languages and follows language-specific 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。
Analyze detected vulnerabilities to assess realistic exploitability by examining control flow, input sources, sanitization logic, and execution context. Use when users need to: (1) Determine if a vulnerability is actually exploitable in practice, (2) Assess severity and impact of security issues, (3) Prioritize vulnerability remediation, (4) Understand attack vectors and exploitation conditions, (5) Generate exploitability reports with proof-of-concept scenarios. Focuses on injection vulnerabilities (SQL, command, XSS, path traversal, LDAP) with detailed analysis of reachability, controllability, sanitization, and impact.
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。
Update Java test classes and methods to work with new code versions after refactoring or modifications. Use when code changes break existing tests due to signature changes, refactoring, or behavior modifications. Takes old and new code versions plus old tests as input, and outputs updated tests that compile and pass against the new code. Handles method signature changes, class refactoring, assertion updates, and mock modifications.
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 CVE reachability in software repositories by examining how vulnerable dependencies are imported and used. Determines whether vulnerable components, classes, or functions are reachable from project code through call chain analysis, reflection detection, dynamic loading patterns, and configuration-gated behavior. Classifies each CVE as likely reachable, possibly reachable, or likely unreachable with supporting evidence. Use when analyzing security vulnerabilities in dependencies, performing post-disclosure CVE triage, assessing vulnerability impact, or when users ask to analyze CVE reachability, check if vulnerabilities are exploitable, or evaluate dependency security risks.
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 surviving mutants from mutation testing to identify why tests failed to detect them. Takes repository code, test suite, and mutation testing results as input. Identifies root causes including insufficient coverage, equivalent mutants, weak assertions, and missed edge cases. Automatically generates actionable test improvements and new test cases. Use when analyzing mutation testing results, improving test suite effectiveness, investigating low mutation scores, generating tests to kill surviving mutants, or enhancing test quality based on mutation analysis.
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 concrete counterexamples when formal verification, assertions, or specifications fail. Use this skill when debugging failed proofs, understanding why verification fails, creating minimal reproducing examples, analyzing assertion violations, investigating invariant breaks, or diagnosing specification mismatches. Produces concrete input values, execution traces, and state information that demonstrate the failure.
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。