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概要と使いどころ
Implement features and fix bugs test-first, the Canon TDD way (test list → one test → make it pass → refactor → repeat). Use when implementing any new feature, endpoint, model, or method, fixing a bug, or executing a plan — write the test list and a failing test BEFORE production 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。
Lint code with swiftlint and swiftformat
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。
Format code with swiftlint and swiftformat
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。
Implement the current plan test-first, the Canon TDD way, driving to a single finishing condition — an empty test list. Derives a Canon TDD test list from the plan, shows it before any code, writes one failing test at a time, and keeps the list visible as it evolves. Use when the user asks to implement, build, or execute the current/approved plan.
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。
Watch the current branch's PR — reply to and resolve review threads, fix failing checks, and optionally merge when ready
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 the knowledge/ base and the .claude/ skills, agents and workflows for staleness, self-contradiction, and drift from their own stated rules — four independent adversarial auditors (two lenses over two trees) verify every load-bearing claim against the current tree, cross-examine each other within their tree, and reach a consensus on if/what needs changing. Use periodically, after a run of deliveries that changed build config, target layout or the skills themselves, or whenever you suspect the docs have aged out of true.
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。
Explore and manage databases with Drizzle Studio. Use when a user asks to browse database contents visually, inspect tables and data, run ad-hoc queries, manage database records through a GUI, debug database issues, or use a lightweight alternative to pgAdmin or DBeaver. Covers setup with Drizzle ORM, standalone usage, data browsing, filtering, and inline editing.
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。
Edits existing marketing copy in ordered passes (structure, claims, sentences, consistency, proof) and returns the edited text with a change log and a list of questions for the author. Flags claims that need evidence instead of inventing proof, and keeps the writer's voice. Use when someone says "edit this copy", "proofread this page", "tighten this", "polish this email", "review my landing page copy", "make this clearer", "check this before we publish", or asks for copy feedback on a page, ad, email or product description.
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。
Guides a founder from real decisions to a short set of written company values that people can act on: collects incidents, tests each candidate value, writes it as a trade-off with behaviors and a story, and wires it into hiring, feedback and promotion. Use when someone asks to "define our company values", "write a values document", "our values are generic, fix them", "turn our values into interview questions", "what should our culture be before we hire", or wants to check whether the team lives the values it has. Produces VALUES.md, an evidence log and an interview kit.
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。
Amazon SES (Simple Email Service) sends and receives email through AWS: transactional and marketing mail, templates, bounce and complaint tracking, and inbound mail processing. Use when a user asks to send email with SES, verify a domain or set up DKIM, move an SES account out of the sandbox, create or bulk-send email templates, add an attachment or unsubscribe header, handle bounces and complaints with SNS, manage the suppression list, or store incoming mail in S3 with receipt rules. Covers the SES v2 API (aws sesv2, boto3 sesv2).
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 expert in AWS Lambda, Amazon's serverless compute service. You help developers build event-driven applications using Lambda functions triggered by API Gateway, S3 events, SQS queues, DynamoDB streams, and scheduled events — with support for Node.js, Python, Go, Rust, Java, and container images, automatic scaling from zero to thousands of concurrent executions, and pay-per-invocation pricing.
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。
Add persistent memory to AI coding agents — file-based, vector, and semantic search memory systems that survive between sessions. Use when a user asks to "remember this", "add memory to my agent", "persist context between sessions", "build a knowledge base for my agent", "set up agent memory", or "make my AI remember things". Covers file-based memory (MEMORY.md), SQLite with embeddings, vector databases (ChromaDB, Pinecone), semantic search, memory consolidation, and automatic context injection.
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。
Memory systems specialist for hierarchical memory, consolidation, and outcome-based learningUse when "memory system, memory hierarchy, memory consolidation, forgetting strategy, salience learning, outcome feedback, temporal memory levels, entity resolution, memory, zep, graphiti, mem0, letta, hierarchical, consolidation, salience, forgetting, ml-memory" mentioned.
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。
World-class infrastructure automation - Terraform, Pulumi, CloudFormation, and the battle scars from managing infrastructure that handles production trafficUse when "terraform, pulumi, cloudformation, infrastructure, iac, state file, remote backend, s3 backend, dynamodb lock, terraform plan, terraform apply, terraform destroy, module, workspace, provider, resource, state drift, import, aws, gcp, azure, infrastructure, terraform, pulumi, cloudformation, iac, devops, aws, gcp, azure, cloud" mentioned.
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 AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when "computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation, Claude computer, OpenAI Operator, browser agent, visual agent, RPA with AI, " mentioned.
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。
Patterns for feedback control systems including PID tuning, state-space control, Model Predictive Control (MPC), trajectory tracking, and stability analysis. Covers both classical and modern control approaches for robotics and automation. Use when ", " mentioned.
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。
Data pipeline specialist for ETL design, data quality, CDC patterns, and batch/stream processingUse when "data pipeline, etl, cdc, data quality, batch processing, stream processing, data transformation, data warehouse, data lake, data validation, data-engineering, etl, cdc, batch, streaming, data-quality, dbt, airflow, dagster, data-pipeline, ml-memory" mentioned.
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。
Database design specialist for schema modeling, query optimization, indexing strategies, and data integrityUse when "database design, schema, indexes, query optimization, migrations, normalization, database scaling, foreign keys, data modeling, database, sql, postgres, mysql, mongodb, schema, indexes, migrations, normalization, optimization" mentioned.
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。
Code review specialist for quality standards, design patterns, security review, and constructive feedbackUse when "code review, pull request, PR review, code quality, refactor, technical debt, design pattern, best practice, code-review, quality, patterns, security, refactoring, best-practices, pull-request, review, ml-memory" mentioned.
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。
World-class backend engineering - distributed systems, database architecture, API design, and the battle scars from scaling systems that handle millions of requestsUse when "backend, api, database, postgres, mysql, mongodb, redis, graphql, rest, authentication, authorization, caching, queue, background job, webhook, migration, transaction, n+1, rate limit, server, node.js, python, go, backend, api, database, architecture, performance, reliability, security" mentioned.
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。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.
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 fusion of trend prediction and AI content generation: detecting emerging trends before they peak and generating content that rides—or creates—the wave. This is marketing alchemy, transmuting data signals into viral gold. Traditional trend marketing is reactive: see trend, create content, hope you're not too late. AI Trend Alchemy is predictive: detect signal, generate content, be there as the wave crests. The combination of AI trend detection and AI content generation creates a feedback loop that can outpace human competition. This skill combines data science intuition with creative generation, reading the digital tea leaves while simultaneously conjuring the content that captures the moment. It's part analyst, part creator, part prophet. Use when "trend, trending, viral, predict, emerging, what's next, cultural moment, before it peaks, early signal, catch the wave, trend prediction, going viral, trend, prediction, viral, cultural, emerging, timing, alchemy, detection" mentioned.
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。
Citation-integrity forensics: is every reference real, correctly attributed, and used in a context the cited work actually supports? Catches hallucinated references (no paper at the claimed arXiv id/DOI/venue, fabricated authors/year), metadata drift (wrong year/venue/version), and wrong-context citations (a real paper cited for a claim it never makes — or argues against). A hot zone for machine-generated papers. Decidable at L0 (text + canonical sources). Span-anchored to the evidence ledger (claims.json); the executor gathers canonical facts (DBLP / arXiv / DOI), then one FRESH cross-model thread per cited key proposes findings; reviewer != adjudicator. Emits citation-forensics.findings.json; NEVER computes the verdict. Triggers: "citation forensics", "check the references", "hallucinated citations", "wrong-context citation", "verify references", "引用核对".
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。
MEMO-ONLY prior-work overlap advisory: surfaces the two ADVISORY taxonomy signals neither a tool nor a model can decide from the paper alone — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合 stapling) and ADV-DUPLICATE-PUBLICATION (repackaged / duplicate submission). The executor RETRIEVES candidate prior work (DBLP fuzzy-title + boolean · WebSearch · WebFetch) from the paper's own title + contribution spans in the evidence ledger; TWO fresh cross-model codex reviewers (one per axis) LAY OUT the overlap side-by-side against each anchored contribution claim. It NEVER rules 'trivial' or 'duplicate' (that is a human judgment) and absence of a match is NOT evidence of originality. Emits novelty-duplication-advisory.memo.md + an info-only findings mirror; carries NO verdict weight — tools/adjudicate_findings.py lists it in ZERO_WEIGHT_SKILLS and caps it at info. Detect-only. Adapted from ARIS novelty-check, reframed from 'is MY idea novel' to 'here is the overlap a reviewer should weigh'. Triggers: "novelty advisory", "duplication check", "prior-work overlap", "is this stapling", "缝合", "查重", "重复发表", "duplicate submission".
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。