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Investigate incidents and recurring defects with structured methods such as Five Whys and Fishbone cause maps. Identify multiple contributing conditions, build a blame-free account of what happened, and propose practical changes to prevent similar failures.
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Audit online store product pages for descriptions, images, metadata, links, and structured data that helps search engines understand products. With optional DataForSEO access, compare marketplace prices and identify differences between organic search and Google Shopping visibility.
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Plan and audit large sets of pages generated from structured data. Assess source quality, template uniqueness, URLs, and internal links, then identify thin content and unnecessary search indexing with prioritized recommendations and staged publishing guidance.
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Review a single web page for search optimization, covering titles, content, technical settings, images, and structured data that describes the page to search engines. Receive category scores, prioritized issues, practical recommendations, and suggested markup.
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Review content for citation in AI search answers. Use checklists covering clear definitions, sourced data, author information, FAQs, and structured data, then plan how to monitor citations, brand mentions, and AI-referred traffic.
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Design GraphQL APIs with clear data types, data-fetching functions called resolvers, and real-time updates. Address repeated database calls, pagination, structured errors, and expensive queries while planning schema changes that preserve compatibility.
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Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.
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Design structured summaries for long agent sessions that preserve goals, file changes, decisions, and next actions. Choose compression methods and timing, then test whether the agent can continue accurately without repeatedly retrieving information it already processed.
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Turn structured JSON into editable SVG diagrams for system architectures, workflows, and pipelines. Specify positions, connections, and groups precisely, reproduce the same output under fixed rendering conditions, and convert figures to PDF for papers.
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Turn research ideas and literature into a structured funding proposal. Supports KAKENHI and other funding programs, guiding literature positioning, research aims, methods, timelines, and budget justification, with external review to identify weaknesses in the draft.
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Create new PPTX presentations, DOCX documents, XLSX spreadsheets, and PDF reports with PaperJSX. Describe content and layout in structured JSON, then run a Node.js script to generate the file and check that it is not empty.
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Search and aggregate Datadog logs through the Composio CLI. Filter by service, environment, and time range, follow requests across services, and export structured JSON for incident analysis or scheduled daily digests.
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Coordinate multiple coding agents through Superset in separate workspaces. Split work into bounded tasks, track dependencies and progress, send follow-ups, and collect structured results covering changed files, verification checks, blockers, and the order for combining work.
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Review Agent-Native Research Artifacts, structured research packages for agents, across six dimensions of research quality. Produce a JSON report with scores, strengths, severity-ranked findings, practical improvements, and an overall acceptance or rejection recommendation.
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Explore research directions through ten structured thinking frameworks, then rank ideas by their importance, clarity, and feasibility. Turn a vague interest into a focused proposal with a short explanation, candidate experiments, and an initial feasibility study.
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Build Python 3.11+ applications with type annotations, asynchronous operations, and structured error handling. The skill also creates pytest tests and checks code with mypy, black, and ruff to support consistent implementation and validation.
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Turn a feature idea into a specification through structured questions about users, business goals, and technical constraints. Produce requirements, user stories, testable acceptance criteria, and an implementation checklist, including performance, security, and error handling.
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Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.
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Review code changes and files for bugs, security risks, performance problems, design issues, and test coverage. Receive a structured report with prioritized findings, concrete improvement examples, positive feedback, and an overall review verdict.
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Scan recent merges, pull requests, and commits for release note material. Produce structured entries and a summary with PR references, highlighting breaking changes and security items while filtering routine dependency and bot noise.
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Classify failures in CI, automated checks run during development, to distinguish code regressions, temporary infrastructure problems, security test failures, and inconsistent results. Record each failure's category, suggested next action, and attempt count in a structured report.
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Review merged pull requests and significant commits within a release window, then organize features, fixes, breaking changes, and security signals into structured source material for drafting release notes.
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Research topics on the web and turn findings into a structured brief for content creation. Cross-check important claims across independent sources, collect attributed statistics and quotes, and identify useful angles alongside gaps, disagreements, and outdated evidence.
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Read and summarize AI research papers from Hugging Face or arXiv links and identifiers. Retrieve paper content alongside structured details about authors, related models, datasets, Spaces, and implementation links to support further investigation.
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