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Guides AI agents through repeated failures, passive behavior, and incomplete work using structured investigation, alternative approaches, and evidence from actual checks. It pairs these practices with forceful coaching modeled on corporate performance reviews.
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Detect and check structured data, machine-readable descriptions of page content, and generate suitable Schema.org JSON-LD snippets. Get a report of missing fields, syntax errors, and markup issues, plus code prepared for implementation.
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Investigate a specific OpenShell problem or feature need and create a structured GitHub issue. Record observed behavior, technical findings, alternatives, and missing evidence so a human can decide what to accept and pursue.
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Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.
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Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.
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Turn papers, code, experiment logs, and notes into an ARA, a structured research package for AI agents. Organizes claims, evidence, verification plans, code stubs, and research decisions, then checks coverage and consistency across the generated files.
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Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.
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Turn a structured change list into a categorized release notes draft for users. Follow the project's writing style, highlight breaking changes and security items, and include upgrade guidance where evident, leaving publication to human review.
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Turn unstructured clinical encounter summaries into SOAP notes, a four-part format for medical records. Organize symptoms and findings, include diagnoses with ICD-10 classification codes, and document treatment steps, follow-up timing, and reasons to return for care.
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Builds and improves C/C++ and Rust fuzzing harnesses, the entry points that feed generated test data into code. Helps structure inputs, reach more code paths, and address crashes caused by inconsistent test behavior.
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Improve how search engines discover and understand a website by reviewing titles, descriptions, links, and sitemaps. Add structured data, machine-readable descriptions of page content, and use checklists to identify technical and on-page SEO issues.
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Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.
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Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.
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Coauthor proposals, specifications, and other structured documents through context gathering, section-by-section drafting, and revision. Test the finished draft with a fresh Claude conversation to identify unclear assumptions, missing information, and contradictions.
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Search Zhihu articles by keyword and publication period through the BrowserAct API. Retrieve full article text, titles, authors, dates, cover image URLs, and source links as structured data for industry research, brand monitoring, or competitor updates.
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Collect structured Google Images search results through the BrowserAct API. Set keywords, country, language, scrolling, and result limits to obtain titles, thumbnail URLs, source names, source logos, and destination links for visual research.
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Search Google News by keyword and publication period through the BrowserAct API. Collect headlines, publishers, publication times, and article links in a structured form for industry research, brand coverage checks, and competitor news tracking.
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Collect structured YouTube search results for videos, Shorts, channels, or playlists. Retrieve titles, displayed view counts, publication times, and URLs to build topic lists, research competitors, or gather tutorial resources.
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Collect Reddit posts and comments through the BrowserAct API using keywords, date filters, and sorting options. Results include votes, authors, timestamps, and reply relationships, providing structured source data for competitor research and reviews of community feedback.
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When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema Markup Validator," "Google Rich Results Test," "FAQ schema," "Article schema," "Organization schema," "JobPosting," "HowTo," "Event," "SoftwareApplication," "BreadcrumbList," "WebSite," "Recipe," "Product," or "Dataset." For SERP feature types and zero-click patterns, use serp-features. For AI search visibility strategy (not markup), use generative-engine-optimization. For HowTo step sections (placement, copy, vs FAQ), use howto-section-generator.
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Assesses a legal argument, submission, or piece of structured reasoning from the perspective of a judge reading it cold under time pressure. Produces a structured seven-part assessment: what the case appears to be about, immediate points of confusion, what feels strong, what feels weak, what is assumed but unproved, a provisional confidence level (low/medium/high), and what would be needed to persuade. The skill does not rewrite, improve, or attack the argument — it tells you how it actually lands on a sceptical, experienced reader with no prior context. Works on skeleton arguments, witness statements, letters before action, position statements, academic articles, and non-legal structured reasoning.
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Generate Schema.org structured data (JSON-LD) for any page type. Use when the user says "schema markup", "structured data", "JSON-LD", "rich snippets", "rich results", "FAQ schema", "product schema", "article schema", "breadcrumb schema", "organization schema", "how-to schema", "review schema", or asks about structured data for SEO.
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Implement modern concurrency with Virtual Threads and Structured Concurrency in Java. Use when implementing Java Virtual Threads (Java 21), Structured Concurrency with StructuredTaskScope, CompletableFuture pipelines, or debugging race conditions.
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Structured data extraction - tables, pricing, products, API endpoints with schema
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