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Move reusable code and its tests from OpenHuman into the appropriate tiny* submodule. Separate host responsibilities, update module interfaces, verify the migration, and align release versions and integrity hashes when a loadable module changes.

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• Extract reusable logic from the OpenHuman core
• Move implementation tests into the owning submodule
• Update TinyBus contracts and host adapters

Prepare for CMMC Level 2, a cybersecurity certification for defense contractors. Define the systems handling controlled information, review security requirements, calculate the SPRS assessment score, and organize remediation plans and evidence into a readiness report.

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• Define the assessment scope for systems handling controlled information
• Calculate an SPRS score and prioritize unmet security requirements
• Prepare security plans, remediation milestones, and assessment evidence

Use a concise SEO reference to review content credibility, page performance, site structure, and page elements. It explains key checks, structured data types, measurement tools, and editorial practices for AI-assisted content.

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• Learn the main areas to check for SEO
• Review credibility and originality in articles
• Understand page performance metrics and targets

Review completed AI work by scoring task difficulty and execution quality separately. A fixed scoring matrix, arguments for higher and lower ratings, and saved score history help examine weaknesses and detect repetitive scoring patterns across sessions.

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• Evaluate a completed feature implementation
• Review the quality and coverage of a bug fix
• Reflect on work completed during a session

Analyze customer recommendation and satisfaction surveys, NPS and CSAT, from Google Sheets. Group comments into themes, explain weekly score changes and low-score drivers, and post a Slack digest with representative quotes without modifying survey data.

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• Summarizing customer surveys each week
• Identifying recurring complaints behind low scores
• Comparing NPS or CSAT with the previous period

Examine writing for familiar AI patterns, statistical features, Pangram detection scores, and hidden character signatures. Compare drafts against known human writing to interpret the evidence, with explanations and limitations rather than a definitive authorship claim.

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• Check a post for common AI writing patterns
• Compare AI detection scores across drafts
• Compare a revision with known human writing

Define the structure and available components for AI-generated JSON screens or videos. Build generation prompts, process incremental updates, connect values to application state, and validate or repair definitions before passing them to a renderer.

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• Define allowed components for AI-generated screens
• Build prompts for generating or editing JSON definitions
• Assemble definitions from streamed AI responses

Audit a website’s technical foundations for search engines and AI services. Check crawler access, indexing, security, mobile usability, speed, and server-rendered content, then produce a scored Markdown report with prioritized findings.

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• Check whether AI crawlers can access key pages
• Find content missing from the initial HTML
• Review page speed and mobile usability

Review an implemented frontend through a visual audit organized around six pillars. Get a Markdown report with a score from 1 to 4 for each pillar, covering a specified phase or the last completed phase.

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• Reviewing the visual quality of an implemented frontend
• Recording a visual review for a specific phase
• Keeping a scored UI assessment as a Markdown report

Navigate Apache Beam contributions from finding an issue to submitting a pull request, a proposed code change, and responding to reviews. Covers environment setup, tests, formatting, licensing requirements, and closer evaluation of changes to core components.

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• Prepare a first Apache Beam contribution
• Organize a pull request and its related issue
• Check tests and formatting before submission

Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.

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• Running C/C++ fuzzing across multiple CPU cores
• Exploring further after libFuzzer progress stalls
• Monitoring fuzzing progress and stability

Evaluate local Codex skills and plugins, understand their scores, and identify fixes to prioritize. Get explanations of token estimates, set up benchmark scenarios, and prepare workflows for measuring actual token usage.

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• Evaluate a local skill or plugin
• Understand scores and prioritize fixes
• Explain token estimates and prepare actual measurements

Audit page loading speed with Chrome DevTools MCP, a tool connection for browser inspection. Measure performance indicators, investigate network delays and layout shifts, check accessibility gaps, and produce prioritized fixes with estimated impact and relevant code findings.

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• Investigate slow page loads
• Find causes of layout shifts
• Identify improvements related to Lighthouse scores

Improve website loading and responsiveness by optimizing images, fonts, scripts, and caching. Identify priorities, reduce JavaScript download size, split code into smaller loads, and verify changes through a structured performance improvement workflow.

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• Improving slow page loading
• Reducing JavaScript download size
• Reviewing image and font loading

Create and distribute NFTs and tokens on Solana, or register blockchain identities for agents. The skill guides selection of Metaplex tools and explains when to use its command-line interface or SDKs, libraries for writing application code.

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• Create NFTs and collections on Solana
• Prepare an NFT drop with minting rules
• Launch a token with Genesis

Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.

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• Summarize Excel records using custom categories
• Compare category distributions across groups
• Score and grade text using adjustable rules

Configure automatic crash dumps, files that capture application state, or collect one from a running modern .NET process. Follow platform and runtime guidance for desktop, server, Docker, or Kubernetes environments and receive the saved file location.

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• Enabling automatic dump collection when a .NET app crashes
• Capturing a dump from a running .NET process
• Setting up dump collection in Docker or Kubernetes

Generate a Chinese master's thesis review from the full thesis and a user-provided score. Produce an academic evaluation plus specific shortcomings and revision suggestions, with tone and criticism adjusted to the supplied score.

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• Drafting a Chinese master's thesis review
• Matching review wording to an assigned score
• Turning thesis-specific issues into revision suggestions

Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.

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• Build the core structure for claude-flow v3
• Implement task assignment and status rules
• Connect SQLite storage through dependency injection

Full SEO audit combining Google Search Console, URL Inspection API, PageSpeed Insights, a technical crawl, keyword research, and metadata, schema, search intent and Core Web Vitals checks. Surfaces quick wins, diagnoses traffic drops and content gaps, and produces an actionable 30-day plan. Use whenever the user asks about SEO, search rankings, organic traffic, Google Search Console, keyword performance, search visibility, technical SEO, URL indexing, or LCP, INP, CLS or Lighthouse scores. Also trigger on: "why is my traffic down", "what keywords am I ranking for", "improve my rankings", "check my search console", "SEO audit", "analyze my SEO", "technical SEO", "meta tags", "indexing issues", "crawl errors", "content strategy", "keyword cannibalization", "search intent", "schema markup", "structured data", "URL inspection", "page speed", "performance score", "core web vitals", "lighthouse", or any organic search question. If in doubt, trigger.

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[omh] Aiming for design-award quality: score a web surface against published design-award judging axes and name the binding constraint. Use when the user says: award-bar-score, award bar score, award winning, award-winning, award winning website, award-winning website, award winning design, award ready.

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Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). Also for prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend.

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AWS SDK for Python (boto3/botocore) development patterns. You MUST use this skill when writing Python code that uses AWS services via boto3 or botocore. This includes creating service clients or resources, configuring sessions and credentials, handling errors with ClientError, using paginators and waiters, S3 file transfers and presigned URLs, DynamoDB table operations, and any boto3/botocore client configuration. Use this skill whenever Python code imports boto3 or botocore, or when the user asks about AWS operations in Python.

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Score one blog post (or a folder of posts) on a 5-category 100-point scale covering content quality, SEO, E-E-A-T, technical elements, and AI citation readiness. Runs the deterministic analyzer first, then adds judgment and prioritized Critical/High/Medium/Low fixes. Advisory style diagnostics never infer authorship or affect the score. Exports markdown, JSON or table; works with MDX, markdown, HTML or a URL. Use when the user asks to analyze, score, rate or check the quality of specific posts ("analyze blog", "blog score", "check blog quality", "rate this post"). For a site-wide health check (orphan pages, cannibalization, stale content, schema across all posts) use blog-audit instead.

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