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Convert PDF text and tables into Markdown for later summarizing, searching, or data extraction. Embedded images are saved separately and grouped by page in an appendix. Process individual files or entire folders, including subfolders.
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Verify file splits, function moves, module extractions, and renames by reproducing changes from a base commit and comparing files byte for byte. Generate repeatable proof scripts and reports that expose changes beyond relocation.
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Monitor requests to an AI prediction API for signs of model copying or training-data exposure. Test an authorized model with simulated attacks, measure the risk, and evaluate defenses such as request limits and reduced prediction detail.
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Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.
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Review code changes for unnecessary complexity, including unused flexibility, avoidable dependencies, and abstractions with little purpose. Receive concise findings that name what to remove, what can replace it, and how many lines could be cut.
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Research topics across multiple sources, cross-check findings, and verify source URLs before producing a report with confidence labels. Choose from quick lookups to iterative investigations that preserve findings, with community sources prioritized for questions about public reactions.
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Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.
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Recover readable text from PDFs when read_file or Python extraction fails. Use pdftotext, a command-line text extractor, with options to preserve layout, specify character encoding, or save extracted content to a file.
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Retrieve PDFs from web sources and extract their text through an ordered sequence: direct reading, pdftotext, then PyMuPDF. Includes file validation, extraction checks, and guidance for documenting inaccessible sources and distinguishing supplementary content.
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Download PDFs from web URLs or process local files, verify their format, and extract text with pdftotext. If extraction fails or looks poor, try PyMuPDF, check the results, and document any remaining source limitations.
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Extract text from difficult PDFs by trying several libraries and command-line tools in sequence, then using OCR, which reads text from images. Record failed attempts and check that extracted content meets the task requirements.
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Investigate how credentials can be exposed in CI/CD, the systems that build and deploy software. Covers environment variables, logs, credential stores, cloud trust settings, and runner tokens, with methods to assess access scope and defensive controls.
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Replace C# static calls for time, files, or environment values with existing abstractions that tests can substitute. Update construction and affected tests within a defined scope, while preserving read counts, evaluation order, date-time semantics, and existing static callers.
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X (Twitter) data extraction and monitoring via Xquik: tweet search, user lookup, follower extraction, giveaway draws, trending topics, account monitoring with webhooks, reply/retweet/quote extraction, community and Space data, follow checks. 22 MCP tools + REST API.
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Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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DO NOT USE when the target already consumes an injected interface or built-in abstraction such as IFileSystem or TimeProvider, even if the request says "generate a wrapper"; no new wrapper is needed. Use only when C# source calls an ambient/static dependency and no injectable seam exists: first-time TimeProvider, IHttpClientFactory, or System.IO.Abstractions adoption; minimal Environment/Console/Process wrappers; IProcessRunner; DI registration; or an ambient seam that preserves a static API. Exclude static detection (detect-static-dependencies), migration to an existing/registered abstraction (migrate-static-to-wrapper), one blocked behavior plus deterministic tests (testability-obstacle), and general interface design.
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Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
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Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.
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You are an expert in Crawl4AI, the open-source web crawler built for AI applications. You help developers extract clean, structured data from websites for LLM training, RAG pipelines, and content analysis — with automatic markdown conversion, JavaScript rendering, CSS-based extraction, LLM-powered structured extraction, and session management for multi-page crawling.
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Kubernetes is the operating system for the cloud. It schedules containers, handles networking, manages storage, and keeps your apps running. But K8s has a steep learning curve - abstractions on abstractions on abstractions. This skill covers the core concepts (Pods, Deployments, Services), production patterns (health checks, resource limits, HPA), and the gotchas that catch everyone (namespace confusion, service discovery, secret management). 2025 reality: You probably don't need to run your own cluster. Use managed K8s (EKS, GKE, AKS) unless you have a very good reason not to. Focus on writing good manifests, not managing etcd. Use when "kubernetes, k8s, kubectl, deployment manifest, helm chart, kustomize, pod, service yaml, ingress, horizontal pod autoscaler, hpa, kubernetes, k8s, containers, orchestration, devops, cloud-native, helm, kustomize" mentioned.
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Comprehensive expertise in ERC-4337 account abstraction, smart contract wallets, paymasters, bundlers, and user operation handling. Covers social recovery, session keys, gas sponsorship, and wallet SDKs. Use when "account abstraction, ERC-4337, smart wallet, paymaster, bundler, user operation, gasless transaction, session keys, social recovery, smart account, " mentioned.
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Audits Claude Code repositories. Two-phase workflow — Phase 1 sweeps 14 hygiene categories (permissions, hooks, CLAUDE.md bloat, Skills hygiene, process-abstraction, plus structural) and an optional 7-layer leak check. Phase 2 executes [APPROVED] markers from the report file. Use for repo cleanup, pre-distribution sanity, post-feature hygiene, environment audit, process-abstraction discovery. Trigger on "audit my CC repo," "sweep my repository for hygiene," "check this CC environment," "find process abstraction candidates," "review my CLAUDE.md," "clean up my .claude directory," "pre-distribution hygiene check," "find layer leaks in my CC setup." Routes structural findings to rootnode-cc-design REMEDIATE. Composes with rootnode-critic-gate. CC-only — does not scan Claude Projects. Do NOT use for building a Skill (use rootnode-skill-builder), compiling a prompt (use rootnode-prompt-compilation), scoring a single prompt (use rootnode-prompt-validation), or auditing a Project (use rootnode-project-audit).
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Build AI data extraction pipelines. TRIGGERS - Use when user needs help with ai-extraction-pipeline related tasks.
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