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Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.

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Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.

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Detects 55 AI writing patterns and rewrites text in five voice profiles so it reads like a specific human wrote it, with an optional 0-100 AI-tell score. Use when text sounds AI-generated or like a chatbot, when preparing a blog post, README, or LinkedIn post for publication, when auditing prose for AI tells, or when editing a Markdown file in place. Triggers on phrases like "humanize this", "make this sound less AI", "make this sound human", "remove AI tells", "does this read like ChatGPT", and "rewrite so it does not sound AI-generated". Pure Markdown, zero dependencies, no network calls.

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Fix features broken by REST API responses that flatten Nostr events, losing tag data. Use when: (1) A feature works for WebSocket-loaded events but not REST API-loaded ones, (2) A model field (like sha256, textTrackRef) is null for REST API data but set for WebSocket data, (3) REST API returns denormalized fields (d_tag, video_url) but omits raw tags array, (4) hasSubtitles/hasFeature returns false for REST-loaded videos. Common in Nostr clients that use both REST APIs (for analytics/bulk queries) and WebSocket (for real-time events).

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Fix ClickHouse "Code: 243 Cannot reserve 1.00 MiB, not enough space" errors caused by system log tables (text_log, trace_log, processors_profile_log, query_log) filling the disk. Use when: (1) ClickHouse inserts fail with NOT_ENOUGH_SPACE error, (2) Disk is 100% full but application tables are small, (3) system database is 10-100x larger than user databases, (4) Sentry shows batch insert/commit failures across multiple tables simultaneously. Covers both self-hosted ClickHouse (Altinity operator on K8s) and ClickHouse Cloud with different remediation paths for each.

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Fix .well-known files (apple-app-site-association, assetlinks.json) being served as HTML by the @fastly/compute-js-static-publish SPA fallback instead of JSON. Use when: (1) iOS Universal Links or Android App Links are broken because the verification files return text/html instead of application/json, (2) PublisherServer with spaFile config intercepts /.well-known/ paths and returns index.html (200, text/html) instead of 404 for missing files, (3) apple-app-site-association (no file extension) gets wrong content type even when correctly stored in KV. Covers both apex domain and subdomain handler patterns.

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Fix Flutter macOS build failure caused by duplicate camera plugins. Use when: (1) macOS build fails with "Ambiguous use of 'CameraMacosPlugin.register(with:)'" error, (2) Both camera_macos and camera_macos_plus are in pubspec.yaml, (3) Build worked before adding a new camera package. The plugins define the same class name causing Swift registration conflicts.

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Configure static outbound IP addresses for Google Cloud Run jobs/services. Use when: (1) External service needs to whitelist your IP (archive.org, APIs, firewalls), (2) Cloud Run requests appear from random/changing IPs, (3) Need consistent source IP for crawlers, scrapers, or API clients running on Cloud Run. Requires VPC connector + Cloud NAT + static IP reservation. Works for both Cloud Run services and jobs.

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Fix Dart/Flutter services where calling start() after stop() is a silent no-op because stop() sets a _disposed (or similar) flag that start()'s guard short-circuits on. Use when: (1) A repository/service/controller has startListening/stopListening, subscribe/unsubscribe, open/close, or similar lifecycle methods, (2) Re-opening the service after closing it appears to do nothing — no subscription, no events, no error, (3) Unit tests that only exercise a single mount/open/start cycle pass while the real app breaks on the second visit to a screen, (4) A boolean flag is used both for "in the middle of tearing down this instance forever" AND for "currently stopped, can be re-started". Common in Riverpod/Bloc-driven screens that wire startListening() in initState and stopListening() in dispose — the second time the user visits the screen, nothing happens.

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Fix cert-manager DNS01 ACME challenges stuck in "pending" state with "DNS record not yet propagated" inside GKE private clusters, even when TXT records exist in Cloudflare DNS. Use when: (1) cert-manager challenges show "pending" for hours with propagation check failures, (2) dig from outside cluster shows correct TXT records but cert-manager can't verify them, (3) Using Cloudflare DNS01 solver in a GKE private cluster with Cloud NAT, (4) Google Cloud intercepts 8.8.8.8 DNS queries returning NXDOMAIN for Cloudflare-managed records, (5) Even --dns01-recursive-nameservers with 1.1.1.1 doesn't fix the propagation check despite TXT records being verifiable from busybox pods in the same namespace. Covers the full debugging flow and certbot manual workaround.

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AutoMCM-Pro industrial-grade math modeling agent (opencode binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.

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AutoMCM-Pro industrial-grade math modeling agent (Codex CLI binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.

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AutoMCM-Pro industrial-grade math modeling agent (DeepSeek Harness binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.

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。

AutoMCM-Pro industrial-grade math modeling agent. Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.

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。

Automatically migrate Spring MVC applications to Spring Boot. Use when you need to modernize a Spring MVC project to Spring Boot while preserving functionality. The skill analyzes the codebase, updates build configuration (Maven/Gradle), migrates annotations, converts XML configuration to Java/properties, updates controllers and tests, and creates the Spring Boot main application class. Creates git commits for each migration phase and generates a comprehensive summary. Supports both Maven and Gradle projects.

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Generate comprehensive test case documentation from test code, test framework output, existing test docs, and source code context. Use when documenting test suites, creating test specifications, generating test coverage reports, onboarding developers to testing practices, or preparing QA documentation. Analyzes test functions (pytest, unittest) to extract test names, docstrings, assertions, and organization, then produces structured markdown with both overview-level summaries and detailed test case specifications including purpose, preconditions, steps, expected results, and test data. Triggers when users ask to document tests, generate test specifications, create test reports, summarize test coverage, or explain what tests do.

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Analyze proof states in Isabelle or Coq and suggest applicable tactics to make progress. Use when users need help with: (1) Choosing the next tactic in an interactive proof, (2) Understanding what tactics apply to their current goal, (3) Getting unstuck in a proof, (4) Learning which tactics work for specific goal structures (conjunctions, implications, induction, etc.). Provides 3-5 ranked tactic suggestions with explanations for intermediate-level proofs in both Isabelle/Isar and Coq.

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Generate structured proof skeletons with tactics, strategies, and intermediate lemmas for theorems in Isabelle/HOL or Coq. Use when users need to: (1) Create proof outlines for theorem statements, (2) Generate proof structure with tactic placeholders, (3) Identify key lemmas needed for a proof, (4) Plan proof strategies (induction, case analysis, forward/backward reasoning), (5) Scaffold proofs with intermediate steps and subgoals, or (6) Convert theorem statements into detailed proof templates. Supports both Isabelle/HOL and Coq equally.

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Summarize long Isabelle or Coq proof scripts into high-level logical steps and reasoning flow. Use when users need to: (1) Understand the structure of a complex proof, (2) Document proof strategies for others, (3) Extract the key reasoning steps from verbose proof scripts, (4) Create readable proof outlines from detailed tactical proofs. Produces hierarchical outlines with moderate detail showing proof structure, main cases, key lemmas, and reasoning flow for both Isabelle/Isar and Coq proofs.

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Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Python, JavaScript, Java, and C/C++ with both custom instrumentation and existing replay tools.

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Recommend relevant Isabelle/HOL or Coq standard library theories, lemmas, and tactics based on proof goals. Use when: (1) Users need library lemmas for their proof, (2) Proof goals match standard library patterns, (3) Users ask what libraries to import, (4) Specific lemmas are needed for list/set/arithmetic operations, (5) Users are stuck and need to know what library support exists, or (6) Guidance on find_theorems/Search commands is needed. Supports both Isabelle/HOL and Coq standard libraries.

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Generate formal specifications (definitions, predicates, invariants, pre/post-conditions) in Isabelle/HOL or Coq from informal requirements, source code, pseudocode, or mathematical descriptions. Use when users need to: (1) Formalize algorithms or data structures, (2) Create function specifications with contracts, (3) Generate predicates and properties for verification, (4) Translate informal requirements into formal logic, (5) Specify invariants for loops or data structures, or (6) Create formal definitions for mathematical concepts. Supports both Isabelle/HOL and Coq equally.

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Identify and replace deprecated API usage in source code with modern alternatives. Use when: (1) Modernizing legacy codebases, (2) Upgrading framework versions (React, Django, Spring, etc.), (3) Fixing deprecation warnings in build output, (4) Preparing for major version upgrades, (5) Ensuring code uses current best practices. Supports Python, JavaScript/TypeScript, Java, and other major languages with both AST-based detection and pattern matching for accurate identification and automated replacement with validation.

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Generate Dockerfiles, Docker Compose configurations, and Kubernetes manifests for containerizing applications. Use when: (1) Creating Dockerfiles for Node.js, Python, Java, Go, or other applications, (2) Setting up multi-service environments with Docker Compose, (3) Generating Kubernetes deployments, services, and ingress configurations, (4) Optimizing container images for production, (5) Implementing containerization best practices. Provides both ready-to-use templates and custom-generated configurations based on project requirements.

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。