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Suggest and apply code refactorings to improve readability, maintainability, and code quality. Use this skill when improving existing code structure, eliminating code smells, applying design patterns, simplifying complex logic, extracting duplicated code, renaming for clarity, or preparing code for new features. Provides specific before/after examples, explains benefits, identifies risks, and ensures behavior preservation through tests.

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Analyzes and optimizes code for better performance, memory usage, and efficiency. Use when code is slow, memory-intensive, or inefficient. Supports Python and Java optimization including execution speed improvements, memory reduction, database query optimization, and I/O efficiency. Provides before/after examples with detailed explanations of why optimizations work, complexity analysis, and measurable performance improvements.

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Automatically repair buggy code and generate comprehensive tests for Python, Java, and C++ programs. Use when users need to fix logic errors or runtime errors in functions, modules, or repositories. Accepts specifications via natural language descriptions, existing test cases, or input/output examples. Generates corrected code, creates or updates tests to verify correctness and prevent regressions, and produces a detailed report explaining the bug, fix, and testing strategy. Triggers on requests like "fix this bug", "repair this code", "debug this function", or "this code is broken".

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Generate comprehensive API documentation from repository sources including OpenAPI specs, code comments, docstrings, and existing documentation. Use when documenting APIs, creating API reference guides, or summarizing API functionality from codebases. Extracts endpoint details, request/response schemas, authentication methods, and generates code examples. Triggers when users ask to document APIs, generate API docs, create API reference, or summarize API endpoints from a repository.

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Identify and report code smells indicating poor design or maintainability issues in Python code, including duplicate code, magic numbers, hardcoded values, God classes, feature envy, inappropriate intimacy, data clumps, primitive obsession, and long parameter lists. Use when conducting code quality audits, preparing for refactoring, improving codebase maintainability, or performing design reviews. Produces markdown reports with severity ratings, locations, descriptions, and specific refactoring recommendations with before/after examples. Triggers when users ask to find code smells, identify design issues, suggest refactorings, improve code quality, or detect maintainability problems.

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Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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Every option you can pass to the TestDriver client, with defaults and examples

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Find documentation and repository evidence for library behavior, API contracts, error messages, and known bugs. Use when a programming question needs current public documentation, issues, pull requests, or implementation examples.

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。

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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。

Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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Establish a naming convention system for design elements, components, and tokens with clear rules and examples.

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Use when the user drops or points at a local talking-head video and wants the kinetic supers + rapid camera reposition prompt for video-to-video AI (one real take turned into split-second camera snaps that lock off, with bold motion-graphics text synced to the speech). Triggers on /kinetic-multicam, "kinetic supers", "kinetic typography video", a dropped .mp4 plus a motion-graphics-text + camera request. PT examples for reliability, "faz o prompt de kinetic supers desse vídeo", "gera supers cinéticos com cortes de câmera", "bota motion graphics e câmera nesse take".

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Build original LangGraph agents for Warden Protocol and prepare them for publishing in Warden Studio. Use this skill when users want to: (1) Create new Warden agents (not community examples), (2) Build LangGraph-based crypto/Web3 agents, (3) Deploy agents via LangSmith Deployments or custom infra, (4) Participate in the Warden Agent Builder Incentive Programme (open to OpenClaw agents), or (5) Integrate with Warden Studio for Agent Hub publishing.

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Create a comprehensive brand design guide — color palette, typography pairings, UI component previews, and visual identity rules with example mockups.

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Creates professional API documentation using OpenAPI specifications with endpoints, authentication, and interactive examples. Use when documenting REST APIs, creating SDK references, or building developer portals.

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Use when a user wants a TikZ figure for a paper — an icon, an editable architecture/pipeline/flow template, or an example — and wants you to find, edit, and verify it. The single repo-wide skill for working with the OpenTikZ library.

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Base template for Stage1 reviewer-comment breakdown. Use when creating or extending conference-specific Stage1 skills (for example ICLR, ICML, NeurIPS, ACL) so shared extraction, splitting, and response-mapping logic stays consistent.

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Think and work like an expert Dentist-Scientist. Use when a task calls for Dentist-Scientist judgment. Reasons from oral biofilm-host ecology, tissue healing capacity, and patient-level clinical endpoints (DMFS, PD/CAL, implant survival) through PICO/PROSPERO protocols, CAMBRA and 2017 AAP/EFP staging, ISO 4049/14801 bench tests with thermocycling, and GRADE-rated reviews while treating in-vitro-to-chairside leaps, plaque-index surrogates without caries reduction, and examiner calibration drift as first-class failure modes.

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Think and work like an expert Algorithms Researcher. Use when a task calls for Algorithms Researcher judgment. Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.

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Scaffold and amend the L1 Investment Constitution — [ALL_CAPS] placeholder bootstrap, SemVer bump rules, Sync Impact Report, MINOR/MAJOR re-examination dispatch after the gate-5 budget confirm. Thesis creation stays refused until ratification (Q83).

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Provider-independent reference media analysis for generated-media production. Use when an agent must analyze reference images, videos, audio, style boards, product shots, brand assets, mood boards, storyboards, performances, prior cuts, or client examples and translate them into safe, non-copying direction, prompts, QA criteria, provenance records, and handoff notes for image, video, audio, avatar, or post-production agents.

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Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the p53-dependent G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in head and neck), and CRISPRi/a alternatives that bypass the artifact. Use when screening cancer cell lines, diagnosing essentiality at amplified loci, choosing CRISPRcleanR / CERES / Chronos, deciding whether CN correction is needed before MAGeCK / BAGEL2 / drugZ, or switching from Cas9 to CRISPRi.

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Analyze codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis.

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。