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概要と使いどころ
Implements error handling patterns, structured logging, retry strategies, circuit breakers, and graceful degradation. Use when designing error handling, setting up logging, implementing retries, adding error tracking, or when asked about error boundaries, log aggregation, alerting, or resilience patterns.
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 this skill when planning, redesigning, or reviewing Xiaohongshu carousel visuals, covers, 3:4 image posts, page-by-page visual direction, image-generation prompts, style selection, layout critique, or XHS captions. It acts as a senior visual director for 小红书图文, not a generic copywriting assistant.
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。
Help users interact with probabilistic models by designing interfaces that manage fluidity, intent, and agency while maintaining trust and control.
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。
Help users build a robust enterprise sales engine by mastering founder-led discovery, navigating complex procurement cycles, and designing products that meet organizational governance needs.
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。
Help users navigate the end-to-end negotiation process for product and engineering roles, from initial recruiter calls to evaluating startup equity and final offer signing.
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 designing, changing, or reviewing user-facing interfaces or interaction flows, including API changes that alter visible states or recovery. Backend-only work without user-facing effects stays on its existing route.
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。
Detect insecure JWT (JSON Web Token) implementations in a codebase using a two-phase approach: first map all JWT issuance and verification sites to understand the token lifecycle and signing configuration, then check each verification site for exploitable weaknesses such as algorithm confusion, missing signature verification, weak secrets, header injection, and missing claim validation. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/jwt-results.md. If no JWT usage is found in Phase 1, Phase 2 is skipped. Use when asked to find JWT, token forgery, or authentication bypass bugs.
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。
Open image shots (screenshots, snapshot baselines, montages, renders) in ONE macOS Preview window so the user can eyeball them. Use when the user asks to open / show / pull up / review shots in Preview or Finder, or right after producing visual artifacts the user should look at before signing off.
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。
Shared loop-engineering reference for COG skills - the agent loop, deterministic verifiers, termination conditions, in-loop context management, and named patterns. Invoke when designing or debugging a skill that iterates (search-verify-retry, scan-until-dry, fetch-retry-gate).
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。
Reapply and repair Windows Codex Desktop after Store upgrades, including custom provider models hidden by Statsig available_models filtering, the dependent blue-purple Power slider and its Ultra toggle, Fast Mode request/UI gates, locale i18n, plugin UI gates, Chrome/browser_use gates, Goal command gates, Windows Computer Use availability gates and plugin/runtime repair, phone remote-control pairing under third-party/API-key main app usage, Desktop dynamicTools/inputSchema thread-start schema drift, local conversation visibility recovery after model_provider switches, restored-conversation missing-cwd continuation repair, ASAR integrity repair, signing/installing patched MSIX packages, SDK cleanup, Fast Mode wire verification, local plugin marketplace registration, and optional custom model_instructions_file setup (the bundled system prompt, also called 听话水).
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。
Desktop JVM and build traps: JNA natives, bundling, memory, packaging, code signing, R8, deep links, Gradle and CI releases. Use when a desktop build fails packaged, signed, on another OS or arch, or in CI.
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 designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.
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 designing or auditing figures for an Advanced Materials manuscript, including the lead figure, multi-panel characterization figures, and the Table-of-Contents graphic, where visual quality is a house expectation. Designs figure strategy; does not run the analysis or write prose.
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 designing or auditing ACM CCS experiments, attack demonstrations, adaptive-attack defense evaluations, security measurements, baselines, overhead and cost reporting, ablations, and claim-to-evidence fit, with emphasis on evidence that survives an adversarial program committee rather than leaderboard wins.
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 designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia claim.
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 designing the study for an Accounting, Organizations and Society (AOS) manuscript — qualitative field studies (access, case logic, interviews, observation), behavioral experiments, surveys, historical/archival inquiry, or theory-laden archival designs, each with the ethics and site-anonymity groundwork done early. Designs the inquiry; it does not run the coding or estimation (aos-data-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。
Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.
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 designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
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 designing or auditing AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard.
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, structure, and package agent skills. Use when designing new skills, updating existing skills, or helping users build skills with scripts, references, and assets. Triggers on requests to create skills, write SKILL.md files, or structure skill directories.
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。
Comprehensive GTM analysis of any target company. Researches what a company is currently doing across all go-to-market dimensions — content/blog, founder LinkedIn activity, SEO/traffic, hiring signals, social/community presence, customer acquisition channels, podcast appearances, review sites, competitive positioning, and partnerships. Produces a structured report identifying what's working, what's missing, and where white space exists for new strategies. Use before designing GTM strategies for a client or prospect.
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。
Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate end-protection, and ranked HDR enhancers. Use when designing a donor for a point mutation, epitope/fluorophore tag, allele replacement, or knock-in, or when HDR efficiency is low. Guide design and base/prime editing are separate skills.
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。
Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate skills.
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。
Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.
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。