「red」の検索結果

Fix Monte Carlo project completion simulations that give identical dates for all sub-groups (milestones, priorities, epics). Use when: (1) All sub-group predictions converge to the same date despite different remaining counts, (2) Proportional throughput scaling produces flat/identical results, (3) Applying overall scope rate to individual sub-groups causes simulations to hit max_weeks cap and never converge, (4) Building project forecasting tools that predict completion for sub-groups of a larger backlog. Covers hypergeometric draw model for milestones and cumulative priority model for sequential priorities.

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Fix silent failures in Fastly Compute@Edge when using send_async for fire-and-forget requests. Use when: (1) Background tasks (migrations, webhooks, notifications) triggered from Compute never complete, (2) send_async PendingRequest is immediately dropped, (3) Fire-and-forget pattern works locally but fails in production, (4) An async trigger "logs as triggered" but the target service never receives the request (especially after renaming backend constants — the URL hostname is cosmetic, the backend name determines routing, so a global rename can silently reroute calls to the wrong service). Also covers silent backend-not-found errors when FALLBACK_BACKENDS or other backend constants reference names not configured in the Fastly dashboard.

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Automatically reduces bug-triggering test cases to minimal form while preserving the failure. Use when debugging with large, complex test cases that need simplification, reproducing bugs with minimal examples, or creating regression tests from verbose failure scenarios. Takes a failing test and systematically removes unnecessary inputs, steps, assertions, and code using delta debugging and other reduction algorithms. Supports unit tests, integration tests, input files, and multiple programming languages (Python, JavaScript, Java, C/C++).

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Optimize test suites using mutation testing to maximize mutation kill rate with minimal tests. Use when you need to reduce test suite size while maintaining quality, identify redundant tests, improve mutation coverage, or validate test effectiveness. The skill analyzes test coverage, execution intervals, and redundancy using mutation operators, selects or generates a minimal subset of tests that maximizes mutation kill rate, and outputs an optimized test suite with detailed reports showing killed and surviving mutants. Supports multiple programming languages and mutation testing frameworks.

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Generate Xiaohongshu (XHS / RedNote) cover images using GPT Image 2 / Codex image generation first, with the legacy Gemini CLI as fallback. Triggers for cover generation: "生成封面", "小红书封面", "xhs封面", "制作封面", "帮我做张图", "XHS cover", "RedNote cover". Triggers for cover editing: "修改封面", "改一下封面", "上一张图基础上", "基于这张图调整", "不要重新做", "edit this cover". Triggers for style learning: "学习这个风格", "提取风格", "我想自定义风格", "这张图的风格", "learn this style", "extract style". Always trigger this skill when user mentions 小红书/XHS/RedNote together with 封面/cover/图片, OR when user uploads a reference image and asks about creating a similar style.

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A terminal-native marketing system rendered entirely in Berkeley Mono — every word on the page, from the hero headline down to the footer fine print, is monospaced. The page itself reads like a manpage or a static-site README: warm cream canvas (`#fdfcfc`), nearly-black ink (`#201d1d`), 4px-radius rectangles for the few interactive elements, and bracketed `[+]`/`[-]` ASCII markers used as bullets. The brand's only "visual moment" is a single dark hero card that mocks up the OpenCode TUI itself — black background, monospaced terminal output, ASCII pipe characters, and a wordmark rendered as block-pixel ASCII. Every section sits as a hairline-bordered text block on the cream canvas with no shadows, no gradients, no decorative imagery, and no non-monospaced character anywhere in the system.

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Use when working with Redis in Bun (ioredis, Upstash), caching, pub/sub, session storage, or key-value operations.

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Use when the user wants to simplify, clean up, refactor, tidy, or refine code — their staged/unstaged git changes or a target file/path. Reduces complexity, flattens nesting, removes redundancy and dead code, scores each change by value (holding low-value churn), then runs tests to prove nothing broke. Invoke on: "simplify", "simplify this", "simplify my code/changes", "clean up", "clean this up", "clean up my changes", "refactor this", "make this cleaner", "tidy this up", "reduce complexity", "flatten this", "remove dead code", "make it more readable", "polish before commit", or "absolute simplify". Acts on your working diff; for repo-wide dead code use absolute-prune; for lint/type debt use absolute-debt.

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Real-world Active Directory environment constraints that silently break attacks when ignored: NTLM disabled (Kerberos fallback), AES-only KDCs (RC4 blocked by GPO), LDAP signing and channel binding requiring LDAPS on 636, Kerberos clock skew (KRB_AP_ERR_SKEW), SPNs that must be FQDNs never short names or IPs (the misleading invalidCredentials), Protected Users / LAPS / gMSA / MachineAccountQuota, LDAP 1000-object paging, and VPN latency. Use this whenever a bind, TGT request, LDAP query, or SMB negotiation fails in a way that looks like bad credentials but is really hardening, or before running any AD tooling against a domain you have not fingerprinted. Standard-tooling knowledge (netexec, impacket, certipy, bloodyAD, kerbrute), no vendor-specific engine.

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Generate one Content-Derived Design direction from an HTML source, or let Auto choose the strongest direction. Use when the user says /reimagine-it generate, "redesign this page", "make it a landing page", "turn this into an infographic", names a token (all 17: webpage, infographic, svg, 3js, simulation, artistic, cinematic, dashboard, photography, landing, editorial, gradient, showcase, motion, glass, lookbook, particles), or wants a content-aware redesign instead of a mood board. CLI: npx reimagine-it --auto -i page.html -o out.html. Part of the reimagine-it Content-Derived Design suite.

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Content-Derived Design — reads existing HTML and writes a stronger standalone page from headings, facts, names, dates, numbers, links, emails, and colors already in that file. CLI: npx reimagine-it --auto -i page.html -o out.html; also variations, lock, and audit. Use when the user says /reimagine-it, "reimagine it", "reinvent this", "redesign this page", "make an infographic", wants a content-aware redesign or a visual leap instead of a mood board. All 17 tokens: webpage, infographic, svg, 3js, simulation, artistic, cinematic, dashboard, photography, landing, editorial, gradient, showcase, motion, glass, lookbook, particles — plus leftover words as a brief. Also /reimagine-it audit for quality checks. The agent runs the CLI; it does not hand-author the page. Installed via npx skills add Kayforkind/reimagine-it or plugin marketplace. Not /better. Not a mood board. Does not ingest PDF, PPTX, or DOCX.

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Generate Xiaohongshu (XHS / RedNote) cover images using GPT Image 2 / Codex image generation first, with the legacy Gemini CLI as fallback. Triggers for cover generation: "生成封面", "小红书封面", "xhs封面", "制作封面", "帮我做张图", "XHS cover", "RedNote cover". Triggers for cover editing: "修改封面", "改一下封面", "上一张图基础上", "基于这张图调整", "不要重新做", "edit this cover". Triggers for style learning: "学习这个风格", "提取风格", "我想自定义风格", "这张图的风格", "learn this style", "extract style". Always trigger this skill when user mentions 小红书/XHS/RedNote together with 封面/cover/图片, OR when user uploads a reference image and asks about creating a similar style.

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options income, covered call strategy, cash secured puts, credit spreads, iron condor, wheel strategy, premium selling, yield enhancement, defined risk income, monthly income options

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Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.

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Predict protein-coding genes in eukaryotic genomes using BRAKER3 for combined RNA-seq and protein evidence, or GALBA for protein-only evidence. Runs Augustus with trained parameters for accurate gene models. Use when annotating a newly assembled eukaryotic genome or improving existing gene predictions.

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Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).

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Query AlphaFold protein structure predictions. Use when user asks about protein structure, 3D structure, protein folding, or structure prediction. Triggers on "alphafold", "protein structure", "3D structure", "folding", "pLDDT", "structure prediction".

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Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

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Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2/AF3/Boltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE. For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.

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Search discussions, track communities, analyze posts, and discover trends on Reddit through SandBase. Use when asked for Reddit research, community analysis, trending topics, discussion monitoring, content discovery, or audience insight beyond customer-focused research.

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Discover evidence-backed customer language, pain points, objections, and buying triggers from relevant Reddit communities. Use when asked for Reddit research, voice-of-customer analysis, audience pain points, product feedback, or community-led content opportunities.

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