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Found 352 Skills
Convert text to natural speech with DIA TTS, Kokoro, Chatterbox, and more via inference.sh CLI. Models: DIA TTS (conversational), Kokoro TTS, Chatterbox, Higgs Audio, VibeVoice (podcasts). Capabilities: text-to-speech, voice cloning, multi-speaker dialogue, podcast generation, expressive speech. Use for: voiceovers, audiobooks, podcasts, accessibility, video narration, IVR, voice assistants. Triggers: text to speech, tts, voice generation, ai voice, speech synthesis, voice over, generate speech, ai narrator, voice cloning, text to audio, elevenlabs alternative, voice ai, ai voiceover, speech generator, natural voice
Character consistency across AI-generated images with reference sheets and LoRA techniques. Covers turnaround views, expression sheets, color palettes, and style consistency tricks. Use for: character design, game art, illustration, animation, comics, visual novels. Triggers: character design, character sheet, character consistency, character reference, turnaround sheet, expression sheet, character art, consistent character, character concept, reference sheet, character creation, oc design, character bible
Ultra-compressed Chinese communication mode. Express complete technical information with fewer Chinese characters, while retaining code, terminology, original error messages and key constraints. Supported intensity levels: lite, full (default), ultra. Applicable when users mention "Chinese caveman", "caveman mode", "shorter", "fewer words", "use Chinese caveman", or call /caveman-cn. Also applicable to Chinese conversations where token saving is explicitly required.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Automatically detects and documents user preferences, coding rules, and style guidelines when expressed during conversations
Forces exhaustive problem-solving using corporate PUA rhetoric and structured debugging methodology. MUST trigger when: (1) any task has failed 2+ times or you're stuck in a loop tweaking the same approach; (2) you're about to say 'I cannot', suggest the user do something manually, or blame the environment without verifying; (3) you catch yourself being passive — not searching, not reading source, not verifying, just waiting for instructions; (4) user expresses frustration in ANY form: 'try harder', 'stop giving up', 'figure it out', 'why isn't this working', 'again???', '换个方法', '为什么还不行', '你再试试', '加油', '你怎么又失败了', or any similar sentiment even if phrased differently. Also trigger when facing complex multi-step debugging, environment issues, config problems, or deployment failures where giving up early is tempting. Applies to ALL task types: code, config, research, writing, deployment, infrastructure, API integration. Do NOT trigger on first-attempt failures or when a known fix is already executing successfully.
LaTeX academic paper assistant for English papers (IEEE, ACM, Springer, NeurIPS, ICML). Domains: Deep Learning, Time Series, Industrial Control. Triggers (use ANY module independently): - "compile", "编译", "build latex" → Compilation Module - "format check", "chktex", "格式检查" → Format Check Module - "grammar", "语法", "proofread", "润色" → Grammar Analysis Module - "long sentence", "长句", "simplify" → Long Sentence Analysis Module - "academic tone", "学术表达", "improve writing" → Expression Module - "logic", "coherence", "methodology", "argument structure", "论证" → Logical Coherence & Methodological Depth Module - "translate", "翻译", "中译英", "Chinese to English" → Translation Module - "bib", "bibliography", "参考文献" → Bibliography Module - "deai", "去AI化", "humanize", "reduce AI traces" → De-AI Editing Module - "title", "标题", "title optimization", "create title" → Title Optimization Module
Decide how to split skill content between SKILL.md and reference files for context efficiency and reliable triggering. Use this whenever creating a new Claude skill, refactoring an existing one, or when a SKILL.md is growing past 300-400 lines. Also trigger when the user mentions "progressive disclosure", "reference files", "splitting skills", "skill bundling", "context window for skills", "SKILL.md too long", "what goes in references/", "skill structure", or expresses any uncertainty about where to put content within a skill. Use this even if the user phrases the question as a triggering problem ("how do I make my skill trigger better"), because that question is often confused with the splitting question and needs to be disentangled first.