Total 53,102 skills, AI & Machine Learning has 8881 skills
Showing 12 of 8881 skills
Retrieve the status of the last diagnosis to continue using it. Use in conjunction with dbs-save. Trigger methods: /dbs-restore, /continue, "continue from last time", "previous conclusion", "where did we leave off in the last diagnosis" Restore the most recent diagnosis snapshot saved by dbs-save. Trigger: /dbs-restore, "continue from last time", "where did we leave off"
Dontbesilent Business Model Diagnosis. Two modes are available: Consultation (dissolve your problem) and Checkup (deconstruct your business model). Triggers include commands like /dbs-diagnosis, /consultation, and natural language phrases such as "Help me analyze my business model", "Diagnose my business", "I have a business question". This service provides business model diagnosis based on dontbesilent's ontological framework, with two core modes: consultation (dissolve your question) and checkup (analyze your business model). Triggers: /dbs-diagnosis, "diagnose my business model", "I have a business question"
Transmission Psychology Decoder. Given a piece of content, use 5 classic communication theories to decode why it resonates with the audience, analyze the underlying audience emotions and effective stance, and output discussion directions for chatrooms. Triggers: /dbs-spread, "Transmission Psychology Decoder", "Why did this go viral", "What do the audience want to hear", "What emotion does this content hit" Transmission psychology decoder. Given a piece of content, analyze the psychological mechanism that makes it resonate, identify the emotional core and effective stance, and output direction for chatroom discussion. Trigger: /dbs-spread, "why does this resonate", "what emotion does this hit", "decode this content"
Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
Discover, vet, and install agent skills by searching ACROSS every major registry at once — skills.sh, clawhub.ai, and GitHub — presenting each board on its own native metric (installs / stars) with the top entry per board, security-scanning the top candidates' real SKILL.md for risky patterns, and flagging what's already installed. Use when the user asks "how do I do X", "find a skill for X", "is there a skill that…", "what skill should I install for…", or wants to extend the agent with a capability that might already exist as a published skill. Unlike single-registry search, this surfaces the best of every platform side by side, so you recommend the genuinely relevant, popular, well-maintained, and SAFE one — not whatever ranked first on one site.
Start, query, and stop a network-specific TAO inference microservice ({network_arch}-inference-microservice) by delegating container execution to the appropriate platform skill. Handles container image resolution, job-payload JSON construction, and the service registry. Use when the user wants to run inference on a TAO model checkpoint using a microservice container, deploy a TAO inference endpoint, or stop a running inference container.
Knowledge comic creator supporting multiple art styles and tones. Creates original educational comics with detailed panel layouts and sequential image generation. Use when user asks to create "知识漫画", "教育漫画", "biography comic", "tutorial comic", or "Logicomix-style comic".
Run a model-diverse subagent council to investigate the same problem from multiple perspectives, compare findings, and produce a final recommendation. Use this skill whenever the user asks for a council, second opinions, multiple agents/models to evaluate one question, parallel investigation, red-team/blue-team comparison, or help deciding between competing technical approaches.
Run an autonomous, spec-driven development "saga" for medium-to-large features using an orchestrator agent and a fleet of worker subagents. Use this skill whenever the user invokes /saga, asks to autonomously build a sizable feature end-to-end with minimal human intervention, wants a comprehensive spec broken into milestones and tasks with airtight validation criteria before parallelized implementation, or wants an orchestrator to delegate implementation to worker agents while preserving its own context window. Trigger on phrases like "run a saga", "autonomously implement this feature", "spec it out then build it with subagents", "orchestrate this big feature end-to-end", or "build this with workers and validate each step". Also use this skill when asked to continue, resume, or pick up an existing saga from its saga directory (e.g. under ~/.sagas).
Create or edit images with Pilio GPT Image 2 through the unified Pilio developer API. Use when the user wants text-to-image generation, prompt-based image editing, restyling, product-photo transformation, or composition from one or more local reference images.
Virtual try-on: clothing, accessories, hairstyles, makeup, glasses, hats, shoes, watches. Use when the user wants to see how an item looks on a person — e.g. "try on this dress", "put these glasses on me", "show me with this hairstyle", "what would I look like in this outfit".
Run a second round on a contested question by circulating each subagent's independent proposal to the other authors and asking for structured pros and cons, then synthesize. Use this skill whenever you have multiple independent proposals or opinions on a contested decision — architecture tradeoffs, code review disagreements, design choices, competing root-cause theories — and want sharper analysis than you'd produce by synthesizing alone. Pairs naturally with the council and research skills; reach for it liberally whenever proposals diverge.