Total 55,607 skills, AI & Machine Learning has 9242 skills
Showing 12 of 9242 skills
Adversarial due-diligence on a benchmark you envy — a founder, KOL, company, or product whose claimed success you suspect is inflated. Inline four-phase orchestration — fan-out collection, adversarial verification grading every claim L1-L4 to split marketing bubble from real signal, attribution weighting (product vs timing vs IP vs luck, what's replicable), then mapping the validated playbook onto the user's own resources. Use whenever the user wants to 尽调/对标/拆解 a competitor or role-model, 抄/偷师 someone's playbook, suspects 水分/泡沫 in their claims (Product Hunt
Operate SkipCalls AI phone receptionists through MCP. Use when the user mentions SkipCalls, AI receptionist/answering service/front desk, missed or inbound calls, call transcripts, scheduling a one-time outbound call, greetings, tasks, calendars, transfers, SMS behavior, business profile Q&A, or MCP connector setup.
Install Zero and load version-matched workflows with zero skills.
Cosmos-Reason2-8B video QA supervised fine-tuning with FSDP parallelism. Use when training or evaluating video question-answering models, fine-tuning Cosmos-Reason2 with SFT, or working with Cosmos-RL. Trigger phrases include "fine-tune Cosmos-Reason", "Cosmos-RL SFT", "video QA fine-tune", "Cosmos-Reason2-8B training".
OCRNet for scene text recognition. Recognizes text content from cropped text-region images and supports CTC and attention-based decoders. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a TAO OCRNet model. Trigger phrases include "train OCRNet", "scene text recognition", "OCR cropped text", "CTC / attention text decoder".
Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. Use when the user asks to "fine-tune Cosmos-Embed1", "run cosmos-embed inference", "export Cosmos-Embed1", "embed videos", or "search videos with text".
Declared architecture snapshot for one Agentforce agent: planner, topics, actions, flows, Apex, prompt templates, and NGA plugins. Renders a human-readable architecture document and Mermaid invocation graph from design-time metadata (not runtime audit rows). TRIGGER when user asks to describe, diagram, inventory, audit, document, or diff (e.g. v3 vs v5) the architecture / action tree / topic structure / tool inventory of a specific agent by agent API name in a specific org. DO NOT TRIGGER for runtime session traces, conversation transcripts, generation timings, or gateway audit chains — this skill reads design-time metadata only (use investigating-agentforce-d360 for session traces).
Legacy-project style inheritance skill. Use when the user types /inherit-legacy-style, or when onboarding an AI coding agent onto a hand-written legacy project and you need to prevent "style drift" (the model imposing its pretrained mainstream idioms onto the project). Language- and framework-agnostic — it aligns meta-architecture only, not syntax. Once run, it becomes a behavioral constraint on all subsequent coding tasks. Do NOT use for pure research or one-off questions unrelated to code-style alignment.
Data Cloud 360° view of a single Agentforce session. TRIGGER when user asks to trace, inspect, summarize, or describe a specific Agentforce session by session id (Agent Session UUID `019d…` or MessagingSession id `0Mw…`). Also triggers on session discovery — find/list/search sessions by time, agent, channel, outcome, or conversation text — when the user has no session id yet. DO NOT TRIGGER for design-time architecture questions (use agentforce-architecture-analyze instead) or for runtime perf/latency/SLO questions that require platform telemetry beyond Data Cloud.
Generate speech from text using Google Gemini TTS models via scripts/. Use for text-to-speech, audio generation, voice synthesis, multi-speaker conversations, and creating audio content. Supports multiple voices and streaming. Triggers on "text to speech", "TTS", "generate audio", "voice synthesis", "speak this text".
VoltAgent architectural patterns and conventions. Covers agents vs workflows, project layout, memory, servers, and observability.
Set up and manage local skills for automatic matching and invocation