Total 56,302 skills, AI & Machine Learning has 9376 skills
Showing 12 of 9376 skills
AscendC Operator End-to-End Development Orchestrator. Used when users need to develop new operators, implement custom operators, or complete the full process from requirements to testing. Keywords: operator development, end-to-end, full process, workflow orchestration, new operator creation.
Generate 3D CGI and rendered video prompts for Seedance 2.0 (Higgsfield). Use this when users want 3D rendering, CGI, Pixar-style, Unreal Engine, photorealistic 3D, computer-generated, or digitally rendered video content. Trigger words: 3D animation, CGI, rendering, Blender, Unreal Engine, octane render, ray tracing, volume, subsurface scattering, physically based rendering, or any 3D/CG video request. Use this even if the user only says "make it look 3D" or describes a rendering aesthetic.
Independence-validated parallel fleet that runs each worker (claude -p or codex exec) in its own git worktree. Use when tasks touch non-overlapping files and you need merge-safe isolation (each worker on its own branch). For DAG-ordered one-shot workers with budgets, use dag-fleet. For headless iteration with a reviewer loop, use iterative-fleet.
Analyze a task, pick the right fleet type, and generate a ready-to-launch fleet (fleet.json + prompt.md files). Discovers available fleet skills dynamically. Use when the user wants to run work in parallel, asks to "plan a fleet", or says "fleet-plan".
Use when creating a new skill, adding a skill to the user's setup, or the user says "make this a skill". All personal skills live in the arjit-skills monorepo and are symlinked into place.
Route audio, video, transcript, subtitle, and edit-prep requests into the right media-understanding workflow before execution. Use this when the user wants transcription, subtitle generation, beat mapping, B-roll planning, or edit-ready outputs and the first question is which skill and model chain should run.
Forces adversarial reasoning before committing to decisions. Triggers on architectural choices, approach selection, and planning phases to prevent premature commitment bias.
Resolves experiment references from natural language to concrete experiment IDs. Handles name lookups, fuzzy descriptions ('the signup experiment', 'my latest experiment'), status filtering, and disambiguation when multiple experiments match. TRIGGER when: user refers to an experiment by name, description, or relative reference ('latest', 'most recent', 'the one I created yesterday') and you don't already have the experiment ID. DO NOT TRIGGER when: user provides an experiment ID directly, or you already resolved the experiment earlier in the conversation.
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Use when adding capabilities to an existing agent project — memory, app integration, VPC, multi-agent, migration, model changes, browser, code interpreter, or resource removal. Triggers on: "add memory", "remember across sessions", "call agent from app", "invoke agent from code", "auth to call agent", "streaming responses", "VPC", "VPC connectivity", "VPC error", "can't reach from VPC", "multi-agent", "A2A", "A2A auth", "orchestrator not delegating", "specialist not called", "migrate Bedrock Agent", "after import", "migration issue", "framework for migration", "change model", "browser tool", "code interpreter", "delete agent", "tear down", "agentcore remove", "cross-account memory", "resource-based policy on memory". Not for connecting to external APIs via Gateway — use agents-connect. Not for scaffolding a new project — use agents-get-started. Not for CLI/dev server errors — use agents-debug. Strands vs LangGraph in a migration context routes here.
Create and manage agent graphs — directed graphs of AI Configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
Multi-perspective adversarial review. 4 Agents are spawned in parallel (full mode), each identifying issues from different perspectives, and the main thread makes a comprehensive ruling. Trigger methods: /story-review, /审查, "审查一下", "帮我审一下"