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Found 196 Skills
Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains `from manimlib import *`, (3) User runs `manimgl` CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown version) - OpenGL-based animation engine with interactive development. Covers InteractiveScene, Tex with t2c, camera frame control, interactive mode (-se flag), 3D rendering, and checkpoint_paste() workflow. NOT for Manim Community Edition (which uses `manim` imports and `manim` CLI).
High-performance web crawler for discovering and mapping website structure. Use when users ask to crawl a website, map site structure, discover pages, find all URLs on a site, analyze link relationships, or generate site reports. Supports sitemap discovery, checkpoint/resume, rate limiting, and HTML report generation.
Use when "training LLM", "finetuning", "RLHF", "distributed training", "DeepSpeed", "Accelerate", "PyTorch Lightning", "Ray Train", "TRL", "Unsloth", "LoRA training", "flash attention", "gradient checkpointing"
[Tooling & Meta] Save memory checkpoint to preserve analysis context
Plan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and evaluation of trained checkpoints or existing/hosted endpoints. Use when a request names a Nemotron step or workflow, or asks to clean, translate, train, fine-tune, align, convert, optimize, evaluate, or compose these into a pipeline. Do NOT use for frontend/dashboard/visualization work, generic ML advice, billing/access, or non-Nemotron coding tasks.
This skill should be used at natural checkpoints (after completing complex tasks, at session end, or when friction occurs) to reflect on skill and process execution and identify targeted improvements. Use when experiencing confusion, repeated failures, or discovering new patterns that should be codified into skills for smoother future operation.
When a user asks how long a task will take, requests a time estimate, or before starting any non-trivial coding task, immediately run scripts/estimate_task.py to analyze the codebase scope and provide a data-driven time estimate. Show the estimate breakdown, risk factors, and checkpoint recommendations without asking.
Configure Lakebase for agent memory storage. Use when: (1) Adding memory capabilities to the agent, (2) 'Failed to connect to Lakebase' errors, (3) Permission errors on checkpoint/store tables, (4) User says 'lakebase', 'memory setup', or 'add memory'.
BAZDMEG Method workflow checkpoint system for AI-assisted development. Enforce quality gates at three phases: pre-code, post-code, and pre-PR. Use when: (1) starting a new feature or bug fix, (2) finishing AI-generated code before review, (3) preparing a pull request, (4) running a planning interview, (5) auditing automation readiness, (6) preventing AI slop, (7) session bootstrap, (8) source rank, (9) domain gates, (10) bugbook. Triggers: 'bazdmeg', 'pre-code checklist', 'post-code checklist', 'pre-PR checklist', 'planning interview', 'quality gates', 'session bootstrap', 'source rank', 'domain gates', 'bugbook'.
Pixel-perfect Figma to React conversion using coderio. Generates production-ready code (TypeScript, Vite, TailwindCSS V4) with high visual fidelity. Features robust error handling, checkpoint recovery, and streamlined execution via helper script.
This skill implements a specific task from a project's ROADMAP.md file. It should be used when the user wants to work on a roadmap action item by its ID (e.g., '1.1', '2.3'). Triggered by requests like '/do-task 1.1', '/do-task 2.3', or 'do task 3.1'. Works alongside the project-init skill (which creates the roadmap) and the checkpoint skill (which commits afterward).
Build a pre-implementation harness for ambiguous or risky coding tasks by grounding the request in the repository, producing a structured impact map, surfacing ambiguities and risks, defining scope boundaries, and creating a validation-ready implementation contract before any code changes are made. Use when a task is broad, underspecified, cross-cutting, or likely to drift without an explicit planning checkpoint.