Loading...
Loading...
Found 540 Skills
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
Use when need systematic innovation through comprehensive solution space exploration, resolving technical contradictions (speed vs precision, strength vs weight, cost vs quality), generating novel product configurations, exploring all feasible design alternatives before prototyping, finding inventive solutions to engineering problems, identifying patent opportunities through parameter combinations, or when user mentions morphological analysis, Zwicky box, TRIZ, inventive principles, technical contradictions, systematic innovation, or design space exploration.
Conduct targeted code exploration on a repository, and document the process of "Asking Questions → Reading Code → Reaching Conclusions" as searchable evidence for direct reuse when similar questions arise next time. There are three types: question (investigate code around a specific problem and provide conclusions), module-overview (organize the structure, boundaries, entry points, and dependencies of a module), spike (conduct lightweight technical exploration of multiple possible directions without making final decisions). Trigger scenarios: When users say "Let's explore first", "How is X implemented in this repository", "Quickly get familiar with this module", "Archive the exploration results". For the distinction from learning / tricks / decisions, refer to the root skill `easysdd`.
Use these skills when you need to handle large-scale data exploration and dataset management. Use when users need to find data assets or run SQL at scale. Provides metadata discovery and query execution across the data warehouse.
Train custom AI models (LoRA) on fal.ai for personalized image generation tailored to a brand, character, or style.
Collaborative design exploration that refines ideas into validated specs through iterative questioning. Use before any creative work including creating features, building components, adding functionality, or modifying behavior.
Use when the task needs real browser automation, DOM exploration, browser session state, network capture, or browser-backed request replay with Opensteer. The default pattern is: explore with the CLI first, then write the final code with the SDK.
Create algorithmic art with seed-based randomness and interactive parameter exploration using p5.js. Use this skill when users request to create art with code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art instead of copying existing artists' works to avoid copyright infringement.
Data visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience.
Guides QA engineers through daily testing activities—morning review, test case creation, automation, exploratory testing, bug reporting, and end-of-day wrap-up. Use when planning or executing day-to-day testing or when the user asks about daily testing workflow.
Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
Explore-lane experimental execution skill for deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with results summarized in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, or implicit experimentation.