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Found 455 Skills
Documents the results of a time-boxed technical or design exploration (spike). Use after completing a spike to capture learnings, findings, and recommendations for the team.
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
LLM fine-tuning with LoRA, QLoRA, and instruction tuning for domain adaptation.
Comprehensive academic writing skill for drafting journal-ready manuscripts. Orchestrates specialized sub-skills for introduction sections (q-intro), descriptive analysis (q-descriptive-analysis), methods sections (q-methods), and results sections (q-results). Use when the user needs end-to-end support for academic manuscript preparation, from initial data exploration through publication-ready prose. Follows APA 7th edition formatting standards.
Use when querying Outlit customer data via MCP tools (outlit_*). Triggers on customer analytics, revenue metrics, activity timelines, cohort analysis, churn risk assessment, SQL queries against analytics data, or any Outlit data exploration task.
explore — Deep codebase exploration with parallel agents. Use when exploring a repo or discovering architecture.
Primary tool for all code navigation and reading in supported languages (Rust, Python, TypeScript, JavaScript, Go). Use instead of Read, Grep, and Glob for finding symbols, reading function implementations, tracing callers, discovering tests, and understanding execution paths. Provides tree-sitter-backed indexing that returns exact source code — full function bodies, call sites with line numbers, test locations — without loading entire files into context. Use for: finding functions by name or pattern, reading specific implementations, answering 'what calls X', 'where does this error come from', 'how does X work', tracing from entrypoint to outcome, and any codebase exploration. Use Read only for config files, markdown, and unsupported languages.
Bounded codebase exploration and architecture mapping. Use when discovery is needed before implementation. Do NOT use for broad refactoring — use do-plan instead.
Use Chanjing text-to-digital-person APIs for AI portraits, talking videos, optional LoRA training, polling, and explicit downloads when requested.
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.
Fine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.