data-engineering
946
parallel-processing
Parallel processing with joblib for grid search and batch computations. Use when speeding up computationally intensive tasks across multiple CPU cores.
benchflow-ai
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
data-engineering
946
workload-balancing
Optimize workload distribution across workers, processes, or nodes for efficient parallel execution. Use when asked to balance work distribution, improve parallel efficiency, reduce stragglers, implement load balancing, or optimize task scheduling. Covers static/dynamic partitioning, work stealing, and adaptive load balancing strategies.
benchflow-ai
data-ai
data-engineering
946
data-cleaning
Clean messy tabular datasets with deduplication, missing value imputation, outlier handling, and text processing. Use when dealing with dirty data that has duplicates, nulls, or inconsistent formatting.
benchflow-ai
data-ai
machine-learning
946
pid-controller
Use this skill when implementing PID control loops for adaptive cruise control, vehicle speed regulation, throttle/brake management, or any feedback control system requiring proportional-integral-derivative control.
benchflow-ai
data-ai
machine-learning
946
vehicle-dynamics
Use this skill when simulating vehicle motion, calculating safe following distances, time-to-collision, speed/position updates, or implementing vehicle state machines for cruise control modes.
benchflow-ai
data-ai
machine-learning
946
egomotion-estimation
Estimate camera motion with optical flow + affine/homography, allow multi-label per frame.
benchflow-ai
data-ai
machine-learning
946
ac-branch-pi-model
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
benchflow-ai
data-ai
machine-learning
946
glm-basics
Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration parameters.
benchflow-ai
data-ai
machine-learning
946
tts-audio-mastering
Practical mastering steps for TTS audio: cleanup, loudness normalization, alignment, and delivery specs.
benchflow-ai
data-ai
machine-learning
946
senior-data-scientist
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
benchflow-ai
data-ai
machine-learning
946
feature-engineering
Engineer dataset features before ML or Causal Inference. Methods include encoding categorical variables, scaling numerics, creating interactions, and selecting relevant features.
benchflow-ai
data-ai
data-analysis
943
data-viz-plots
Publication-quality matplotlib/seaborn plots: scatter, heatmap, violin, bar, line, multi-panel figures. Works with ANY LLM provider.
Starlitnightly
data-ai
machine-learning
943
data-transform
Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
Starlitnightly
data-ai
machine-learning
943
single-popv-annotation
PopV population-level cell annotation: 10 algorithms (SCVI, SCANVI, CellTypist, OnClass, RF, SVM, XGBoost, BBKNN, HARMONY, SCANORAMA), consensus voting, pretrained hub models.
Starlitnightly
data-ai
data-analysis
934
performance-reporter
Generate SEO/GEO dashboards: rankings, traffic, backlinks, AI visibility for stakeholders. SEO报告/绩效仪表盘
aaron-he-zhu
data-ai
llm-ai
934
ai-model-wechat
Use this skill when developing WeChat Mini Programs (小程序, 企业微信小程序, wx.cloud-based apps) that need AI capabilities. Features text generation (generateText) and streaming (streamText) with callback support (onText, onEvent, onFinish) via wx.cloud.extend.AI. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended) and DeepSeek (deepseek-v3.2 recommended). API differs from JS/Node SDK - streamText requires data wrapper, generateText returns raw response. NOT for browser/Web apps (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation (not supported).
TencentCloudBase
data-ai
llm-ai
934
ai-model-nodejs
Use this skill when developing Node.js backend services or CloudBase cloud functions (Express/Koa/NestJS, serverless, backend APIs) that need AI capabilities. Features text generation (generateText), streaming (streamText), AND image generation (generateImage) via @cloudbase/node-sdk ≥3.16.0. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended), DeepSeek (deepseek-v3.2 recommended), and hunyuan-image for images. This is the ONLY SDK that supports image generation. NOT for browser/Web apps (use ai-model-web) or WeChat Mini Program (use ai-model-wechat).
TencentCloudBase
data-ai
llm-ai
934
ai-model-web
Use this skill when developing browser/Web applications (React/Vue/Angular, static websites, SPAs) that need AI capabilities. Features text generation (generateText) and streaming (streamText) via @cloudbase/js-sdk. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended) and DeepSeek (deepseek-v3.2 recommended). NOT for Node.js backend (use ai-model-nodejs), WeChat Mini Program (use ai-model-wechat), or image generation (Node SDK only).
TencentCloudBase
data-ai
data-analysis
923
agentsociety-synthesize
Synthesize experiment results into research insights and summaries.
tsinghua-fib-lab
data-ai
data-engineering
917
dataset-manager
Use this skill to generate benchmark datasets (TPC-H, TPC-DS, etc.). Trigger when the user needs test data at a specific scale factor for benchmarking or testing. Supports parquet and duckdb output formats.
sirius-db
data-ai
llm-ai
915
claudeception
Claudeception is a continuous learning system that extracts reusable knowledge from work sessions. Triggers: (1) /claudeception command to review session learnings, (2) "save this as a skill" or "extract a skill from this", (3) "what did we learn?", (4) After any task involving non-obvious debugging, workarounds, or trial-and-error discovery. Creates new Claude Code skills when valuable, reusable knowledge is identified.
blader
data-ai
data-engineering
913
moai-formats-data
Data format specialist covering TOON encoding, JSON/YAML optimization, serialization patterns, and data validation. Use when optimizing data for LLM transmission or implementing serialization.
modu-ai
data-ai
machine-learning
913
moai-workflow-thinking
Sequential Thinking MCP for structured step-by-step analysis via --deepthink flag. Separate from UltraThink which is Claude's native extended reasoning mode. Use for multi-step analysis or architecture decisions.
modu-ai
data-ai