machine-learning
38
model-hyperparameter-tuning
Optimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt
aj-geddes
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
38
recommendation-engine
Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches
aj-geddes
data-ai
machine-learning
38
model-deployment
Deploy machine learning models to production using Flask, FastAPI, Docker, cloud platforms (AWS, GCP, Azure), and model serving frameworks
aj-geddes
data-ai
machine-learning
38
feature-engineering
Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability
aj-geddes
data-ai
llm-ai
37
weaviate-query-agent
Search and retrieve data from local Weaviate using semantic search, filters, RAG, and hybrid queries
saskinosie
data-ai
llm-ai
36
memory-optimizer
Refactors CLAUDE.md into minimal startup context by extracting path-specific rules, skills, commands, and agents. Use when CLAUDE.md exceeds 50 lines, startup feels slow, memory needs restructuring, or splitting monolithic project instructions.
nwiizo
data-ai
llm-ai
35
external-consensus
Synthesize consensus implementation plan from multi-agent debate reports using external AI review
Synthesys-Lab
data-ai
llm-ai
35
pact-memory
Persistent memory for PACT agents. Save context, goals, lessons learned, decisions, and entities. Semantic search across sessions. Use when: saving session context, recalling past decisions, searching lessons. Triggers: memory, save memory, search memory, lessons learned, remember, recall
ProfSynapse
data-ai
llm-ai
34
wispr-flow
Analyze Wispr Flow voice dictation data. Stats, search, export, visualizations. Use when user says "dictation history", "word counts", "voice analytics", "how much did I dictate", "search my dictation".
ArtemXTech
data-ai
data-engineering
34
analyze-bigquery-usage
Comprehensive analysis of BigQuery usage patterns, costs, and query performance
openshift-eng
data-ai
data-engineering
34
bigquery
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
lanej
data-ai
llm-ai
34
jimeng-mcp-skill
使用jimeng-mcp-server进行AI图像和视频生成。当用户请求从文本生成图像、合成多张图片、从文本描述创建视频或为静态图像添加动画时使用此技能。支持四大核心能力：文生图、图像合成、文生视频、图生视频。需要jimeng-mcp-server在本地运行或通过SSE/HTTP访问。
wwwzhouhui
data-ai
llm-ai
34
self-learning-skills
Memory sidecar for agent work: recall before tasks, record learnings after tasks, review recommendations, optional backport bundles.
scottfalconer
data-ai
llm-ai
34
siliconflow-api-skills
硅基流动（SiliconFlow）云服务平台文档。用于大语言模型 API 调用、图片生成、向量模型、在 Claude Code 中使用硅基流动、Chat Completions API、Stream 模式等。
wwwzhouhui
data-ai
llm-ai
33
anthropic-expert
Expert on Anthropic Claude API, models, prompt engineering, function calling, vision, and best practices. Triggers on anthropic, claude, api, prompt, function calling, vision, messages api, embeddings
raintree-technology
data-ai
llm-ai
33
ralph
Autonomous feature development - setup and execution. Triggers on: ralph, set up ralph, run ralph, run the loop, implement tasks. Two phases: (1) Setup - chat through feature, create tasks with dependencies (2) Loop - pick ready tasks, implement, commit, repeat until done.
ampcode
data-ai
llm-ai
32
pine-visualizer
Breaks down trading ideas into component parts for systematic Pine Script implementation. Use when analyzing trading concepts, decomposing strategies, planning indicator features, or extracting ideas from YouTube videos. Triggers on conceptual questions, "how would I build", YouTube URLs, or video analysis requests.
TradersPost
data-ai
llm-ai
32
shorts-script-personality
Generates hyper-optimized YouTube Shorts/Instagram Reels scripts with personality-specific styles while enforcing strict anti-AI-slop writing rules
outscal
data-ai
llm-ai
32
script-writer-personality
Generates educational video scripts with personality-specific styles (GMTK, Fireship, Chilli) while enforcing strict anti-AI-slop writing rules
outscal
data-ai
llm-ai
32
prompt-engineering
LLM prompt optimization and design patterns. Use for crafting effective prompts, chain-of-thought, and AI integration.
lovedragonball
data-ai
data-analysis
31
count-dataset-tokens
This skill provides guidance for counting tokens in datasets using specific tokenizers. It should be used when tasks involve tokenizing dataset content, filtering data by domain or category, and aggregating token counts. Common triggers include requests to count tokens in HuggingFace datasets, filter datasets by specific fields, or use particular tokenizers (e.g., Qwen, DeepSeek, GPT).
letta-ai
data-ai