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Found 278 Skills
A fast, extensible progress bar for Python and CLI. Instantly makes your loops show a smart progress meter with ETA, iterations per second, and customizable statistics. Minimal overhead. Use for monitoring long-running loops, simulations, data processing, ML training, file downloads, I/O operations, command-line tools, pandas operations, parallel tasks, and nested progress bars.
Process Excel files, supporting reading, analysis, statistics and export of xlsx data
Quantifies market breadth health using TraderMonty's public CSV data. Generates a 0-100 composite score across 6 components (100 = healthy). No API key required. Use when user asks about market breadth, participation rate, advance-decline health, whether the rally is broad-based, or general market health assessment.
Create a custom technical indicator using Numba JIT + NumPy. Generates production-grade, O(n) optimized indicator functions with charting and benchmarking.
Use this skill for ANY question about creating test or evaluation datasets for LangChain agents. Covers generating datasets from traces (final_response, single_step, trajectory, RAG types), uploading to LangSmith, and managing evaluation data.
Optimize Daft UDF performance. Invoke when user needs GPU inference, encounters slow UDFs, or asks about async/batch processing.
Automatic generation system for A-share daily briefings. It crawls real-time data from East Money and generates daily reports covering complete information such as market indices, popular sectors, and capital trends.
Run SQL queries against the attached DuckDB database or ad-hoc against files. Accepts raw SQL or natural language questions. Uses DuckDB Friendly SQL idioms.
Enrich.so platform help — real-time B2B data enrichment API with 50+ data points per lookup and success-based billing. Key capabilities: reverse email lookup, LinkedIn profile enrichment, email/phone finders, company data, similar company search, employee search, IP to company, and bulk enrichment. Use when enriching contacts by email or LinkedIn URL, finding emails/phones from name+company, looking up company data, running bulk enrichment, or working with the Enrich.so API. Do NOT use for cross-platform enrichment strategy (use /sales-enrich), email deliverability strategy (use /sales-deliverability), or prospect list strategy (use /sales-prospect-list).
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
Extract quantities from BIM/CAD data for cost estimation. Group by type, level, zone. Generate QTO reports.
Collect validated Xiaohongshu image assets from normalized XHS datasets into local manifests and downloaded files. Use this when you need reproducible local media artifacts from note covers or other already-exposed remote asset URLs.