Plugins
9 plugins@phuryn
Execution
Execution and product management skills: PRDs, OKRs, roadmaps, sprints, pre-mortems, stakeholder maps, user stories, prioritization frameworks, and more.
16 skills · plugin
curated
Options Trade Idea to Execution
From market sentiment to trade idea, score contracts, simulate P&L, and get execution-ready.
4 skills · plugin
curated
Task Execution Workflow
Load a plan, execute tasks with verification, and track progress via issues.
10 skills · plugin
curated
MCP Toolkit Automation
For users automating third-party services via MCP toolkits with dynamic discovery and execution.
10 skills · plugin
@dotnet
Dotnet Test
Skills for running, generating, analyzing, and improving .NET tests: test execution, filtering, platform detection, coverage, testability, and MSTest workflows.
20 skills · plugin
curated
Publish Interactive Plotly Dashboard
For analysts who need to turn tabular data into an interactive Plotly dashboard with statistical context and safe execution.
3 skills · plugin
@fradser
Pi
Bridges to pi (dev/pi), a minimal terminal coding harness. Delegates coding tasks to the pi CLI for execution with full file and git context.
3 skills · plugin
curated
Product Idea Validation
Install this pack to transform a raw product idea into clarifying questions, deep research, a PRD, and a phased execution plan with kill criteria.
6 skills · plugin
@testdouble
Han Coding
Code-writing and execution skills for the Han suite. Home of the tdd skill, which drives a feature or behavior through a BDD-framed red-green-refactor loop with an enforced observed-failure gate. Depends on han-core and han-communication; bundled by the han meta-plugin.
11 skills · plugin
Results for “execution”
80 skills
arjumaan Polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
1
26bb Polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
0
sickn33 Polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
45.1k
mit-network Polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
2
alterlab-ieu Alterlab Polars
Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.
60 · bundle
schattenspiegel Excel Python
Use for writing, reviewing, debugging, or testing Python code that inspects, edits, extracts, validates, preserves, or generates Excel .xlsx or .xlsm workbooks. Trigger on workbook contracts, formulas and cached values, Excel Tables, defined names, OOXML parts, types and precision, macros, charts, hidden sheets, external links, and semantic workbook verification. Do not use for CSV-only work, dataframe computation with no workbook boundary, Excel UI automation, recalculation, connection refresh, or macro execution.
0 · bundle
alterlab-ieu Alterlab Dask
Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.
60 · bundle
claude-dev-suite Tabular RAG
Structured data + RAG. NL2SQL hybrid patterns (text-to-SQL then execute vs embed rows), table embedding strategies (row-level, schema-level, hybrid), semantic layer integration (Cube, dbt metrics), LangChain SQLDatabaseChain, LlamaIndex PandasQueryEngine, safe SQL execution (read-only, sandboxed), schema-aware retrieval. Full PostgreSQL + pgvector hybrid code. USE WHEN: user mentions "tabular RAG", "NL2SQL", "text to SQL", "RAG on tables", "database RAG", "SQL RAG", "semantic layer", "structured data RAG" DO NOT USE FOR: unstructured doc RAG - use `rag-architecture`; metadata filtering only - use `self-querying-retriever`; KG retrieval - use `graph-rag`
28