tangled-skills

Phase/task based workflow system with PLAN.md as single source of truth. Use when tackling projects that require structured iteration through Planning, Analysis, Design, Implementation, Testing, Deployment, Maintenance, etc phases with clear dependency graphs.

Manages Python packages, projects, scripts, tools, and Python versions with extreme speed. Use when the user mentions uv, python packages, pip replacement, virtual environments, venv, pyproject.toml, dependency management, python version switching, tool installation (uvx), script dependencies, lockfiles, workspace management, or any Python packaging task. Replaces pip, pip-tools, pipx, poetry, pyenv, twine, and virtualenv.

jq 1.8.2 — lightweight command-line JSON processor. Use when the user needs to parse, query, transform, or manipulate JSON data from the command line, process API responses, extract fields from JSON, convert between formats (JSON-to-CSV, JSON-to-XML), validate JSON, or work with any structured data in JSON format. Covers filters, builtins, regex, modules, streaming, and all jq 1.8.2 features.

Operate rqlite 10.2.4 — distributed SQLite database with Raft consensus. Use for deploying, configuring, querying, backing up, and managing rqlite clusters. Covers rqlited server flags, HTTP API (/db/execute, /db/query, /db/request), CLI (.status, .backup, .nodes), clustering (join, DNS, Consul, etcd, Kubernetes), TLS/mTLS auth, queued writes, CDC, consistency levels, and SQLite extensions. Use whenever the user mentions rqlite, distributed SQLite, Raft database, or needs a lightweight fault-tolerant relational store.

Query, transform, and convert YAML, JSON, XML, INI, TOML, HCL, CSV, TSV, Properties, Lua, Shell variables, and Base64 data using jq-like expressions. Use when the user mentions yq, YAML processing, JSON-to-YAML conversion, config file manipulation, Kubernetes YAML editing, or any structured data transformation task. Supports multi-document YAML, anchors/aliases, comments, and in-place updates.

Ruff 0.15.18 — extremely fast Python linter and code formatter written in Rust. Use this skill whenever the user mentions ruff, linting Python, formatting Python code, ruff check, ruff format, pyproject.toml lint config, Flake8 replacement, Black replacement, isort replacement, or any Python code quality task involving ruff configuration, rule selection, or CI integration. Covers `ruff check`, `ruff format`, `ruff rule`, `ruff config`, `ruff analyze graph`, and `ruff server` (LSP).

Run ty (Astral's Python type checker, v0.0.51) for type checking, rule diagnostics, and language server integration. Use when the user mentions ty type checking, Python type errors, ty check, ty rules, ty suppression, migrating from mypy or pyright to ty, configuring ty.toml or pyproject.toml type checking, or setting up a Python language server. ty is 10x-100x faster than mypy/Pyright and supports intersection types, redeclarations, and reachability-based analysis.

Lint, format, and configure Python code with Ruff (v0.4+). Use when the user mentions ruff, python linting, python formatting, code quality, replacing flake8/black/isort/pyupgrade, pyproject.toml ruff config, ruff check, ruff format, or needs help setting up Python linting and formatting. Also triggers for import sorting (isort replacement), docstring checks, auto-fixing lint violations, pre-commit hooks with ruff, CI/CD linting pipelines, and migrating from flake8 + black + isort to a single tool.

Use ty, the extremely fast Python type checker and language server by Astral (creators of uv and Ruff). Use this skill whenever the user mentions ty, Python type checking, mypy migration, pyright migration, type diagnostics, or needs to configure a Python type checker. Also triggers for questions about Python typing features like intersection types, gradual typing, or redeclarations. ty is 10x–100x faster than mypy/Pyright and supports full LSP (completions, hover, navigate, etc.).

Manage Python projects, scripts, tools, environments, and packages with uv (0.11.21) — the fast Python package manager. Use when working with pyproject.toml, virtual environments, pip alternatives, venv creation, dependency locking (uv.lock), running Python scripts with inline metadata, installing/running CLI tools via uvx, managing Python versions, building wheels/sdists, publishing to PyPI, workspace management, or migrating from pip/pip-tools/virtualenv. Covers uv run, uv add, uv sync, uv lock, uv build, uv publish, uv venv, uv pip, uv tool, uv python, uv check, uv upgrade, and all related workflows.

COIN-OR Cbc (Coin-or Branch and Cut) 2.10.13 — open-source MILP solver. Use this skill whenever the user needs to solve mixed-integer linear programs, integer programming, MIP, branch-and-cut, branch-and-bound, or any optimization problem with discrete/integer variables. Covers CLI usage (`cbc` executable), C API (`Cbc_C_Interface.h`), and C++ API (`CbcModel`, `OsiCbcSolverInterface`). Also covers cut generators, heuristics, custom branching, event handlers, callbacks, SOS constraints, solution pools, parallel solving, AMPL interface, and integration with modeling languages (PuLP, cvxpy, Pyomo, JuMP, MiniZinc). Trigger on: MILP, MIP solver, branch-and-cut, integer programming, Cbc solver, COIN-OR optimization, mixed-integer optimization.

DuckDB 1.5.3 — high-performance analytical OLAP database with embedded SQL engine. Use this skill whenever the user queries about DuckDB, needs to run SQL analytics on local files (CSV, Parquet, Excel/XLSX), wants to install or use extensions (excel, json, icu, parquet, httpfs, delta, iceberg, postgres_scanner, sqlite_scanner, fts, spatial, and others), needs data import/export workflows, or is comparing DuckDB against other analytical databases. Covers CLI usage, Python API, extension management, Excel file handling (read/write/formatting/metadata), and the full extension ecosystem available in v1.5.3.

Convert documents between formats using Pandoc 3.10. Use when the user mentions pandoc, document conversion, format transformation, or needs to convert between Markdown, HTML, LaTeX, PDF, Word (docx), OpenDocument (odt), PowerPoint (pptx), Excel (xlsx), CSV, TSV, EPUB, reStructuredText, Org mode, AsciiDoc, RTF, Textile, CommonMark, GFM, or any markup/format conversion task. Also use when user asks about pandoc filters, templates, defaults files, citeproc, or Lua filters.

Mermaid diagram syntax reference and validation. Use when writing, debugging, or converting Mermaid diagrams: flowchart, sequenceDiagram, stateDiagram, classDiagram, gantt, erDiagram, pie, gitgraph, journey, mindmap, timeline, xychart, radar-beta, quadrantChart, sankey, block, architecture-beta, c4, packet, treemap-beta, venn-beta, wardley-beta, ishikawa-beta, kanban, requirementDiagram.

Vega-Lite is a high-level grammar for interactive graphics — a concise JSON syntax for creating data visualizations. Use this skill whenever the user mentions Vega-Lite, chart specifications, JSON-based charts, declarative visualization, or wants to create bar charts, line charts, scatter plots, heatmaps, pie charts, area charts, boxplots, trellis/facet charts, layered compositions, geographic maps, or any data visualization using the Vega-Lite specification format (v6.4.3). Also use when the user asks about encoding channels (x, y, color, size, shape, theta, radius), mark types, transforms, aggregations, binning, time units, selections/interactions, or embedding Vega-Lite charts in web applications.

Static type checking for Python via basedpyright — a fork of pyright with stricter defaults, new diagnostic rules, baseline support, pylance features in open-source, and improved CI integration. Use when the user mentions basedpyright, pyright, type checking, static analysis, type stubs, pyrightconfig, or wants to configure/resolve Python type errors.

DuckDB Python client 1.5.4 API reference and usage patterns. Use when working with the `duckdb` Python package — in-process analytical SQL database. Covers connection management, relational API (lazy evaluation), data I/O (CSV/Parquet/JSON), Python UDFs, type system, pandas/PyArrow/Polars integration, fsspec filesystems, ADBC driver, profiling, and extensions. Trigger on: duckdb, DuckDBPyConnection, DuckDBPyRelation, read_parquet, from_df, create_function, fetch_arrow_table, register_filesystem.

Evaluate Excel formulas in Python without Excel. Use when the user needs to compute spreadsheet formulas, calculate xlsx files, convert formula-based spreadsheets to calculated values, export Excel to CSV/JSON, run batch scenarios, build JSON models from workbooks, or serve spreadsheets as a Flask API. Also triggers on mentions of formulas package, openpyxl calculation, or spreadsheet automation.

Matplotlib plotting library (v3.11). Use this skill whenever the user mentions plots, charts, graphs, figures, data visualization, matplotlib, pyplot, or needs to create any kind of visual output from Python data — line plots, scatter plots, bar charts, histograms, heatmaps, contour plots, subplots, legends, colormaps, saving figures, styling, animations, or interactive widgets. Covers both the pyplot (state-based) and object-oriented APIs.

Python graph library (NetworkX 3.6.1) for creating, manipulating, and analyzing complex networks. Use this skill whenever the user works with graphs, networks, nodes, edges, shortest paths, centrality, community detection, spanning trees, flow networks, DAGs, topological sort, graph generators, adjacency matrices, Laplacian spectra, isomorphism, bipartite matching, or any network science task. Triggers on: graph algorithms, network analysis, node/edge operations, Dijkstra, BFS, DFS, PageRank, Louvain communities, connected components, minimum spanning tree, max flow, transitive closure, and anything involving NetworkX or the `nx` module.

NumPy 2.4.6: array creation, manipulation, broadcasting, ufuncs, linear algebra, statistics, random sampling, structured arrays, and I/O. Use whenever working with numerical arrays, matrices, scientific computing, data analysis, or any task involving NumPy operations. Covers ndarrays, dtype system, einsum, stride tricks, masked arrays, FFT, polynomials, and the full NumPy 2.x API.

Pandas 3.0.3 data manipulation and analysis library for Python. Use when working with DataFrames, Series, data wrangling, CSV/Excel/Parquet I/O, groupby aggregations, merging/joining, time series, resampling, rolling windows, string operations, or any tabular data processing in Python. Covers pandas 3.0 semantics including dedicated string dtype by default, Copy-on-Write behavior, pd.col() expressions, Arrow PyCapsule interface, and anti-joins. Trigger on: DataFrame, Series, read_csv, merge, groupby, pivot_table, resample, rolling, time series analysis, ETL pipelines, data cleaning, or any mention of pandas dataframes.

PuLP 3.3.2 — Python LP/MILP modeler for linear, mixed-integer, and binary programming. Use this skill whenever the user mentions optimization, linear programming (LP), mixed-integer programming (MIP), MILP, integer programming, solver selection, transportation problems, assignment problems, blending, scheduling, cutting stock, Sudoku solving via MIP, column generation, stochastic programming, sensitivity analysis, or any problem that can be expressed as minimizing/maximizing a linear objective subject to linear constraints. Covers PuLP 3.3.2 API including `prob.add_variable()`, `prob.add_variable_dicts()`, `prob.add_variable_matrix()`, `lpSum`, `lpDot`, solver configuration (CBC, HiGHS, GLPK, Gurobi, CPLEX, etc.), file I/O (LP/MPS), dual values, resolve workflows, and common modeling patterns. Install via `pip install "pulp[cbc]==3.3.2"` (the `[cbc]` extra bundles the CBC solver).

Pyomo 6.10.1 — Python optimization modeling for LP, MIP, NLP, MINLP, DAE/optimal control, GDP (disjunctive programming), MPEC (equilibrium/complementarity), piecewise functions, network flow, stochastic programming, and more. Use this skill whenever the user mentions Pyomo, optimization modeling, mathematical programming, linear/nonlinear/mixed-integer programming, optimal control, differential equations optimization, disjunctive constraints, complementarity conditions, solver interfaces (Gurobi, CPLEX, IPOPT, GLPK, CBC, etc.), AMPL-style modeling in Python, or building optimization models with Python. Also use when the user asks about formulating LP/MIP/NLP models, network flow problems, facility location, lot sizing, transportation problems, diet/nutrition problems, parameter estimation, reactor design, job shop scheduling, or any mathematical optimization task in Python.

Write, run, and debug Python tests with pytest 9.1.0. Use when the user mentions pytest, writing tests, test fixtures, parametrization, test discovery, conftest.py, pytest plugins, test marks, skip/xfail, monkeypatch, tmp_path, capsys, caplog, assertion rewriting, or any Python testing task.

Test async/await code with pytest using event loops, async fixtures, and loop scopes. Use when writing async tests, configuring asyncio mode (auto/strict), managing event loop scopes, using @pytest.mark.asyncio, @pytest_asyncio.fixture, custom loop factories via pytest_asyncio_loop_factories hook, port fixtures (unused_tcp_port, unused_udp_port), testing with uvloop or other custom event loops, integrating Hypothesis with async tests, or migrating from older pytest-asyncio versions. Covers pytest-asyncio 1.4.0+ and Python 3.10+.

Python HTTP library (requests) version 2.34.2 — sends HTTP/1.1 requests via the high-level API (requests.get, requests.post, etc.), Session objects for cookie/auth persistence and connection pooling, PreparedRequest for low-level control, streaming responses, file uploads, authentication (Basic/Digest), proxies, TLS verification, retries, and hooks. Use this skill whenever the user works with Python HTTP clients, needs to call REST APIs, upload files, handle sessions or cookies, configure timeouts or retries, inspect response headers/status codes, or debug HTTP requests in Python, even if they don't name "requests" explicitly.

Comprehensive guide to scikit-learn 1.9.0 — the Python machine learning library. Use when working with ML models, pipelines, preprocessing, model selection, metrics, or any data science task using scikit-learn. Covers classification, regression, clustering, dimensionality reduction, ensemble methods, hyperparameter tuning, cross-validation, feature engineering, and more. Trigger on: sklearn, scikit-learn, machine learning, ML pipeline, model training, cross-validation, GridSearchCV, Random Forest, SVM, logistic regression, PCA, KMeans, train_test_split, metrics.

SciPy (scientific Python) library reference for mathematics, science, and engineering. Covers optimization, integration, linear algebra, statistics, signal processing, FFT, interpolation, sparse matrices, spatial algorithms, special functions, image processing, clustering, I/O, and physical constants. Use when the user needs scientific computing in Python, numerical methods, data analysis with scipy, solving equations, statistical tests, Fourier transforms, ODE systems, matrix operations, or any math-heavy computation. Also triggers on mentions of scipy, SciPy, scientific Python, numerical Python, or packages like numpy/scipy together.

SQLAlchemy 2.0 ORM and Core toolkit for Python database access. Use this skill whenever the user mentions SQLAlchemy, ORM models, database queries, engine creation, session management, declarative mappings, relationships (one-to-many, many-to-many), connection pooling, async database access, SQL expression construction, or any Python database abstraction task. Covers both Core (expression language) and ORM layers. Supports PostgreSQL, MySQL/MariaDB, SQLite, Oracle, Microsoft SQL Server, and third-party dialects (CockroachDB, IBM DB2, Firebird, SAP HANA, etc.).

Symbolic mathematics with SymPy 1.14.0 — algebra, calculus, ODE/PDE solving, matrices, number theory, geometry, special functions, transforms, and code generation. Use when the user needs symbolic computation, equation solving, differentiation, integration, series expansion, matrix operations, polynomial manipulation, simplification, or any CAS (computer algebra system) task in Python. Also use for exact arithmetic with rationals, symbolic constants (pi, E), or converting expressions to LaTeX/C/Fortran.

Scaffold, validate, and inspect agent skills (SKILL.md files). Use when creating new skills, checking skill format compliance, or reviewing skill structure.

Embed interactive Vega and Vega-Lite visualizations into web pages. Load specs from URLs or JSON objects, render with SVG or Canvas, add export/source actions, apply themes, tooltips, patches, and custom renderers. Use when embedding interactive data visualizations in browser applications via CDN or bundlers.

Searches the web via DuckDuckGo and returns results as markdown, CSV, or JSON. Use this skill whenever the user wants to search the web, look up information online, find URLs, do web search, research a topic, or needs current/web-based answers.

Fetches web pages as markdown or HTML for LLM consumption. Use when the user wants to fetch, scrape, download, retrieve, grab, pull, or access any URL or webpage content. Supports uvx, pipx, curl, wget, and python3 fallbacks. Always impersonates Safari to avoid blocks. Use this whenever the user asks to read a website, get page content, or fetch a URL.

Git version control. Use when the user mentions git, commits, branches, pushing, pulling, merging, rebasing, stashing, worktrees, submodules, or any version control task. Covers straightforward workflows (add/commit/push) and advanced topics.

Lightweight token-pruning communication mode that drops filler and hedging while keeping full sentences and professional tone. Follows guidelines for code quality. Default intensity is lite. Use when user requests tzip, prune tokens, be concise, or needs efficient communication without losing clarity.

Add-commit-push all changes in one flow. Use when the user says acp, add commit push, stage and push, or wants to quickly commit and push all working directory changes.

Open standard for communication between independent AI agent systems. Agents discover capabilities via Agent Cards and collaborate on tasks with streaming without exposing internal state. Supports JSON-RPC 2.0, gRPC, and HTTP+JSON bindings with enterprise security. Use when building multi-agent systems requiring agent discovery, task collaboration, or secure inter-agent communication.

Protocol standardizing communication between code editors and AI coding agents using JSON-RPC 2.0 over stdio. Decouples agents from editors for cross-compatibility. Use when building AI coding agents needing editor interoperability, implementing persistent sessions with tool calls, connecting agents via MCP delegation, or working with the official SDKs.