arnesto

Run a prompt or skill in this session on a recurring or variable interval (e.g. /loop 5m /foo).

Use when the user wants to clean up AI slop, harden a codebase, or run technical due diligence: review code for security flaws and vulnerable or wrongly-licensed dependencies, find and remove dead code, audit dependency health, run and triage linters, type checkers, test suites and formatters, evaluate design principles (SOLID and the cohesion/coupling equivalents for non-OO stacks), audit logging and observability, find performance smells, check documentation and CI health, scaffold baseline configs for the detected stack, do a structured code review, guide a one-major-at-a-time dependency migration, fix project structure, or produce one aggregated health report across all of these. Triggers on phrases like clean this up, find security issues, remove dead code, check dependencies, lint, typecheck, run the tests, format, review my code, check the design, check logging, is this slow, migrate a dependency, fix the project structure, set up the project, audit everything, due diligence, or this looks like AI slop. Wraps the detected stack's real tooling — dependency auditor, linter, type checker, test runner, formatter, dead-code and license scanners — and triages its output. Not for UI/visual design work.

Prepare, submit with user authorization, and check municipal reports at avisos.madrid.es. Use for Avisos Madrid, reporting street defects, cleaning, lighting or urban equipment in Madrid city, and requests for new street elements. Not for other municipalities, emergencies, or general administrative appeals.

Ensures proper Python dependency management, avoiding global `pip install` and adhering to project-specific tooling. Use this skill if any of the following are true: 1. Attempting to run `pip install {package_name}`. 2. Python packages or dependencies need to be added or modified. 3. Initiating a new Python project. 4. Creating a new notebook, even if just using BigQuery cells. 5. Generating Python code that includes `import` statements for third-party libraries. 6. Before executing Python scripts via the terminal to ensure the correct virtual environment is active.

Convert 'Applied intelligently' Cursor rules (.cursor/rules/*.mdc) and slash commands (.cursor/commands/*.md) to Agent Skills format (.cursor/skills/). Use when you want to migrate rules or commands to skills, convert .mdc rules to SKILL.md format, or consolidate commands into the skills directory.

CRITICAL RULE: You MUST use this skill whenever the task involves any machine learning tasks or data analysis. Use this skill if the user's prompt or requirements mention any of the following: * Clustering * Classification * Regression * Time series forecasting * Statistical testing * Model comparison * ML * Data analysis SQL/BigQuery ML HANDOFF: If the user requires a SQL solution, use this skill to dictate the ANALYSIS STEPS (e.g., markdown analysis cells, visualization logic), but defer to `bigquery` for all SQL syntax.

Principles for building Unix-composable command-line tools in Go: stdout/stderr discipline, JSON and plain output modes, semantic exit codes, noun-verb commands, structured errors. Use when designing, building, or reviewing CLIs.

Use when asked about "pricing strategy", "willingness to pay", "value metric", "packaging tiers", "good better best pricing", "subscription vs usage pricing", or "price before product". Helps design products customers will pay for and choose pricing models that capture value. Based on Madhavan Ramanujam's Monetizing Innovation framework from Simon-Kucher.

Finds weak or missing tests by analyzing if code changes would be caught. Use when verifying test effectiveness, strengthening test suites, or validating TDD workflows.

Create a Cursor-hosted repo for the current project and push it. Use when a project has no git remote yet and the user asks to create a repo on Cursor, push the project to Cursor, or set up a Cursor-hosted remote; installs and signs in the origin CLI when needed.

This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.

Writes tests without mocks using Nullables. Use when writing tests, especially testing code with external I/O (HTTP, files, databases, clocks, random numbers), designing infrastructure wrappers or replacing mocking libraries.

Interact with Obsidian vaults using the Obsidian CLI to read, create, search, and manage notes, tasks, properties, and more. Also supports plugin and theme development with commands to reload plugins, run JavaScript, capture errors, take screenshots, and inspect the DOM. Use when the user asks to interact with their Obsidian vault, manage notes, search vault content, perform vault operations from the command line, or develop and debug Obsidian plugins and themes.

Use when asked to "set OKRs", "objectives and key results", "quarterly OKR planning", "align objectives", "measure OKR progress", or "focus priorities with OKRs". Helps teams focus on what matters most and create a cadence of progress. The OKR framework (originated by Andy Grove at Intel, popularized by John Doerr at Google) creates alignment, focus, and learning cycles. Christina Wodtke's Radical Focus approach emphasizes simplicity and avoiding common pitfalls.

Use /onboard for a focused Cursor onboarding flow that learns basic preferences, picks a first goal, and routes the user to the right next action.

Spec-driven development with OpenSpec. Use when the user mentions OpenSpec, OPSX, spec-driven development, SDD, change proposals, delta specs, /opsx commands, or wants structured planning for features, migrations, refactors, or bug fixes.

Use when asked to "opportunity solution tree", "OST", "Teresa Torres", "map customer opportunities to outcomes", "structure discovery around opportunities", or "compare solutions for a customer need". Helps product teams connect outcomes to customer opportunities and test solutions with Opportunity Solution Trees (created by Teresa Torres).

Install, sign in, update, or repair the origin CLI for repos hosted on Cursor (origin.cursor.com). Use when the CLI is missing or outdated, git push/pull fails to authenticate against origin.cursor.com, or the user asks to set up, clone, or configure Cursor-hosted repos.

Pragmatic property-based testing review and first-wave adoption for this listings-webapp monorepo (Jest + fast-check, unit tests only).

Route durable personal context and reusable knowledge between Arnesto and the personal knowledge vault. Use when the user asks to remember, persist, capture, retrieve, or organize personal knowledge; when deciding whether information belongs in rules, skills, portfolio, notes, maps, or inbox; or when using personal-knowledge-vault as a low-context reference layer.