meleantonio

Simplify and clean up code after changes are complete. Reduces complexity, improves readability, and ensures consistency.

Commit changes, push to remote, and create a pull request. Use for completing features or fixes ready for review.

Implements the Spec-Driven Development lifecycle (Intent, Requirements, Design, Tasks, Build) for structured feature development. Use when the user wants to scaffold a new feature spec, generate EARS requirements, create a technical design, break work into tasks, or check spec status. Trigger on keywords: sdd, spec-driven, ears requirements, feature spec.

Find and fix technical debt including duplicated code, dead code, outdated patterns, and code smells. Run at the end of sessions to clean up.

Run IV, DiD, and RDD analyses in R with proper diagnostics

Build and solve Walrasian general equilibrium models with theory derivations and Julia computation

Panel data analysis with Python using linearmodels and pandas.

Run regression analyses in Stata with publication-ready output tables.

Finds and synthesizes recent economics literature for a research question, prioritizing top economics journals and strong working-paper venues, resolving paywalled links through IDEAS/RePEc or author webpages, and writing a markdown review. Use when the user asks for a literature search, literature review, related-work scan, paper shortlist, bibliography, or economics article review.

Coordinates multi-agent research work through GitHub issues, branches, and PRs with explicit review and verifier gates. Use when running a swarm, parallel issues, orchestration log, feat/issue-N branches, merge order, consistency pass before merge, or Day 3-style lab workflow.

Exports approved spec/tasks.md entries into GitHub issue drafts with labels, branch names, and acceptance criteria. Use when syncing SDD tasks to issues, creating a swarm milestone from spec, or generating labeled issue bodies for feat/issue-N branches.

Guides applied microeconometrics workflows for estimands, identification, specification, inference, and robustness. Use when working on DiD, IV, RD, panel regressions, treatment effects, or empirical design questions.

Structures game-theory analysis across normal-form and extensive-form settings. Use when defining players, actions, information, equilibrium concepts, or comparative statics in strategic interaction problems.

Guides HANK modeling workflows including calibration review, equilibrium checks, and transition diagnostics. Use when working on heterogeneous-agent macro models, household distributions, policy experiments, or transition-path questions.

Supports replication, result handoff, and canonical research pipelines. Use when organizing empirical or quantitative research code, preparing tables and figures, or making work rerunnable by someone else.

Implements the Spec-Driven Development lifecycle (Intent, Requirements, Design, Tasks, Build) for structured feature development. Use when the user wants to scaffold a new feature spec, generate EARS requirements, create a technical design, break work into tasks, or check spec status. Trigger on keywords: sdd, spec-driven, ears requirements, feature spec.

Simplify and clean up code after changes are complete. Reduces complexity, improves readability, and ensures consistency.

Commit changes, push to remote, and create a pull request. Use for completing features or fixes ready for review.

Find and fix technical debt including duplicated code, dead code, outdated patterns, and code smells. Run at the end of sessions to clean up.

Python code style and formatting standards using Ruff. Use when writing or reviewing Python code.

Git workflow and commit conventions. Use when committing code, creating branches, or making pull requests.

LLM and ML development best practices with LangChain and transformers. Use when building AI/ML applications.

Testing conventions using pytest. Use when writing tests, creating fixtures, or running test suites.

Apply Gary Becker-style economic reasoning to human behavior: incentives, opportunity cost, human capital, household production, family decisions, crime and deterrence, discrimination, education, labor training, time allocation, organ markets, policy design, and theory-data tradeoffs. Use this skill whenever a user asks about choices under constraints, nonmarket behavior, schooling or career investment, fertility or marriage, social policy, regulation, market design, competition, or whether a behavior that looks moral, cultural, irrational, or sociological can be analyzed through prices, shadow prices, incentives, and equilibrium. Gary Becker, economist, Nobel laureate, University of Chicago.

Apply Kevin Murphy's Chicago price-theory style of reasoning, associated with empirical economics at the University of Chicago, to practical economic analysis. Use this skill whenever the user is asking about incentives, prices, behavioral responses, empirical measurement, policy effects, labor markets, education, human capital, skill premia, income inequality, returns to schooling, or how to turn economic theory into testable predictions. Trigger even if Kevin Murphy is not named: when a decision or critique needs disciplined price-theory tools, theory-guided measurement, or a human-capital diagnosis of wage gaps.

Apply Luis Garicano’s economic reasoning to advice, decisions, and critiques, especially for Luis Garicano, economist at LSE School of Public Policy. Use this skill whenever the user is dealing with AI and jobs, task automation, junior training, organizational design, knowledge hierarchies, European growth, single-market scale, startup ecosystems, regulation, climate-policy trade-offs, fiscal restraint, eurozone architecture, banking union, institutional credibility, public-sector competence, monetary sovereignty, or policy implementation. Trigger even when Garicano is not named: the distinctive move is to combine evidence, incentives, bottlenecks, cost-benefit discipline, and implementable reform.

Use this skill to reason in the style of Thomas Sargent, economist, Nobel laureate, Stanford University, quantecon, whenever the task involves macroeconomic policy, inflation, monetary-fiscal coordination, expectations, credibility, dynamic stochastic models, econometrics, policy evaluation, the Lucas critique, government budget constraints, robustness to model misspecification, learning, or quantitative decision-making under uncertainty. Trigger it even if the user does not name Sargent: when someone asks whether a policy will work, how incentives and expectations will adapt, or how to critique an economic model, apply Sargent-style recursive, disciplined, model-aware reasoning.

Lists events from macOS Calendar.app within a time range using AppleScript. Use when checking availability, summarizing the day, or before scheduling messages or tasks on a self-hosted macOS agent.

Reads recent iMessage history from the local Messages database and sends iMessages via AppleScript. Use when the user asks to text someone, check conversation context before replying, or send an iMessage from a self-hosted Cursor agent on macOS.

Creates, lists, and completes items in macOS Reminders.app via AppleScript. Use when capturing todos, listing open tasks, or marking reminders done from a self-hosted macOS agent.

Summarizes macOS battery, disk usage, VPN/network service names, hostname, and top CPU processes. Use when the user asks about machine health, connectivity, or whether a laptop is on battery before long agent tasks.

Drafts highly effective cold emails based on the best practices (short, clear ask, no signature, highly personalized, no AI vibes).