Coding standards, frameworks, refactoring, and code review (16.6K skills)
Design an OpenRig agent team around a project goal. Create team and agent specifications, role guidance, and collaboration rules, then validate the setup and diagnose problems with startup instructions or agent coordination.
Open terminal views for OpenRig agents in cmux, grouped by rig, pod, mission, slice, or saved view. Inspect connection health and view agent sessions with read-only access where required, while keeping the underlying sessions running.
Define reusable OpenRig agent setups, roles, and workflows in specification files. Review dependencies that prevent use on another host, distinguish specifications from bundles and extensions, and verify both file structure and actual execution.
Guide changes to OpenRig itself by locating relevant source files, choosing tests, assessing sensitive areas, and preparing pull requests. Use isolated test environments to check changes without disrupting the daemon running the current agent session.
Guide a development team from an assigned user outcome through implementation, testing, and handoff. It covers reproducing defects, checking behavior against requirements, involving design when needed, and reporting the exact changes, verification results, and remaining uncertainty.
Create reusable starting configurations that combine an agent’s role, startup context, and source history. The skill guides registry entries, checks whether their sources are current, and manages their progression from initial capture to recommendation or retirement.
Understand how OpenRig organizes a team of coding agents. This skill explains the main terms, shows how to identify an agent’s role and peers, and clarifies where skills and working context come from.
Manage OpenRig development work from setup to completion using shared specification, progress, and evidence files. Record what was actually checked and pass work to the next owner so handoffs and conversation compaction preserve the context.
Look up exact syntax, options, JSON output, defaults, and error meanings for a known OpenRig command. Use the reference to check command behavior and diagnose runtime differences against the installed help and running daemon.
Design and run bounded procedures in environments where current conditions are uncertain. The skill separates agent judgment from precise tool actions, verifies each effect, and records checkpoints so interrupted work can resume from observed evidence.
Design and maintain applications where OpenRig agents help run the system. Organize instructions, shared state, and artifact checks, then trace unexpected behavior to the code, skill, or schema that needs repair.
Set up OpenRig to run a team of coding agents, including Claude Code and Codex. Open the team interface, pass project goals to its operator, reconnect to agents, and follow assigned work through to its results.
Review assigned code changes against project conventions, architecture, and intended behavior. Verify findings with source code and tests, classify their severity, and record a merge readiness verdict, with an optional deeper process for independent reviews and resolving disagreements.
Investigate bugs, failed tests, and unexpected behavior through reproducible steps, evidence gathering, and comparison with working code. Test one hypothesis at a time, then implement a focused fix and verify it with tests.
Answer Python questions and write code using examples fetched from pythonsheets.com. Find patterns for syntax, asynchronous tasks, networking, databases, machine learning, and distributed computing, or use topic-based questions to prepare for Python interviews.
Write, review, and refactor Python code using concrete readability rules. Keep functions focused, reduce nested logic, clarify names and expressions, and apply Python features such as comprehensions, dataclasses, and pathlib while respecting existing codebase conventions.
Prepare Gentle AI changes for review by linking approved issues, following branch and commit naming rules, and drafting pull requests with observed test results. Check change size and required automated checks before claiming readiness.
Audit existing SKILL.md files for clear activation conditions, usable instructions, and defined outputs. Get prioritized findings and specific improvements, with changes applied only when requested and the author's intent preserved.
Split large code changes into linked pull requests, each with a focused scope. Organize their order, dependencies, and verification steps so reviewers can assess one unit of work at a time.
Split changes exceeding 400 lines into linked pull requests with clear boundaries and dependencies. Choose a chain structure, keep tests and documentation with each work unit, and document review order, verification plans, and justified size exceptions.
Prepare and manage GitHub bug reports and feature requests using repository evidence and required forms. Check for duplicates, identify missing information, and verify published issues or comments before reporting the result.
Coordinate temporary agents for code exploration, independent implementation tasks, verification, reviews, or complex debugging. Define self-contained assignments, run independent work in parallel, and check returned evidence before combining the results.
Prepare pull requests by checking approved issue links, change-type labels, and required automated checks. Draft the repository’s PR template using observed test results, while leaving commits, pushes, PR creation, and merging under human control.
Turn reusable workflows and project conventions into concise instructions for AI agents. Structure SKILL.md with required metadata, move supporting material into local files, and register project skills in AGENTS.md when applicable.