Build Iterated Agentic Loop

Use this skill when the user wants to turn a repeatable agent task into a repo-local skill plus a GitHub Actions workflow that runs a coding agent on a schedule, manually, or both.

The target shape is an iterated agentic loop: a focused skill defines the agent's judgement, a workflow invokes a coding agent with a repo-specific prompt, an agent-memory file carries standing feedback between runs, and each workflow labels its PRs so only one open PR exists per loop. The narrow-react-prop-types skill is the concrete reference pattern.

Outputs

Create or update these files in the target repo:

• .claude/skills/<skill-name>/SKILL.md for the repo-local agent behavior.
• .github/workflows/agent-<task-name>.yml for the recurring coding-agent automation.
• .github/agent-memory/<task-name>.md for stable feedback and scope constraints.
• Optional references under .claude/skills/<skill-name>/references/ when the skill needs templates, examples, or long supporting material.

Workflow

1. Explore the target repo

Read before asking setup questions:

• Existing .github/workflows/*.yml and .github/actions/** to understand runner, checkout, dependency install, cache, and PR patterns.
• Package manager files such as package.json, bun.lock, pnpm-lock.yaml, yarn.lock, package-lock.json, pyproject.toml, go.mod, or Cargo.toml, or other package management-related files
• Existing validation scripts, especially typecheck, lint, test, quality, format, and package-scoped commands.
• Existing .claude/skills and .agents/skills to avoid duplicating conventions.

Completion criterion: you can name the repo's package manager, install command, likely validation commands, and existing workflow conventions.

2. Ask setup questions

Walk the user through these decisions. Recommend defaults from repo evidence instead of presenting a blank form.