ralph-plan

Overview of ralph-plan skill

What ralph-plan does

The ralph-plan skill is a planning helper for turning a rough engineering request into a structured ralph-loop command. Instead of solving the task directly, it guides an interactive conversation that produces a plan with clear sections for context, setup, execution tasks, testing, and a final completion signal.

Best fit for Requirements Planning

ralph-plan for Requirements Planning is best for people who already know they need a multi-step implementation or investigation, but do not yet have a clean execution brief. It is especially useful when the request is underspecified, spans multiple files, or needs explicit validation steps before work begins.

The real job-to-be-done

Most users do not need “more brainstorming.” They need a command structure an agent can actually execute with less ambiguity. The main value of the ralph-plan skill is that it converts vague goals into an actionable plan format with:

• background and operating context
• setup steps before coding
• concrete task lists
• testing and verification steps
• an explicit done condition

What makes ralph-plan different from a generic prompt

A normal prompt might ask an AI to “make a plan.” ralph-plan is narrower and more operational. It pushes planning into a fixed command shape, which is useful if your downstream workflow expects ralph-loop style instructions rather than freeform advice.

When this skill is a strong choice

Use ralph-plan when you need to:

• prepare an implementation plan before touching code
• clarify requirements through back-and-forth questions
• define verification steps early
• reduce agent guesswork on multi-step work

Important limitation to know before install

This skill is lightweight. The repository evidence shows only SKILL.md, with no helper scripts, references, or example assets. That means adoption is easy, but quality depends heavily on how well you answer its clarifying questions and how familiar you are with your codebase.

How to Use ralph-plan skill

Install context for ralph-plan

ralph-plan install is typically done through your skills-enabled Claude or agent setup, then invoked when you want planning help before execution. The repository does not publish a skill-specific install command inside SKILL.md, so use the install flow supported by your environment for GitHub-hosted skills.

If your setup supports direct add commands, the common pattern is:

npx skills add mastra-ai/mastra --skill ralph-plan

If not, add the skill from the repository path:

• repo: mastra-ai/mastra
• skill path: .claude/skills/ralph-plan

Read this file first

Start with:

• SKILL.md

That is the whole skill. There are no supporting README, rules/, resources/, or scripts to inspect, so your decision should be based on whether the planning structure matches your workflow.

What input ralph-plan needs

The ralph-plan usage pattern works best when you provide four things up front:

1. the outcome you want
2. the codebase area or system involved
3. hard constraints
4. how success will be tested

A weak starting input:

• “Help me plan a feature.”

A stronger starting input:

• “Help me create a ralph-loop plan to add CSV export to the reporting module in apps/web. The team prefers minimal schema changes, we need role-based access checks, and success means exports work for existing filtered views with test coverage.”

How to prompt the skill well

Because ralph-plan is conversational, your first message should make the planning target narrow enough that the skill can ask useful follow-up questions.

Use this prompt shape:

Use ralph-plan to help me build a ralph-loop command.

Goal: [what should be delivered]
Codebase area: [files, services, app, package, or unknown]
Constraints: [time, safety, architecture, permissions, compatibility]
Testing expectations: [unit, integration, manual checks, build commands]
My expertise level: [beginner, familiar, maintainer]

This improves output because the skill’s structure explicitly needs background, setup, tasks, and testing. If you omit those inputs, the plan will be generic.

How ralph-plan structures the final plan

The skill is designed around these sections:

• <background>
• <setup>
• <tasks>
• <testing>
• <promise>COMPLETE</promise>

That matters in practice: if your downstream tool or workflow expects a ralph-loop command, ralph-plan gives you a planning format that is closer to executable handoff than ordinary prose.

A practical workflow that works well

A high-signal workflow for ralph-plan guide use is:

1. State the business or engineering goal.
2. Name the code area, even if only approximately.
3. Let the skill ask clarification questions.
4. Answer with constraints, not just preferences.
5. Ask it to turn the discussion into one complete ralph-loop command.
6. Review the setup and testing sections before execution.
7. Tighten vague tasks into verifiable actions.

This workflow is better than asking for the final command immediately, because the skill is built for iterative clarification first.

What good setup details look like

The <setup> section should not be filler. Strong setup steps usually include:

• activating any relevant skills or tools
• inspecting current implementation state
• identifying files or packages to review
• checking assumptions before editing
• noting research needed for unfamiliar areas

If the setup section only says “explore the codebase,” ask for named folders, likely entry points, and specific questions to answer before implementation.

What good task lists look like

The best ralph-plan skill outputs produce tasks that are:

• ordered
• concrete
• limited in scope
• verifiable without interpretation

Weak:

• “Implement the feature.”

Strong:

• “Trace the current export flow in apps/web/src/reports and identify where filtered state is assembled.”
• “Add a CSV export action that reuses the existing filter payload.”
• “Enforce access checks using the same permission gate used by report download actions.”

How to get better testing steps

Users often under-specify testing, which weakens the plan. Tell ralph-plan what counts as done:

• exact build or test commands
• expected UI or API behavior
• compatibility constraints
• regression risks to check manually

Example:

• “Include pnpm test --filter web, a manual check for filtered exports, and a regression check that non-admin users cannot export protected reports.”

When to stop refining and use the plan

You are ready to use the generated command when:

• every task names a concrete action
• the code area is specific enough to start exploration
• testing steps would catch the most likely mistakes
• the plan reflects actual constraints, not idealized ones

If any of those are missing, ask one more refinement round before execution.

ralph-plan skill FAQ

Is ralph-plan useful if I already know the task?

Yes, if the work is multi-step or risky. ralph-plan is less about discovering ideas and more about packaging work into an execution-ready command with setup and validation.

Is ralph-plan beginner-friendly?

Moderately. The structure is clear, but the skill does not include extra examples, references, or codebase-specific guidance. Beginners will get better results if they can at least name the relevant app, package, or feature area.

How is ralph-plan different from asking Claude to make a plan?

The difference is consistency. ralph-plan imposes a specific command format for ralph-loop, which is useful when you want reusable planning output instead of a one-off explanation.

When is ralph-plan not the right tool?

Skip it if:

• you need direct implementation, not planning
• the task is tiny and can be done in one step
• you do not use ralph-loop style workflows
• you need repository-specific automation or templates the skill does not provide

Does ralph-plan include install automation or helper files?

No. Repository evidence shows a single SKILL.md file and no scripts, rules, or supporting resources. That keeps it simple, but means there is little embedded guidance beyond the planning conversation itself.

Can I use ralph-plan for non-code Requirements Planning?

Sometimes, but it is strongest when planning technical work that benefits from setup, tasks, and testing sections. Pure business requirements without an execution path will not gain as much.

How to Improve ralph-plan skill

Give ralph-plan sharper requirements

The fastest way to improve ralph-plan usage is to replace broad goals with constraints and success criteria. The skill performs better when it knows what must not change, what must be validated, and where the work likely lives.

Include codebase clues early

Even partial hints help:

• likely directories
• service names
• feature flags
• existing commands
• related bug IDs or PRs

This reduces generic setup steps and produces a more credible task list.

Ask for explicit assumptions

A common failure mode is a plan that quietly assumes architecture or ownership details. Ask:

• “List assumptions before the final command.”
• “Call out unknowns that need checking in setup.”
• “Separate confirmed facts from likely paths.”

That makes the resulting ralph-plan guide safer to execute.

Push vague tasks into verifiable actions

If a generated task could be interpreted in several ways, ask the skill to rewrite it with:

• named files or modules
• expected output
• validation method
• dependency order

This is the biggest practical upgrade for Requirements Planning quality.

Strengthen the testing section after first draft

Many first-pass plans underweight testing. After the first output, explicitly request:

• build commands
• automated test targets
• manual validation steps
• regression checks
• permission or compatibility checks

This usually improves execution quality more than adding extra task detail.

Use one refinement round for risk and rollback

For higher-stakes work, ask ralph-plan to add:

• key risks
• irreversible changes to avoid
• rollout or rollback considerations
• checks to run before merging

That turns a decent plan into a safer one without overcomplicating the command.

Know the core tradeoff

The strength of ralph-plan is structure, not deep repository intelligence. To improve results, you provide the repository context it lacks. If you do that well, the skill becomes a useful planning accelerator; if not, it will default to generic but tidy plans.