Borrowing it
Nothing to install: this file belongs to argile-ai/claude-ralph-mode. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/argile-ai/claude-ralph-mode/main/.claude/commands/ralph.mdgit clone --depth 1 https://github.com/argile-ai/claude-ralph-modeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/argile-ai/claude-ralph-mode/ralph)<a href="https://agentmods.dev/commands/argile-ai/claude-ralph-mode/ralph"><img src="https://agentmods.dev/badge/commands/argile-ai/claude-ralph-mode/ralph.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00764 |
| Opus 5 | $0.00000 | $0.00382 |
| Sonnet 5 | $0.00000 | $0.00153 |
| Haiku 4.5 | $0.00000 | $0.00076 |
Grade A, and why
ralph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph - Plan Generator
Generate a structured implementation plan from a feature description.
The Job
- Read the project configuration from
ralph.config.json - Analyze the user's feature request
- Ask 3-5 clarifying questions to understand scope
- Generate a detailed plan in
plan.md - Offer to validate or modify the plan
Step 1: Read Configuration
First, read ralph.config.json to understand:
- Project name and description
- Available repositories and their paths
- Quality checks for each repository
If ralph.config.json doesn't exist, ask the user to create one or offer to generate a default configuration.
Step 2: Clarifying Questions
Ask 3-5 essential questions to understand the feature. Format questions with lettered options:
1. What is the primary goal of this feature?
A) Add new functionality
B) Improve existing functionality
C) Fix a bug
D) Refactor/cleanup
2. Which parts of the codebase will be affected?
A) Backend only
B) Frontend only
C) Both backend and frontend
D) Other (specify)
3. Are there any external dependencies required?
A) No new dependencies
B) Yes (specify which)
Wait for the user to respond with format like "1A, 2C, 3A" before proceeding.
Step 3: Generate Plan
Based on the answers, generate plan.md with this structure:
# [Project Name] - [Feature Name]
[Brief description of the feature]
---
## Summary
[2-3 sentences explaining what this plan accomplishes]
---
## User Stories
### [Repository 1] Stories
#### US-001: [Title]
**Repository:** `repo-name`
**Description:** As a [role], I want [feature] so that [benefit].
**Acceptance Criteria:**
- [ ] Specific, verifiable criterion
- [ ] Another criterion
- [ ] Tests pass (pytest/npm test/etc.)
[Repeat for each story...]
---
## Technical Considerations
- [Architecture decisions]
- [Dependencies]
- [Breaking changes]
---
## Open Questions
- [Any unresolved questions]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 152 lines · 0 tokens per session scan A 5077f983bbcd
ralph is a command published in the GitHub repository argile-ai/claude-ralph-mode (11 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 764 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.