agentmods.dev

ralplan

ralplan is a cursor rule for coding agents from Jkudjo/oh-my-cursor. It costs 9 tokens per session (333 once invoked), scanned A, original, MIT.

An approval step for creating a detailed coding plan before implementation begins.

In plain words
What is it for?
Use it after requirements are clarified when a change needs an agreed implementation plan. TDD is not involved here; the procedure explicitly forbids writing implementation code during this phase.
Why use it?
It makes the proposed approach, files, tradeoffs, risks, and exclusions visible so the plan can be corrected before code is written.

Cursor rule

not rated 1 5mo ago A scan 9 tokens original MIT
View the source file Jkudjo/oh-my-cursor
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/jkudjo/oh-my-cursor/ralplan
Clone the repo
git clone --depth 1 https://github.com/Jkudjo/oh-my-cursor

Wrote 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.

agentmods badge for ralplan

README.md
[![agentmods](https://agentmods.dev/badge/rules/jkudjo/oh-my-cursor/ralplan.svg)](https://agentmods.dev/rules/jkudjo/oh-my-cursor/ralplan)
Your own site
<a href="https://agentmods.dev/rules/jkudjo/oh-my-cursor/ralplan"><img src="https://agentmods.dev/badge/rules/jkudjo/oh-my-cursor/ralplan.svg" alt="Measured on agentmods" height="20"></a>
Per session 9 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 333 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00009 $0.00333
Opus 5 $0.00005 $0.00167
Sonnet 5 $0.00002 $0.00067
Haiku 4.5 $0.00001 $0.00033

Measured 3d ago against content hash 33eb33b5df1a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ralplan 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 3d 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.

rules/ralplan.mdc · 50 lines

What it actually says

@ralplan

Trigger: After @deep-interview or when you have a clear task and need an approved plan.

Protocol

  1. Call start_mode with mode=ralplan.
  2. Load clarified scope: call get_memory with key=clarified-scope (if available).
  3. Generate a detailed implementation plan in this format: 
    ## Implementation Plan

### Approach
[1-paragraph summary of the approach and why]

### Steps
1. [ ] Step one
2. [ ] Step two
...

### Files to change
- `path/to/file.ts` — what changes and why
...

### Tradeoffs
- Option A vs Option B: chose A because...

### Risks
- Risk: ... Mitigation: ...

### Out of scope
- ...
  4. Present the plan and ask: "Approve this plan? (yes / revise: )"
  5. If revised: update the plan and re-present. Repeat until approved.
  6. On approval: call save_plan with title and the full plan markdown, approved=true.
  7. Call update_mode_state with status=completed.
  8. Recommend: "Run @ralph to execute the plan."

Rules

  • Do NOT write any implementation code during this phase.
  • Do save the approved plan via save_plan — @ralph will load it.
Changes

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.

  1. 3d ago First seen · 50 lines · 9 tokens per session scan A 33eb33b5df1a

Subscribe to this mod's changes

ralplan is a cursor rule published in the GitHub repository Jkudjo/oh-my-cursor (1 stars, last pushed 5mo ago), licensed MIT. It adds 9 tokens to every session and 333 once invoked, about $0.0000 per session on Opus 5. 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 2026-08-31.

Planningagentai-workflowcodexcursorcursor-aicursor-rules
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