ralplan

ralplan is a skill for Claude Code, Codex from mrzhangguoguo/oh-my-workbuddy. It costs 64 tokens per session (1,460 once invoked), scanned A, original, MIT.

A shortcut for consensus planning: several planning roles repeatedly review a proposed implementation plan until they agree. It can also ask you to review decisions and add extra checks for high-risk work.

In plain words
What is it for?
Use it to prepare plans for ambiguous or risky coding tasks, especially changes involving security, migrations, destructive actions, production incidents, or compliance.
Why use it?
It helps expose missing requirements, design disagreements, and risks before coding begins.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: names the AskUserQuestion tool; mentions Codex.

Good fit Use it to prepare plans for ambiguous or risky coding tasks, especially changes involving security, migrations, destructive actions, production incidents, or compliance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mrzhangguoguo/oh-my-workbuddy/ralplan
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.

Any agent
npx skills add mrzhangguoguo/oh-my-workbuddy --skill ralplan
Clone the repo
git clone --depth 1 https://github.com/mrzhangguoguo/oh-my-workbuddy

Made for: Claude Code, Codex.

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/skills/mrzhangguoguo/oh-my-workbuddy/ralplan/github.svg)](https://agentmods.dev/skills/mrzhangguoguo/oh-my-workbuddy/ralplan)
Your own site
<a href="https://agentmods.dev/skills/mrzhangguoguo/oh-my-workbuddy/ralplan"><img src="https://agentmods.dev/badge/skills/mrzhangguoguo/oh-my-workbuddy/ralplan/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ralplan

Your own site · 80×15
<a href="https://agentmods.dev/skills/mrzhangguoguo/oh-my-workbuddy/ralplan"><img src="https://agentmods.dev/badge/skills/mrzhangguoguo/oh-my-workbuddy/ralplan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,460 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00064 $0.01460
Opus 5 $0.00032 $0.00730
Sonnet 5 $0.00013 $0.00292
Haiku 4.5 $0.00006 $0.00146

Measured 11d ago against content hash 56faaa23b339, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, 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 11d 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.

skills/ralplan/SKILL.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ported from oh-my-codex ralplan. OMX runtime conventions ($macro invocation, omx CLI, .omx/ state directory) are replaced with WorkBuddy idioms (Skill tool, Agent tool, task list, .workbuddy/memory).

Ralplan (Consensus Planning Alias)

Ralplan is a shorthand alias for plan --consensus. It triggers iterative planning with Planner, Architect, and Critic roles until consensus is reached, with RALPLAN-DR structured deliberation (short mode by default, deliberate mode for high-risk work). An advisory ontology reviewer (Scholastic-style) may inform the plan for ontology-heavy evidence but is not part of the durable consensus gate.

Usage

ralplan "task description"
ralplan --interactive "task description"
ralplan --deliberate "task description"

Flags

  • --interactive: Enables user prompts at key decision points (draft review and final approval). Without it the workflow runs fully automated and outputs the final plan.
  • --deliberate: Forces deliberate mode for high-risk work (adds pre-mortem + expanded test planning). Can also auto-enable when the request signals high risk (auth/security, migrations, destructive changes, production incidents, compliance/PII, public API breakage).

Behavior

This skill simply invokes the plan skill in consensus mode:

skill: plan   (with consensus; add --interactive / --deliberate as requested)

The consensus workflow (full detail in the plan skill):

  1. Planner creates an adaptive plan (right-sized, not exactly five steps) and a compact RALPLAN-DR summary (Principles 3-5, Decision Drivers top 3, Viable Options ≥2 with bounded pros/cons; invalidation rationale if only one remains; deliberate mode adds pre-mortem + expanded test plan).
  2. User feedback (--interactive only): present the draft + Principles/Drivers/Options via AskUserQuestion (Proceed to review / Request changes / Skip review). Otherwise auto-proceed.
  3. Architect reviews for soundness via a separate Agent call (strongest steelman antithesis, a real tradeoff tension, synthesis); await completion before step 4.
  4. Critic evaluates via a separate Agent call, only after step 3 (principle-option consistency, fair alternatives, risk clarity, testable criteria, verification steps).
  5. Re-review loop (max 5): any non-APPROVE verdict re-runs Planner→Architect→Critic until APPROVE or 5 iterations; then present the best version.
  6. On Critic approval (--interactive only): present approval options via AskUserQuestion (ultragoal / team / explicit ralph fallback / specialized goal-mode follow-up / Request changes / Reject). Final plan includes ADR, available-agent-types roster, staffing guidance, team launch hints, team verification path, Goal-Mode Follow-up Suggestions. Otherwise output the final plan and stop.
  7. (--interactive only) On approval: invoke ultragoal (default), team, the selected specialized goal-mode follow-up (autoresearch-goal / performance-goal), or ralph only when explicitly selected — never implement directly.

Read the full file on GitHub · 75 lines

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. 11d ago First seen · 75 lines · 64 tokens per session scan A 56faaa23b339

Subscribe to this mod's changes

ralplan is a skill published in the GitHub repository mrzhangguoguo/oh-my-workbuddy (2 stars, last pushed 2mo ago), licensed MIT. It adds 64 tokens to every session and 1,460 once invoked, about $0.0003 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.

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