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.
npx skills add mrzhangguoguo/oh-my-workbuddy --skill ralplangit clone --depth 1 https://github.com/mrzhangguoguo/oh-my-workbuddyWrote 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/skills/mrzhangguoguo/oh-my-workbuddy/ralplan)<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.
<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>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 | $0.00064 | $0.01460 |
| Opus 5.5 | $0.00026 | $0.00584 |
| Sonnet 5.5 | $0.00013 | $0.00292 |
| Haiku 4.5 | $0.00006 | $0.00146 |
Grade A, and why
ralplan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ported from oh-my-codex
ralplan. OMX runtime conventions ($macroinvocation,omxCLI,.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):
- 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).
- User feedback (--interactive only): present the draft + Principles/Drivers/Options via
AskUserQuestion(Proceed to review / Request changes / Skip review). Otherwise auto-proceed. - Architect reviews for soundness via a separate Agent call (strongest steelman antithesis, a real tradeoff tension, synthesis); await completion before step 4.
- Critic evaluates via a separate Agent call, only after step 3 (principle-option consistency, fair alternatives, risk clarity, testable criteria, verification steps).
- Re-review loop (max 5): any non-APPROVE verdict re-runs Planner→Architect→Critic until APPROVE or 5 iterations; then present the best version.
- 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. - (--interactive only) On approval: invoke
ultragoal(default),team, the selected specialized goal-mode follow-up (autoresearch-goal/performance-goal), orralphonly when explicitly selected — never implement directly.
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.
- 1mo ago First seen · 75 lines · 64 tokens per session scan A 56faaa23b339
ralplan is a skill published in the GitHub repository mrzhangguoguo/oh-my-workbuddy (2 stars, last pushed 3mo ago), licensed MIT. It adds 64 tokens to every session and 1,460 once invoked, about $0.0003 per session on Opus 5.5. 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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