Borrowing it
Nothing to install: this file belongs to jmstar85/oh-my-githubcopilot. 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/jmstar85/oh-my-githubcopilot/main/.github/skills/ralplan/SKILL.mdgit clone --depth 1 https://github.com/jmstar85/oh-my-githubcopilotWrote 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/jmstar85/oh-my-githubcopilot/ralplan)<a href="https://agentmods.dev/skills/jmstar85/oh-my-githubcopilot/ralplan"><img src="https://agentmods.dev/badge/skills/jmstar85/oh-my-githubcopilot/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/jmstar85/oh-my-githubcopilot/ralplan"><img src="https://agentmods.dev/badge/skills/jmstar85/oh-my-githubcopilot/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 | $0.00028 | $0.00923 |
| Opus 5.5 | $0.00011 | $0.00369 |
| Sonnet 5.5 | $0.00006 | $0.00185 |
| Haiku 4.5 | $0.00003 | $0.00092 |
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 2mo 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralplan (Consensus Planning)
Shorthand for /plan --consensus. Triggers iterative planning with Planner, Architect, and Critic agents until consensus is reached.
Flags
--deliberate: Forces deliberate mode for high-risk work. Adds pre-mortem (3 scenarios) and expanded test planning.
Interactive Hook Protocol
MANDATORY: Use vscode_askQuestions at every decision gate in this skill (when available).
If vscode_askQuestions is NOT available (e.g., Copilot CLI), present numbered options in markdown and ask the user to respond with a number or freeform text.
When to Fire Hooks
| Trigger Point | Question Type |
|---|---|
| Options presentation | Present viable options with pros/cons for user selection |
| Architect concerns | Surface architectural trade-offs for user decision |
| Critic rejection | Show rejection reasons, ask user for direction |
| Consensus reached | Final plan approval before execution |
--deliberate pre-mortem |
Present risk scenarios for user prioritization |
Workflow
- @planner creates initial plan with RALPLAN-DR summary:
- Principles (3-5)
- Decision Drivers (top 3)
- Viable Options (>=2) with pros/cons
- HOOK: Present options via
vscode_askQuestionswhen options have significant trade-offs:header: "ralplan-options" question: "Planner identified [N] viable approaches. Select preferred direction:" options: [ { label: "Option A: [name]", description: "Pros: X, Y. Cons: Z" }, { label: "Option B: [name]", description: "Pros: A, B. Cons: C" }, { label: "Let agents decide based on technical merit", recommended: true } ] allowFreeformInput: true - @architect reviews for architectural soundness
- If architect raises concerns: HOOK — present trade-offs for user decision
- @critic validates quality and testability
- If critic rejects: HOOK — show issues and ask for direction:
header: "ralplan-critic-feedback" question: "Critic identified [N] issues: [summary]. How to proceed?" options: [ { label: "Address all issues", recommended: true }, { label: "Address critical issues only, skip minor" }, { label: "Override — I accept the trade-offs" }, { label: "Restart with different constraints" } ] - Loop until critic approves (max 5 iterations)
- Final plan includes ADR (Decision, Drivers, Alternatives, Why chosen, Consequences)
- HOOK: Final approval via
vscode_askQuestions:header: "ralplan-approval" question: "Consensus reached after [N] iterations. Execute?" options: [ { label: "Execute with team (parallel)", recommended: true }, { label: "Execute with ralph (sequential + verification)" }, { label: "Execute with omg-autopilot (full pipeline)" }, { label: "Save plan — don't execute yet" } ]
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.
- 2mo ago First seen · 88 lines · 28 tokens per session scan A 5da413968089
ralplan is a skill published in the GitHub repository jmstar85/oh-my-githubcopilot (162 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 923 once invoked, about $0.0001 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 2026-08-30.
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