Borrowing it
Nothing to install: this file belongs to Orinks/AccessiWeather. 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/Orinks/AccessiWeather/main/.codex/skills/ralplan/SKILL.mdgit clone --depth 1 https://github.com/Orinks/AccessiWeatherWrote 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/orinks/accessiweather/ralplan)<a href="https://agentmods.dev/skills/orinks/accessiweather/ralplan"><img src="https://agentmods.dev/badge/skills/orinks/accessiweather/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/orinks/accessiweather/ralplan"><img src="https://agentmods.dev/badge/skills/orinks/accessiweather/ralplan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 18 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00014 | $0.02368 |
| Opus 5.5 | $0.00006 | $0.00947 |
| Sonnet 5.5 | $0.00003 | $0.00474 |
| Haiku 4.5 | $0.00001 | $0.00237 |
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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralplan (Consensus Planning Alias)
Ralplan is a shorthand alias for $plan --consensus. It triggers iterative planning with Planner, Architect, and Critic agents until consensus is reached, with RALPLAN-DR structured deliberation (short mode by default, deliberate mode for high-risk work).
Usage
$ralplan "task description"
Flags
--interactive: Enables user prompts at key decision points (draft review in step 2 and final approval in step 6). Without this flag the workflow runs fully automated — Planner → Architect → Critic loop — and outputs the final plan without asking for confirmation.--deliberate: Forces deliberate mode for high-risk work. Adds pre-mortem (3 scenarios) and expanded test planning (unit/integration/e2e/observability). Without this flag, deliberate mode can still auto-enable when the request explicitly signals high risk (auth/security, migrations, destructive changes, production incidents, compliance/PII, public API breakage).
Usage with interactive mode
$ralplan --interactive "task description"
Behavior
GPT-5.5 Guidance Alignment
Use the shared workflow guidance pattern: outcome-first framing, concise visible updates for multi-step planning, local overrides for the active workflow branch, evidence-backed planning and validation expectations, explicit stop rules, right-sized implementation/PRD shape, and automatic continuation for safe reversible steps. Ask only for material, destructive, credentialed, external-production, or preference-dependent branches.
This skill invokes the Plan skill in consensus mode:
$plan --consensus <arguments>
$plan --consensus --interactive <arguments>
The consensus workflow:
- Planner creates an adaptive plan (right-sized to task scope; do not default to exactly five steps) and a compact RALPLAN-DR summary before review:
- Principles (3-5)
- Decision Drivers (top 3)
- Viable Options (>=2) with bounded pros/cons
- If only one viable option remains, explicit invalidation rationale for alternatives
- Deliberate mode only: pre-mortem (3 scenarios) + expanded test plan (unit/integration/e2e/observability)
- User feedback (--interactive only): If
--interactiveis set, use the structured question UI (omx questionin attached tmux; native structured input outside tmux when available) to present the draft plan plus the Principles / Drivers / Options summary before review (Proceed to review / Request changes / Skip review). Otherwise, automatically proceed to review. - Architect reviews for architectural soundness and must provide the strongest steelman antithesis, at least one real tradeoff tension, and (when possible) synthesis — await completion before step 4. In deliberate mode, Architect should explicitly flag principle violations.
- Critic evaluates against quality criteria — run only after step 3 completes. Critic must enforce principle-option consistency, fair alternatives, risk mitigation clarity, testable acceptance criteria, and concrete verification steps. In deliberate mode, Critic must reject missing/weak pre-mortem or expanded test plan.
- Re-review loop (max 5 iterations): Any non-
APPROVECritic verdict (ITERATEorREJECT) MUST run the same full closed loop: a. Collect Architect + Critic feedback b. Revise the plan with Planner c. Return to Architect review d. Return to Critic evaluation e. Repeat this loop until Critic returnsAPPROVEor 5 iterations are reached f. If 5 iterations are reached withoutAPPROVE, present the best version to the user - On Critic approval (--interactive only): If
--interactiveis set, use the structured question UI to present the plan with approval options (Approve and execute via ralph / Approve and implement via team / Request changes / Reject). Final plan must include ADR (Decision, Drivers, Alternatives considered, Why chosen, Consequences, Follow-ups), an explicit available-agent-types roster, concrete follow-up staffing guidance for bothralphandteam, suggested reasoning levels by lane, explicitomx team/$teamlaunch hints, and a concrete team verification path. Otherwise, output the final plan and stop. - (--interactive only) User chooses: Approve (ralph or team), Request changes, or Reject
- (--interactive only) On approval: invoke
$ralphfor sequential execution or$teamfor parallel team execution with the explicit available-agent-types roster, reasoning-by-lane guidance, role/staffing allocation guidance, launch hints, and verification-path guidance from the approved plan -- 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 · 163 lines · 14 tokens per session scan A 457712ad363b
ralplan is a skill published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed 2d ago), licensed MIT. It adds 14 tokens to every session and 2,368 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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