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 an8079/take-skills --skill ralplangit clone --depth 1 https://github.com/an8079/take-skillsWrote 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/an8079/take-skills/ralplan)<a href="https://agentmods.dev/skills/an8079/take-skills/ralplan"><img src="https://agentmods.dev/badge/skills/an8079/take-skills/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/an8079/take-skills/ralplan"><img src="https://agentmods.dev/badge/skills/an8079/take-skills/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 | $0.00000 | $0.01723 |
| Opus 5 | $0.00000 | $0.00861 |
| Sonnet 5 | $0.00000 | $0.00345 |
| Haiku 4.5 | $0.00000 | $0.00172 |
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 9d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ralplan — RALPLAN Structured Planning Protocol
name
ralplan
description
RALPLAN（Rationale-Augmented LLM Planning）结构化规划协议：提供 4 种规划模式（Quick / Standard / Deep / Review），每种模式包含标准化的输入→结构化分析→输出流程。核心特色是"审议"（Deliberate）阶段，通过追问确保规划质量，避免 LLM 的幻觉式规划。
when to activate
用户说以下话时激活：
- "帮我规划一下"
- "RALPLAN"
- "ralplan"
- "结构化规划"
- "帮我做一个计划"
- "分析一下怎么做"
- 任何需要将复杂目标分解为可执行步骤的场景
- 当用户提出模糊目标（如"帮我做个好的产品"）时自动触发
protocol
输入阶段（Input）
标准化接收格式：
[User Goal] <一句话描述核心目标>
[Context] <背景信息、约束、限制>
[Resources] <可用资源、时间、预算>
[Stakeholders] <相关方>
如果用户未提供完整信息，执行 Quick Clarify（不超过 3 个追问）后继续。
模式一：Quick Plan（快速规划）< 5 分钟
适用场景：简单任务、单一步骤分解
协议步骤：
- 目标澄清：一句话复述目标
- 步骤拆解：3~7 步，每步一行
- 风险识别：最多 2 个关键风险
- 输出格式（标题式）：
## 🎯 [目标] ### 执行步骤 1. [步骤] — 预计 X 分钟 2. [步骤] — 预计 Y 分钟 3. ... ### ⚠️ 关键风险 - [风险1] → 缓解：[方案] - [风险2] → 缓解：[方案] ### ✅ 成功标准 - [可验证的完成条件]
模式二：Standard Plan（标准规划）5~15 分钟
适用场景：中型项目、多步骤协作
协议步骤：
- 目标分解：用 MECE 原则将目标拆解为独立模块
- 依赖分析：建立任务依赖图
- 资源分配：为每个模块分配时间和角色
- 里程碑设置：定义 2~4 个检查点
- 审议（Deliberate）：在输出前，反问自己：
- "每个步骤的完成标准是否可验证？"
- "是否有遗漏的依赖或风险？"
- "假设条件是否合理？"
- 输出格式：
## 🎯 [目标] ### 📊 分解结构 [树形或列表展示模块关系] ### 📅 时间线 - Week 1: [里程碑A] - Week 2: [里程碑B] - ... ### 🔗 依赖图 [A] → [B] → [C] ↘ [D] ↗ ### 🎯 里程碑 - M1 [日期]: [可交付物] - M2 [日期]: [可交付物] ### ⚠️ 风险矩阵 | 风险 | 影响 | 概率 | 缓解 | |------|------|------|------| | ... | ... | ... | ... | ### ✅ 成功标准 - [量化指标]
模式三：Deep Plan（深度规划）15~60 分钟
适用场景：复杂项目、战略级决策
协议步骤：
- 背景深挖：使用
deep-dive技能对背景进行技术调研 - 利益相关方分析：从每个相关方视角推导需求
- 备选方案生成：至少 2 个等效方案（含 SWOT 分析）
- 决策树构建：为每个关键决策点建立决策树
- 压力测试：对每个方案问"如果 X 失败怎么办"（X=资金/人/时间/技术）
- 执行规划：从最佳方案中拆解执行步骤
- 输出格式（完整报告）：
## 🎯 [战略目标] ### 背景摘要 [基于 deep-dive 的调研摘要] ### 利益相关方视角 - [甲方]: [核心诉求] → [设计方案] - [用户]: [核心诉求] → [设计方案] ### 备选方案比较 | 维度 | 方案A | 方案B | |------|-------|-------| | 成本 | | | | 周期 | | | | 风险 | | | | 可扩展性 | | | ### 决策树 [完整的决策树图] ### 执行路线图 Phase 1 (Week 1-2): [目标 + 里程碑] Phase 2 (Week 3-4): [目标 + 里程碑] ... ### 应急计划 - 触发条件：[某种失败] → 执行：[应急方案]
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.
- 9d ago First seen · 192 lines · 0 tokens per session scan A fd204d6d35fe
ralplan is a skill published in the GitHub repository an8079/take-skills (4 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,723 tokens. 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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