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 konglong87/methodology-skills --skill planninggit clone --depth 1 https://github.com/konglong87/methodology-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/konglong87/methodology-skills/planning)<a href="https://agentmods.dev/skills/konglong87/methodology-skills/planning"><img src="https://agentmods.dev/badge/skills/konglong87/methodology-skills/planning/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/konglong87/methodology-skills/planning"><img src="https://agentmods.dev/badge/skills/konglong87/methodology-skills/planning.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 | $0.00029 | $0.09357 |
| Opus 5.5 | $0.00012 | $0.03743 |
| Sonnet 5.5 | $0.00006 | $0.01871 |
| Haiku 4.5 | $0.00003 | $0.00936 |
Grade A, and why
planning 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 — 1,388 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning - 实施规划与步骤分解
前置协议
强制触发规则
触发时机:prompt-enhancer 完成需求细化后(强制)
触发条件:
- ✅ 已有明确的需求和方案
- ✅ 需要实施规划和步骤分解
- ✅ 复杂任务(需要多个步骤完成)
例外情况:
- 简单任务(单步即可完成)
- 用户明确表示"直接开始"
依赖检查
前置依赖:prompt-enhancer
检查内容:
- 检查工件文件:
memory/artifacts/prompt-enhancer/ - 确认已有明确的需求和选定方案
- 如果无前置工件,需先执行 prompt-enhancer
与 goal-oriented 的协作
触发来源:goal-oriented skill 强制调用
触发流程:
goal-oriented 创建目标
↓
调用 prompt-enhancer(需求细化)
↓
生成需求方案工件
↓
调用 planning(实施规划)
↓
读取需求方案工件 → 生成实施计划 → plan-review
↓
生成实施计划工件
↓
传递给 execution 阶段
↓
任务完成后 → experience-manager 经验沉淀
工件接收:
- goal-oriented 会写入工件到
memory/artifacts/prompt-enhancer/ - planning 自动检测并读取最新工件
工件传递机制
输入工件(来自 prompt-enhancer)
工件路径: memory/artifacts/prompt-enhancer/result-{timestamp}.json
工件格式:
{
"skill": "prompt-enhancer",
"version": "2.0.0",
"requirement": {
"original": "用户原始需求",
"refined": {
"type": "功能开发|重构|优化",
"core_features": ["功能1", "功能2"],
"target_users": "目标用户",
"platform": "平台",
"tech_stack": ["技术1", "技术2"],
"success_criteria": ["标准1", "标准2"],
"timeline": "时间线"
}
},
"solution": {
"selected": "选定方案",
"reasons": ["理由1", "理由2"],
"alternatives": [
{
"name": "备选方案1",
"pros": ["优势"],
"cons": ["劣势"]
}
]
}
}
读取方式:
# 自动检测最新的 prompt-enhancer 工件
PROMPT_ENHANCER_ARTIFACT=$(ls -t memory/artifacts/prompt-enhancer/*.json | grep -v latest.json | head -1)
if [ -n "$PROMPT_ENHANCER_ARTIFACT" ]; then
echo "✅ Found prompt-enhancer artifact: $PROMPT_ENHANCER_ARTIFACT"
# Read artifact content using Read tool
else
echo "❌ No prompt-enhancer artifact found"
echo "⚠️ Must execute prompt-enhancer before planning"
fi
输出工件(传递给后续技能)
工件路径: memory/artifacts/planning/result-{timestamp}.json
工件格式: 见 "步骤8:生成工件"
传递机制:
- 创建工件文件
- 创建 latest.json 符号链接
- 后续技能可自动检测 latest.json
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 1,388 lines · 29 tokens per session scan A b3d69bf96855
planning is a skill published in the GitHub repository konglong87/methodology-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 29 tokens to every session and 9,357 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 2026-08-31.
Other skills, from other repositories
importing-a-codebase
Use when asked to create the initial spec graph for an existing codebase that has source code but no specs. Normally reached via setting-up-a-project.
starting-a-new-project
Use when the workspace is empty, has no code, and the user brings a raw project idea. Normally reached via setting-up-a-project; not for an existing project.
managing-mcp-servers
Use when the user asks to add, change, remove, enable or disable an MCP server, or when an MCP tool is missing, needs sign-in, fails, or was blocked. ThinkRail runs pi in-process, so there is no pi command to call.
todos
Use when the user asks for a shared plan, a task needs at least three substantive execution steps, or a loose TODO is pending. Without an explicit plan request, not for one-shot answers or checks, or one- to two-step work that is not already in the list.
asking-user-questions
Use when composing an askuserquestion round inside a workflow, or when a workflow skill names it at a question step. Shared norms for the tool — not a workflow, nothing to execute.
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.