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.
git clone --depth 1 https://github.com/qqabcv520/my-claude-marketplacesWrote 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/commands/qqabcv520/my-claude-marketplaces/ps-plan)<a href="https://agentmods.dev/commands/qqabcv520/my-claude-marketplaces/ps-plan"><img src="https://agentmods.dev/badge/commands/qqabcv520/my-claude-marketplaces/ps-plan/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/commands/qqabcv520/my-claude-marketplaces/ps-plan"><img src="https://agentmods.dev/badge/commands/qqabcv520/my-claude-marketplaces/ps-plan.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 | $0.00020 | $0.00534 |
| Opus 5 | $0.00010 | $0.00267 |
| Sonnet 5 | $0.00004 | $0.00107 |
| Haiku 4.5 | $0.00002 | $0.00053 |
Grade A, and why
ps-plan 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 12d 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.
What it actually says
交互式计划制定命令
此命令启动交互式计划制定流程，帮助你创建详细、可执行的实施计划。
使用方法
/ps-plan [可选：功能描述]
示例：
/ps-plan 添加用户认证功能
/ps-plan 实现深色模式
/ps-plan
执行流程
当你运行此命令时，将自动调用 planning skill。默认流程分为 5 个阶段：
-
深度理解
- 判定任务复杂度
- 收集相关代码、文档、依赖和约束
- 必要时做功能拆解和需求澄清
-
方案设计
- 评估候选技术方案
- 明确范围取舍、模块规约和执行思路
- 为复杂任务补齐技术性验收标准
-
审查对齐
- 回读关键文件验证方案可行性
- 识别风险、补齐测试策略和剩余决策点
-
最终计划
- 按模板整理为结构化计划文件
- 明确执行步骤、文件路径、代码片段和验证命令
-
退出规划
- 由用户确认计划是否作为执行基线
- 如需继续执行，转入
/ps-exec
输出
计划文档将保存到：docs/spec/[YYYYMMDD-HHmm]-[功能名称].md
后续操作
计划创建完成后，可以使用 /ps-exec 命令执行计划：
/ps-exec docs/spec/[YYYYMMDD-HHmm]-[功能名称].md
或直接运行 /ps-exec，系统会自动选择最新的计划文件。
执行指令
重要： 当此命令被调用时，必须立即使用 Skill tool 调用 planning skill。
使用以下方式调用：
Skill tool:
- skill: "planning"
- args: [用户提供的功能描述，如果有的话]
如果用户提供了功能描述参数，将其作为 args 传递给 skill。如果没有提供，则不传递 args 参数，让 skill 自行询问。
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.
- 12d ago First seen · 79 lines · 20 tokens per session scan A 2ce81a1c72b6
ps-plan is a command published in the GitHub repository qqabcv520/my-claude-marketplaces (2 stars, last pushed 5mo ago), licensed MIT. It adds 20 tokens to every session and 534 once invoked, about $0.0001 per session on Opus 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.