plan-task

A command that designs a technical solution and creates an implementation plan for a project task. It reads task files and related requirements before writing the plan.

In plain words
What is it for?
Use it to plan a task stored in the project's .ai-workspace folders, compare solution options, list implementation steps, and define how to verify the work.
Why use it?
It turns existing requirements into a documented approach, including possible solutions, files to change, testing, risks, and rollback steps.

Command for Claude Code

Install

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.

agentmods
npx agentmods add commands/modelengine-group/fit-framework/plan-task
Clone the repo
git clone --depth 1 https://github.com/ModelEngine-Group/fit-framework

Made for: Claude Code.

Per session 12 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,879 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00012 $0.01879
Opus 5 $0.00006 $0.00940
Sonnet 5 $0.00002 $0.00376
Haiku 4.5 $0.00001 $0.00188

Measured yesterday against content hash 96b27f5d2cbb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

plan-task 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 yesterday.

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.

.claude/commands/plan-task.md · 242 lines

How it starts

The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plan Task Command

功能说明

为指定任务设计技术方案,输出详细的实施计划。

⚠️ CRITICAL: 状态更新要求

执行此命令后,你必须立即更新任务状态。参见规则 7。

执行流程

1. 查找任务文件

按以下优先级搜索任务:

  • 查找 .ai-workspace/active/{task-id}/task.md(优先)
  • 如果不存在,查找 .ai-workspace/blocked/{task-id}/task.md
  • 如果不存在,查找 .ai-workspace/completed/{task-id}/task.md
  • 如果都不存在,提示用户任务不存在

找到后记录任务状态(status)和任务目录路径。

注意:{task-id} 格式为 TASK-{yyyyMMdd-HHmmss},例如 TASK-20260205-202013

2. 读取需求分析

读取 .ai-workspace/{status}/{task-id}/analysis.md

  • 如果不存在,提示用户需要先执行需求分析
  • 如果存在,读取并理解需求

3. 理解问题本质和约束条件

  • 阅读 analysis.md,理解问题的根本原因和影响范围
  • 识别技术约束(从 analysis.md 的"技术依赖和约束"章节获取)
  • 识别特殊要求(例如:安全修复需要考虑漏洞修复版本、Bug修复需要防止回归、功能开发需要考虑扩展性)

4. 设计解决方案

按照对应的工作流(如 .agents/workflows/feature-development.yaml)中的 technical-design 步骤:

  • 基于 analysis.md 中的信息,提出多个可行方案
  • 对比各方案的优劣(效果、成本、风险、可维护性)
  • 选择最合适的方案并说明理由
  • 制定详细的实施步骤
  • 列出需要创建/修改的文件清单
  • 设计验证策略(测试、验证、回归检查)
  • 评估影响(性能、安全、兼容性)
  • 制定风险控制和回滚方案

5. 输出方案文档

创建 .ai-workspace/{status}/{task-id}/plan.md,必须包含以下章节:

# 技术方案和实施计划

## 方案决策

### 问题理解
{基于 analysis.md 的问题理解和根本原因}

### 约束条件
- 技术约束: {技术依赖和限制}
- 业务约束: {业务要求和限制}
- 时间约束: {交付时间要求}

### 备选方案对比分析
{如果有多个方案,详细对比分析各方案的优劣}

### 最终选择
- **方案**:{选择的方案}
- **理由**:{选择理由}

## 技术方案

### 核心解决策略
{详细的解决策略}

### 关键技术点
- {技术点1}
- {技术点2}

### 具体实现细节
{根据问题类型的具体实现,例如:代码实现、依赖升级、配置调整等}

## 实施步骤

### 步骤 1: {步骤名称}
**操作**:{具体操作}
**预期结果**:{预期结果}

### 步骤 2: {步骤名称}
...

## 文件清单

### 需要创建的文件
- `{file-path}` - {说明}

### 需要修改的文件
| 序号 | 文件路径 | 修改内容 | 预计行数 |
|------|----------|----------|----------|
| 1 | {path} | {内容} | {行数} |

## 验证策略

### 功能验证
- 单元测试: {测试范围和验收标准}
- 集成测试: {测试范围和验收标准}

### 问题验证
{确认问题已解决,如:功能正常、Bug不再复现、漏洞已修复}

### 回归验证
{确保没有引入新问题}

## 影响评估

### 性能影响
{性能影响分析和优化建议}

### 安全影响
{安全风险评估和防护措施}

### 兼容性影响
{兼容性分析和注意事项}

## 风险控制

### 潜在风险
| 风险 | 等级 | 应对措施 |
|------|------|----------|
| {风险} | {等级} | {措施} |

### 回滚方案
{如果实施失败,如何回滚}

## 预期产出
- {产出1}
- {产出2}

6. 更新任务状态

更新 .ai-workspace/active/{task-id}/task.md

  • current_step: technical-design
  • assigned_to: claude
  • updated_at: {当前时间}
  • 标记 plan.md 为已完成
  • 在工作流进度中标记技术方案设计为完成

Read the full file on GitHub · 242 lines

Changes

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.

  1. yesterday First seen · 242 lines · 12 tokens per session scan A 96b27f5d2cbb

Subscribe to this mod's changes

plan-task is a command published in the GitHub repository ModelEngine-Group/fit-framework (2,117 stars, last pushed 5mo ago), licensed MIT. It adds 12 tokens to every session and 1,879 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 2026-08-30.