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 agentmods add agents/feimacode/feima-copilot-llms-extension/planninggit clone --depth 1 https://github.com/feimacode/feima-copilot-llms-extensionWrote 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/agents/feimacode/feima-copilot-llms-extension/planning)<a href="https://agentmods.dev/agents/feimacode/feima-copilot-llms-extension/planning"><img src="https://agentmods.dev/badge/agents/feimacode/feima-copilot-llms-extension/planning.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.00989 |
| Opus 5 | $0.00016 | $0.00495 |
| Sonnet 5 | $0.00006 | $0.00198 |
| Haiku 4.5 | $0.00003 | $0.00099 |
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 4d 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
Plan 智能体(Agent)让你能够在开始实施之前创建详细的实施计划，确保所有需求都得到满足。借助待办事项列表，智能体(Agent)可以专注于整体目标并有效跟踪进度。
有关 Plan 智能体(Agent)如何适应整体智能体(Agent)架构的背景知识，请参阅智能体(Agent)概念。
本文介绍如何在 VS Code 中使用 Plan 智能体(Agent)和待办事项列表。
💡 国内用户提示 如果您希望使用国内 AI 模型，安装 飞码扣插件 即可在 GitHub Copilot Chat 中使用通义千问(Qwen3)、DeepSeek、GLM 等模型，按次计费。了解更多 → 打开飞码扣
如何规划任务
要规划任务，可在聊天视图中使用内置的 Plan 智能体(Agent)，描述你的任务，然后对生成的计划进行迭代。
-
按 ⌃⌘I（Windows、Linux：Ctrl+Alt+I）打开聊天视图，从智能体(Agent)下拉菜单选择 Plan。
或者，输入
/plan后跟任务描述，可以同时切换到 Plan 智能体(Agent)并开始规划。 -
输入高层级任务（功能、重构、Bug 等）并提交。例如：
实现一个支持 OAuth2 和 JWT 的用户认证系统使用
/plan斜杠命令(Slash Command)直接从聊天输入框开始规划：/plan 为所有 API 端点添加单元测试 -
在智能体(Agent)研究你的任务后，回答它提出的澄清性问题。
-
Plan 智能体(Agent)会生成高层级计划摘要、实施步骤和验证步骤。审查计划草案并提交后续提示进行迭代，直到计划满足你的需求。
-
计划确定后，选择开始实施，或在编辑器中打开规划提示词(Prompt)以进一步审查。
要实施计划，可以在同一会话中继续，也可以启动新的 Copilot CLI 会话在后台实施计划。
提示 Plan 智能体(Agent)会自动将实施计划保存到会话记忆文件（
/memories/session/plan.md）。要访问此文件，运行 Chat: Show Memory Files 命令并从列表中选择plan.md。会话记忆在对话结束时会被清除，因此计划在后续会话中不可用。
自定义规划过程
你可以根据团队的工作流程定制规划过程：
-
创建自定义规划智能体(Agent)。 定义一个自定义智能体(Custom Agent)，包含你规划过程的特定说明，例如强制执行架构指南或要求特定的规划交付物。
-
为规划和实施选择模型。 使用
chat.planAgent.defaultModel设置为 Plan 智能体(Agent)选择默认模型，使用github.copilot.chat.implementAgent.model设置实施步骤的模型。 -
为 Plan 智能体(Agent)添加额外工具（实验性）。 使用
github.copilot.chat.planAgent.additionalTools设置为 Plan 智能体(Agent)在研究和规划阶段提供额外工具访问权限。例如，使用 MCP 服务器连接内部数据源或工具。
延伸阅读
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.
- 4d ago First seen · 61 lines · 32 tokens per session scan A c04994f267cb
planning is an agent published in the GitHub repository feimacode/feima-copilot-llms-extension (5 stars, last pushed 4d ago), licensed MIT. It adds 32 tokens to every session and 989 once invoked, about $0.0002 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 agents, from other repositories
agent_types
This document describes how the unified three-layer Device Agent architecture is implemented across different platforms. While the core framework (State, Processor, Command layers) remains consistent, each platform implements specialized agents optimized for their native control mechanisms and hierarchies.…
server_client_architecture
Device agents in UFO are partitioned into server and client components, separating high-level orchestration from low-level execution. This architecture enables safe, scalable, and flexible task execution across heterogeneous devices through the Agent Interaction Protocol (AIP).
advisor
Opus 级高级技术顾问，提供第二意见、方案比较和风险审阅。适用于技术选型、实现规划、架构/API/并发/安全问题、疑难 bug 根因、代码或调查结论审阅，以及任何需要判断“是否/哪种/怎么做”的任务；可读取调用方明确列出的证据文件，不负责实现或验证。.
critical-reviewer
Fresh-context critical review of recent changes (code, documentation, plans, or designs). Read-only. Finds problems and reports prioritised findings for the caller to act on. Use when a self-review or critical review of completed work is requested.
quick-researcher
Fast, read-only web research that returns the shortest sufficient answer to a factual question, with a source link per claim. Use for "what/which/when/how much/is X still..." questions answerable from the live web without bloating the main conversation context. Not for software implementation guides (use…
orchestrator
Agent "orchestrator" from openinterpreter/openinterpreter, covering user updates spec, reviews and general guidelines.