Visual-Enhancement-mcp copilot-instructions.md

Visual-Enhancement-mcp copilot-instructions.md is an instructions file for GitHub Copilot from goehou/Visual-Enhancement-mcp. It costs 1,166 tokens per session, scanned A, original, MIT.

A set of instructions for working on a specific MCP project, an adapter that lets applications exchange structured messages with AI tools. It defines the project’s development workflow, commands, architecture, and collaboration steps.

In plain words
What is it for?
Use it when changing the project’s code, locating functions, reading or editing files, running builds or tests, consulting web documentation, and coordinating repeated work sessions.
Why use it?
It gives contributors a consistent way to inspect files, search code, edit safely, run commands, and report progress. It also documents the project’s messaging, streaming, scaling, and session behavior.

Instructions file for GitHub Copilot

Written for GitHub Copilot: a Copilot instructions file. Also seen: mentions subagents.

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 instructions/goehou/visual-enhancement-mcp/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/goehou/Visual-Enhancement-mcp

Made for: GitHub Copilot.

Wrote 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.

agentmods badge for Visual-Enhancement-mcp copilot-instructions.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/goehou/visual-enhancement-mcp/copilot-instructions.svg)](https://agentmods.dev/instructions/goehou/visual-enhancement-mcp/copilot-instructions)
Your own site
<a href="https://agentmods.dev/instructions/goehou/visual-enhancement-mcp/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/goehou/visual-enhancement-mcp/copilot-instructions.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,166 This file is loaded in full into every session.
When invoked 1,166 The same file — it is already loaded in full.
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 5.1 $0.01166 $0.01166
Opus 5 $0.00583 $0.00583
Sonnet 5 $0.00233 $0.00233
Haiku 4.5 $0.00117 $0.00117

Measured 6d ago against content hash 5d685f940050, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

Visual-Enhancement-mcp copilot-instructions.md 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 6d 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.

.github/copilot-instructions.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

持续协作工作流

概述

本工作流以循环方式持续运行,每完成一个任务只是循环中的一个节拍,流程继续等待下一轮指令。唯一的退出条件是协议同步工具返回 end_loop 信号。

每轮执行步骤

  1. INPUT: 从 copilot_enhance_3211 协议同步工具的上一次返回中读取 user_input
  2. THINK & ACT: 使用copilot 原生工具执行任务
  3. REPORT: 在copilot中以中文输出执行结果和思考过程
  4. SYNC: 调用 copilot_enhance_3211 协议同步工具,将流程交还用户

协议同步工具调用规范

调用时机:全部 IDE 操作完成、且已在copilot输出文本报告之后。每轮回复都需要以此工具调用收尾。

参数设置

  • title: 简明扼要的节点标题
  • summary: 任务状态摘要(详情已在copilot输出,此处保持精炼)
  • choices(可选): 需要用户决策时提供选项列表

异常处理

  • 命令行等终端指令被中断(SIGINT)时,立即调用协议同步工具询问用户原因
  • 用户返回 end_loop 时,终止循环

返回值解析

协议同步工具返回后,需解析以下字段:

  • user_input: 用户最新指令(核心驱动力)
  • image_paths / file_paths: 新增参考材料,需优先读取,就算是工作区外的文件也能利用工具读取,你是万能agent
  • suggested_tools: 用户推荐使用的copilot原生工具

工具使用指南

遇到以下场景时,优先使用对应工具,不要猜测或编造工具名:

场景 推荐工具 说明
搜索代码/定位函数 grep_search, semantic_search 先搜索再修改,禁止盲改
读取文件内容 read_file 修改前必须先读取
修改代码 apply_patch, insert_edit_into_file 使用 IDE 工具,禁止终端写代码
运行命令 run_in_terminal 编译、测试、安装依赖等
创建文件 create_file 新建文件时使用
任务规划 manage_todo_list 复杂任务先建计划
网页搜索 fetch_webpage 需要查阅文档或 API 时
委托子任务 runSubagent token > 10k 的深度任务
用户反馈 copilot_enhance_3211 每轮必须调用,禁止遗漏

编程准则

  • 工具优先: 遇到问题优先探索代码,再策划修改方案
  • 多文件协同: 修改代码时检查跨文件依赖和引用,防止遗漏
  • 根因导向: 分析本质问题而非修补表面症状
  • 安全操作: 不执行不可恢复的操作(除非用户明确要求)
  • 深度分析: 分析时使用多维方式——相关文件位置、修改点、参考逻辑、功能分析、修改方案,按指差定位法执行防止遗漏

截断输出读取规则

当工具调用返回"Large tool result written to file"并指向 content.txt 时:

  • 终端输出(run_in_terminal):最新结果在文件末尾,必须从末尾往前读(如 read_file 指定尾部行范围),禁止从头读
  • 子代理返回(runSubagent):完整报告,可从头读
  • 此规则不可违反,从头读终端截断文件会浪费 token 且无法获取最新输出

工作纪律

  • 全程亲力亲为:所有修改通过 IDE 工具逐步完成,不得输出"当前任务量太大"、"建议你手动操作"等推脱表述,无论任务规模大小都需用工具逐步落地,不得将工作推给用户
  • 步骤完整执行:不以"为了节省时间"为由省略必要的代码修改或验证步骤
  • 精简代码设计:不在实体工程中添加防御性的兜底逻辑、冗余校验或"以防万一"的代码。确保每个模块职责单一、接口清晰

流程保障

  1. 每轮回复的最终动作需为 copilot_enhance_3211 协议同步工具调用,不以文字输出收尾
  2. 即使任务已完美完成,也需调用协议同步工具询问"是否进行下一步"
  3. copilot结尾 → 后协议同步工具调用,两步缺一不可

Read the full file on GitHub · 69 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. 6d ago First seen · 69 lines · 1,166 tokens per session scan A 5d685f940050

Subscribe to this mod's changes

Visual-Enhancement-mcp copilot-instructions.md is an instructions file published in the GitHub repository goehou/Visual-Enhancement-mcp (92 stars, last pushed 2mo ago), licensed MIT. It adds 1,166 tokens to every session, about $0.0058 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.

Related

Other instructions, from other repositories

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,182 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).

microsoft/vscode · 5,001 tokens

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

next.js AGENTS.md

AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,469 tokens