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context-discipline语境纪律

Agent Skill

context-discipline 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

514

周安装

21

GitHub Stars

315

下载量

165
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:context-discipline(语境纪律)
来源仓库:https://github.com/codewithmukesh/dotnet-claude-kit
仓库路径:skills/context-discipline
安装命令:
npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill context-discipline
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill context-discipline

简介

倡导优先使用 Roslyn MCP 查询而非全文读取文件。

  • 采用懒加载原则,只在需要时加载必要信息。context-discipline 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 子代理享有独立上下文窗口,实现上下文隔离。
  • 适用于 .NET 项目,最大化利用语言服务降低 token 消耗。
  • 需正确配置 MCP 服务器,否则核心功能无法启用。

SKILL.md

Context Discipline

Core Principles

  1. MCP tools first, file reads second — A Roslyn MCP query costs 30-150 tokens. Reading a file costs 500-2000+ tokens. For navigation and understanding, always try MCP tools before opening files. Only read files when you need to modify them or MCP tools don't provide enough detail.
  2. Lazy load everything — Don't read files "just in case." Don't load all skills upfront. Don't explore directories you aren't about to modify. Load information at the moment you need it, not before.
  3. Subagents are context isolation chambers — Every subagent gets its own context window. Offload exploration, research, and analysis to subagents. They process information and return a summary — your main context stays clean.
  4. Summarize and discard — After exploring a subsystem, summarize what you learned in a few lines. The summary is what stays in context, not the raw file contents. Think of it as compressing information.
  5. Know your budget — A 200k token window sounds large but fills fast. A typical.cs file is 500-2000 tokens. Loading 50 files can consume half your budget. Plan your reads like you plan your sprints — deliberately.

Patterns

MCP-First Navigation

Always prefer MCP tools for understanding code structure:

TASK: Understand how OrderService works

EXPENSIVE APPROACH (file reads):
1. Read src/Orders/OrderService.cs          → ~1200 tokens
2. Read src/Orders/IOrderService.cs         → ~300 tokens
3. Read src/Orders/OrderRepository.cs       → ~800 tokens
4. Read src/Orders/Models/Order.cs          → ~600 tokens
Total: ~2900 tokens consumed

TOKEN-EFFICIENT APPROACH (MCP-first):
1. find_symbol "OrderService"               → ~50 tokens (file path + line)
2. get_public_api "OrderService"            → ~120 tokens (method signatures)
3. find_references "OrderService"           → ~80 tokens (who uses it)
4. get_type_hierarchy "OrderService"        → ~60 tokens (inheritance chain)
Total: ~310 tokens consumed — 9x cheaper

Only THEN read the specific method you need to modify: ~200 tokens
Grand total: ~510 tokens vs 2900 — 5.7x savings

Subagent Offloading Decision Matrix

Decide when to offload to a subagent vs. handle in main context:

USE A SUBAGENT WHEN:
- Exploring an unfamiliar part of the codebase (> 3 files to read)
- Researching a question that requires reading docs or multiple files
- Running analysis that produces verbose output (test results, diagnostics)
- Comparing approaches that require loading multiple examples
- Any task where the journey is verbose but the answer is concise

STAY IN MAIN CONTEXT WHEN:
- Modifying a file you've already read
- Quick lookups (1-2 MCP queries)
- Tasks where you need to see prior conversation context
- Writing code that builds on discussion with the user

Example subagent delegation:

TASK: "Find all places where we handle authentication"

MAIN CONTEXT APPROACH (expensive):
- Read 8 files, consume ~8000 tokens
- All that content stays in context forever

SUBAGENT APPROACH (efficient):
- Spawn subagent: "Find all authentication handling in this codebase.
  Use find_symbol and find_references for auth-related types.
  Return: file paths, line numbers, and a 1-line summary per location."
- Subagent returns ~200 tokens of summarized findings
- Main context stays clean

Context Budget Planning

Before a complex task, estimate token spend per phase:

UNDERSTAND (~5k): MCP queries (~300) + subagent exploration (~500) + reserve
PLAN (~2k):       Discussion + plan documentation
IMPLEMENT (~15k): Read files to modify (~3k) + write code (~5k) + iteration (~7k)
VERIFY (~3k):     Build + test + diagnostics + format check
REMAINING: ~175k for conversation — comfortable

File Reading Prioritization

When you must read files, prioritize by impact:

PRIORITY 1 — Files you will modify
Read fully. You need exact content to make correct edits.

PRIORITY 2 — Files with interfaces/contracts you must satisfy
Read the interface/base class. Skip implementation details.

PRIORITY 3 — Files for reference patterns
Use get_public_api first. Only read if the API surface isn't enough.

PRIORITY 4 — Files for general context
Use subagent to summarize. Don't read in main context.

NEVER READ:
- Entire directories "to understand the project" — use get_project_graph
- Test files for context (unless modifying tests) — use get_test_coverage_map
- Generated files (.designer.cs, migrations) — use get_diagnostics for issues
- Package/config files unless specifically needed

Context Pruning & Large Codebase Strategy

WARNING SIGNS (take action if any apply):
- Read 10+ files, 50+ exchanges, forgetting earlier details, re-reading files

RECOVERY: Summarize in 5-10 lines → subagents for remaining exploration →
MCP-only for new lookups → reference prior line numbers → suggest new session if needed

LARGE CODEBASES (50+ projects):
get_project_graph → identify 2-3 relevant projects → find_symbol for key types →
get_public_api for interfaces → read ONLY files you'll modify → subagents for cross-cutting

NEVER: Read every file to "understand" a project, load all skills upfront,
open a file to find one function (use find_symbol)

Anti-patterns

Reading Entire Files for One Function

// BAD — read 1500 tokens to find one 10-line method
Read: src/Orders/OrderService.cs (full file, 80 lines)
*Only needed the ProcessOrder method on line 42*

// GOOD — targeted approach
MCP: find_symbol "ProcessOrder" → "src/Orders/OrderService.cs:42"
Read: src/Orders/OrderService.cs lines 42-55 → ~200 tokens

Loading All Skills Upfront

// BAD — dump 15 skills into context at session start
"Load: modern-csharp, ef-core, minimal-api, testing, docker,
 authentication, logging, caching, messaging, resilience..."
*15 skills × ~300 tokens each = ~4500 tokens before any work starts*

// GOOD — load skills as topics arise
Session start: modern-csharp (always relevant)
User asks about EF: load ef-core
User asks about tests: load testing
*Only pay for what you use*

Not Using Subagents for Exploration

// BAD — explore in main context, polluting the window
Read 12 files across 4 projects to understand auth flow
*~15,000 tokens consumed, all staying in context*

// GOOD — subagent explores, returns summary
Subagent: "Trace the authentication flow from login to token validation.
           Return: the flow as numbered steps with file:line references."
*~300 tokens in main context*

Loading Everything Because the Window Is Large

// BAD — "200k tokens is huge, let's load everything"
Read all 30 files in the Orders module
Read all 15 test files
Read the entire docker-compose.yml
Read all migration files
*80k tokens consumed before writing a single line of code*

// GOOD — minimum viable context
MCP: get_project_graph (solution shape)
MCP: find_symbol (locate target types)
Read: 2-3 files you'll actually modify
Subagent: summarize anything else you need
*~3k tokens consumed, 197k remaining for actual work*

Decision Guide

ScenarioRecommendation
Need to find where a type is definedfind_symbol — never grep
Need to understand a type's APIget_public_api — don't read the file
Need to modify a fileRead it fully — you need exact content
Need to understand project structureget_project_graph — don't browse directories
Need to explore unfamiliar codeSpawn a subagent — keep main context clean
Read more than 10 files in a sessionPause — switch to MCP + subagents
Context feels heavy or sluggishSummarize what you know, use subagents going forward
Large codebase (50+ projects)MCP-first, subagent-heavy, read only files you modify
User asks about a new topic mid-sessionLoad the relevant skill on demand, not in advance
Need to compare two approachesSubagent per approach, compare summaries

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.98%
按下载量换算59

Claude

27.61%
按下载量换算46

Cursor

20.41%
按下载量换算34

Gemini CLI

8.59%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill context-discipline 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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