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Agent Skill · 研究检索

context-discipline(语境纪律)

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。

context-discipline 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 54,235。

总安装 514周安装 21GitHub Stars 315下载 165核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/context-discipline

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill conte…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

context-discipline 怎么安装?

在终端执行 npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill context-discipl…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

context-discipline 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

context-discipline 需要什么权限?

权限级别是执行命令。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。

context-discipline 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

context-discipline 用的人多吗?

公开数据是累计安装 514 次,近一周安装 21 次,下载 165 次,GitHub 315 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

context-discipline 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
context-discipline

语境纪律 · 本页

514315执行命令未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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。目前只有一个公开来源,建议同时核对源仓库。

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