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Workflow Mastery

Organization
codewithmukesh
workflow-mastery

Claude Code workflow mastery for .NET developers. Covers parallel execution with git worktrees, plan mode strategy, verification loops, auto-formatting hooks, permission setup for dotnet CLI, prompting techniques, subagent patterns, and context discipline — token budget management, MCP-first navigation, lazy loading, and subagent isolation — all adapted for the .NET ecosystem. Load this skill when setting up Claude Code for a .NET project, optimizing workflows, running parallel sessions, when context is running low or sessions feel sluggish, when exploring a large codebase efficiently, or when the user mentions "productivity", "workflow", "parallel", "worktree", "plan mode", "permissions", "hooks", "10x", "setup Claude Code", "speed up development", "context", "tokens", "budget", "running out of context", "too many files", or "large codebase". Inspired by tips from Boris Cherny (creator of Claude Code) and the Anthropic team.

Overview

Publishercodewithmukesh
Repositorydotnet-claude-kit
Skill nameworkflow-mastery
Stars
721
Forks
170
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by codewithmukesh on GitHub. Read the source before you install it.

Installation

Install the Workflow Mastery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/codewithmukesh/dotnet-claude-kit.git /tmp/dotnet-claude-kit
mkdir -p .claude/skills
cp -r /tmp/dotnet-claude-kit/skills/workflow-mastery .claude/skills/workflow-mastery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workflow Mastery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Workflow Mastery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Workflow Mastery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Workflow Mastery for .NET

Core Principles

  1. Parallel over sequential — Run 3-5 Claude sessions simultaneously using git worktrees. Build a feature in one, fix a bug in another, run tests in a third. The single biggest productivity unlock.
  2. Plan then execute — For any non-trivial task, start in plan mode, iterate until the plan is bulletproof, then switch to auto-accept. A good plan means Claude 1-shots the implementation.
  3. Verification closes the loop — Give Claude a way to prove its work: dotnet build, dotnet test, get_diagnostics via MCP. This single practice 2-3x the quality of the output.
  4. Context is a budget, not a dumping ground — The context window fills fast: a typical .cs file is 500-2000 tokens, and 50 file reads can burn a large share of the budget. Spend tokens like sprint capacity — deliberately.
  5. Automate the repetitive — If you do it more than once a day, make it a hook, a slash command, or a subagent. Pre-allow safe permissions. Eliminate friction.
  6. Compound your knowledge — Every correction becomes a rule in MEMORY.md (see instinct-system skill). Every PR review adds a learning. Over time, Claude's mistake rate drops because your project's knowledge base grows.

Patterns

Parallel Sessions with Git Worktrees

The biggest productivity multiplier. Each worktree gets its own Claude session, its own files, zero conflicts.

bash
# Create worktrees for parallel work
git worktree add ../my-project-feature origin/main
git worktree add ../my-project-bugfix origin/main
git worktree add ../my-project-tests origin/main

# Start Claude in each (separate terminal tabs)
cd ../my-project-feature && claude
cd ../my-project-bugfix && claude
cd ../my-project-tests && claude

Practical .NET workflow:

WorktreeTaskClaude Session
featureBuild new endpoint + handlerMain development
bugfixFix the failing CI testAutonomous bug fix
testsWrite integration tests for existing featureTest generation
analysisQuery the Roslyn MCP, read logs, review architectureRead-only research

Tips:

  • Name your terminal tabs by task so you never lose track
  • Use shell aliases (alias zf='cd ../my-project-feature') for one-keystroke switching
  • Enable terminal notifications so you know when a session needs input

Auto-Format Hook for .NET

Catch formatting issues on every file write — eliminates the "CI failed on formatting" loop.

json
// .claude/settings.json
{
  "hooks": {
    "PostToolUse": [
      {
        "matcher": "Write|Edit",
        "hooks": [
          {
            "type": "command",
            "command": "dotnet format --include \"$CLAUDE_FILE_PATH\" --no-restore 2>/dev/null || true"
          }
        ]
      }
    ]
  }
}

Why || true: The hook should never block Claude's workflow. If formatting fails (e.g., on a non-C# file), silently continue.

Pre-Allow Safe .NET Permissions

Stop clicking "allow" for every dotnet command. Add these to .claude/settings.json:

json
{
  "permissions": {
    "allow": [
      "Bash(dotnet build *)",
      "Bash(dotnet test *)",
      "Bash(dotnet run *)",
      "Bash(dotnet ef *)",
      "Bash(dotnet format *)",
      "Bash(dotnet restore *)",
      "Bash(dotnet pack *)",
      "Bash(dotnet tool *)"
    ]
  }
}

Check this into git so the whole team gets frictionless workflows.

Plan Mode Strategy

For any task touching 3+ files or involving architecture decisions:

Step 1: Enter plan mode (Shift+Tab twice)
Step 2: Describe the task with full context
Step 3: Iterate on the plan — challenge assumptions, ask "what about edge cases?"
Step 4: Once the plan is solid, switch to normal mode
Step 5: Claude executes with auto-accept — often 1-shots the implementation

Advanced pattern: Have one Claude write the plan, then spin up a second Claude session to review it as a staff engineer:

"Review this plan as a staff .NET engineer. Challenge every assumption.
What could go wrong? What's missing? What would you do differently?"

When things go sideways: The moment implementation deviates from the plan, STOP. Don't push through. Switch back to plan mode, understand what changed, re-plan, then resume.

Verification Loop for .NET

For the full 7-phase verification pipeline (build, diagnostics, anti-patterns, tests, security, format, diff review) with structured PASS/FAIL reporting, see the verify skill.

Boris's #1 tip: "Give Claude a way to verify its work." The short version: always tell Claude to run dotnet build, dotnet test, get_diagnostics, and dotnet format --verify-no-changes before declaring done. The verify skill has the complete pipeline with short-circuit rules and report templates.

Compounding Knowledge via Corrections

For the full correction capture system — detection, generalization, categorized storage, and periodic audits — see the instinct-system skill. The short version: after every correction, capture a generalized rule in MEMORY.md so the same mistake never recurs.

Prompting Techniques for .NET

Challenge Claude's work:

"Grill me on these changes. Would this pass a staff .NET engineer's code review?
Check for: N+1 queries, missing CancellationToken, exposed domain entities,
missing validation, incorrect service lifetimes."

Demand proof:

"Prove this works. Run the tests, show me the output.
Then diff the API response between main and this branch."

After a mediocre fix:

"Knowing everything you know now, scrap this and implement the elegant solution.
No hacks, no workarounds."

For EF Core migrations:

"Generate the migration, then show me the raw SQL it produces.
I want to verify the migration before applying it."

Subagent Patterns for .NET

The kit ships 10 specialist agents — route to them before writing your own: dotnet-architect, code-reviewer, refactor-cleaner, test-engineer, security-auditor, build-error-resolver, ef-core-specialist, api-designer, performance-analyst, devops-engineer. Each carries pre-loaded skills and domain context a generalist session lacks.

Use them:

"Run the code-reviewer agent on my changes before I create the PR."
"Have refactor-cleaner simplify the files I just modified."
"Send the failing CI log to build-error-resolver."

For workflows the kit does not cover, create project-specific subagents in .claude/agents/ — a markdown file with a role, a numbered job list, and a required report format (PASS with summary / FAIL with specifics). Keep one concern per agent so its output stays reviewable.

When to offload vs. stay in main context: see Context Discipline below — subagents are also your context isolation chambers, not just task runners.

Context Discipline

The rules in .claude/rules/agents.md already mandate MCP-first navigation (find_symbol before file reads, get_diagnostics over builds). This section is the strategy layer on top: how to budget, when to offload, and how to recover.

Token Economics

A Roslyn MCP query costs 30-150 tokens; a file read costs 500-2000+. To understand OrderService, four MCP calls (find_symbolget_public_apifind_referencesget_type_hierarchy) cost ~310 tokens; reading the four related files costs ~2900. Then read only the method you'll modify. Reserve full file reads for files you are about to edit.

Subagent Offloading Decision Matrix

OFFLOAD TO A SUBAGENT WHEN:
- Exploring unfamiliar code (> 3 files to read)
- Research requiring docs or multiple files
- Verbose output (test runs, diagnostics, comparisons)
- 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)
- Work that builds on the ongoing conversation with the user

Ask subagents for compressed answers: "Trace the auth flow from login to token validation. Return numbered steps with file:line references." You get ~300 tokens of findings instead of 15k tokens of raw files.

File Reading Prioritization

PRIORITY 1 — Files you will modify: read fully (exact content needed for edits)
PRIORITY 2 — Contracts you must satisfy: read the interface, skip implementations
PRIORITY 3 — Reference patterns: get_public_api first, read only if insufficient
PRIORITY 4 — General context: subagent summarizes; never read in main context

NEVER READ: entire directories (get_project_graph), test files for context
(get_test_coverage_map), generated files/migrations, configs unless needed

Budget Planning and Recovery

Before a complex task, sketch the spend: understand ~5k (MCP + subagent), plan ~2k, implement ~15k (read targets + write + iterate), verify ~3k — leaving the bulk of the window for conversation.

WARNING SIGNS: 10+ files read, 50+ exchanges, forgetting earlier details,
re-reading files you already saw

RECOVERY: summarize what you know in 5-10 lines → subagents for remaining
exploration → MCP-only lookups → suggest a fresh session if still degraded

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

Lazy Skill Loading

Don't front-load skills "just in case" — 15 skills at ~300 tokens each is ~4500 tokens spent before any work starts. Load modern-csharp at session start if relevant; pull ef-core, testing, etc. the moment the topic actually arises.

Anti-patterns

Don't Skip Plan Mode for Complex Tasks

// BAD — dive straight into a multi-file refactor
"Refactor the Orders module to use DDD with aggregates and value objects"
*Claude modifies 15 files, misses half the invariants, tangles the migration*

// GOOD — plan first, execute after
"Enter plan mode. I want to refactor the Orders module to use DDD.
Let's plan which files change, what the aggregate boundary is,
how value objects map to EF Core, and what the migration strategy is."

Don't Work in a Single Session When You Could Parallelize

// BAD — sequential work in one session
1. Build feature       (20 min)
2. Write tests         (15 min)
3. Fix formatting      (5 min)
4. Update docs         (10 min)
Total: 50 minutes

// GOOD — parallel worktrees
Worktree 1: Build feature     (20 min)
Worktree 2: Write tests       (15 min, started simultaneously)
Worktree 3: Update docs       (10 min, started simultaneously)
Total: ~20 minutes (wall clock)

Don't Accept the First Solution

// BAD — accept mediocre code
Claude: "Here's the implementation" *generic, works but not great*
You: "Looks good, ship it"

// GOOD — push for quality
Claude: "Here's the implementation"
You: "Would a staff .NET engineer approve this?
      What about the service lifetime? Is this N+1 safe?
      Is there a more elegant way using C# 14 features?"

Don't Load Everything Because the Window Is Large

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

// GOOD — minimum viable context
MCP: get_project_graph (solution shape) + find_symbol (locate targets)
Read: the 2-3 files you'll actually modify
Subagent: summarize anything else
*~3k tokens consumed, the rest free for actual work*

Decision Guide

ScenarioRecommendation
Task touches 3+ filesPlan mode first
Task is a simple bug fixJust fix it, verify with dotnet test
Need to build + test + review3 parallel worktrees
CI keeps failing on formatAdd PostToolUse format hook
Tired of permission promptsPre-allow dotnet * commands
Claude made a mistake"Update CLAUDE.md so you don't make that mistake again"
Code feels hacky"Knowing everything you know now, implement the elegant solution"
Want to verify architectureSpin up a second session as staff reviewer
Repetitive PR workflowRoute to kit agents (code-reviewer, refactor-cleaner) or create a project subagent
Learning a new codebaseUse "Explanatory" output style via /config
Need a type's API or locationget_public_api / find_symbol — don't read the file
Need to modify a fileRead it fully — exact content required
Exploring unfamiliar codeSpawn a subagent — keep main context clean
10+ files read in a sessionPause — switch to MCP + subagents
Context feels heavy or sluggishSummarize what you know, subagents going forward
Large codebase (50+ projects)MCP-first, subagent-heavy, read only files you modify
New topic mid-sessionLoad the relevant skill on demand, not in advance

Frequently asked questions

What does the Workflow Mastery AI skill do?

Claude Code workflow mastery for .NET developers. Covers parallel execution with git worktrees, plan mode strategy, verification loops, auto-formatting hooks, permission setup for dotnet CLI, prompting techniques, subagent patterns, and context discipline — token budget management, MCP-first navigation, lazy loading, and subagent isolation — all adapted for the .NET ecosystem. Load this skill when setting up Claude Code for a .NET project, optimizing workflows, running parallel sessions, when context is running low or sessions feel sluggish, when exploring a large codebase efficiently, or w...

Why use Workflow Mastery on TypingMind?

Because you install it once and use it with any model. Workflow Mastery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Workflow Mastery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codewithmukesh/dotnet-claude-kit/tree/main/skills/workflow-mastery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Workflow Mastery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Workflow Mastery?

As many as you like. As long as a model supports skills, you can use Workflow Mastery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Workflow Mastery AI skill free?

Yes. It is published on GitHub by codewithmukesh under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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