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Core Workflow

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travisjneuman
core-workflow

Detailed development workflow patterns, checklists, and standards. Auto-loads for complex tasks, planning, debugging, testing, or when explicit patterns are needed. Contains session protocols, git conventions, security checklists, testing strategy, and communication standards.

Overview

Publishertravisjneuman
Repository.claude
Skill namecore-workflow
Stars
98
Forks
22
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 travisjneuman on GitHub. Read the source before you install it.

Installation

Install the Core Workflow 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/travisjneuman/.claude.git /tmp/.claude
mkdir -p .claude/skills
cp -r /tmp/.claude/skills/core-workflow .claude/skills/core-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Core Workflow 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 Core Workflow 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 Core Workflow 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.

Core Workflow Patterns

Comprehensive development workflow reference. This loads on-demand for detailed patterns.

Quick links to rules: ~/.claude/rules/ for stack-specific and task-type checklists.


Session Protocol

Start Checklist

bash
# 1. Sync with remote (ALWAYS FIRST)
git fetch origin main && git merge origin/main --no-edit
# or: git fetch origin master && git merge origin/master --no-edit

# 2. Get context
git log -3

# 3. Check for existing work
ls tasks/*.md 2>/dev/null || echo "No active tasks"

CRITICAL: Check <env> section for today's date. NEVER guess dates.

End Checklist

bash
# 1. Verify
npm run test && npm run type-check  # or project equivalent

# 2. Archive completed work
mv tasks/<completed-task>.md .archive/completed-tasks/

# 3. Commit with comprehensive message
git add .
git commit  # See Git Conventions below
git push origin main

Stop dev server after testing: lsof -ti:PORT | xargs kill (or Windows equivalent)


GSD (Get Shit Done) - Multi-Phase Projects

For complex features spanning days/weeks, use GSD.

When to Use GSD

ComplexityUse GSD?Workflow
Simple fix (<30 min)NoDirect execution
Single feature (30min-2hr)NoTask file + TodoWrite
Multi-phase feature (days)YesGSD workflow
New project/appYesGSD from start

GSD Quick Start

bash
/gsd:new-project       # Initialize with brief + config
/gsd:create-roadmap    # Create phases and state tracking
/gsd:plan-phase 1      # Create detailed plan for phase
/gsd:execute-plan <path>  # Execute the plan

GSD Commands Reference

CommandPurpose
/gsd:progressCheck status, route to next action
/gsd:resume-workResume from previous session
/gsd:pause-workCreate handoff when pausing
/gsd:plan-phase <n>Create detailed phase plan
/gsd:execute-plan <path>Execute a PLAN.md
/gsd:add-phase <desc>Add phase to roadmap
/gsd:insert-phase <after> <desc>Insert urgent work
/gsd:complete-milestone <ver>Archive and tag release
/gsd:helpFull command reference

GSD File Structure

.planning/
├── PROJECT.md          # Vision and requirements
├── ROADMAP.md          # Phase breakdown
├── STATE.md            # Project memory (context accumulation)
├── config.json         # Workflow mode (interactive/yolo)
└── phases/
    └── 01-foundation/
        ├── 01-01-PLAN.md
        └── 01-01-SUMMARY.md

Git Conventions

Commit Types

feat | fix | refactor | perf | test | docs | chore

Commit Message Format

type: Short summary (50 chars max)

## What Changed
- File X: Added feature Y
- File Z: Updated config for A

## Why
- User requested feature Y
- Config A needed update

## Testing
- All tests passing
- Manual verification done

Auto-Commit on Task Completion

When a task or plan is complete, automatically commit without being asked.

Pre-Commit Checks
bash
# 1. Check this is a user-owned repo (not external)
git remote get-url origin | grep -q "travisjneuman" && echo "OK: User repo"

# 2. Check push is not blocked
git remote get-url --push origin | grep -q "no_push" && echo "SKIP: External repo"
Rules
ConditionAction
User's own repoAuto-commit + push
External repo (no_push)Never commit - read-only
Submodule (external)Never commit - read-only
Uncommitted secrets detectedBlock - warn user

Security Checklist

Frontend

  • textContent not innerHTML
  • unknown type for external data
  • No exposed API keys
  • HTTPS for external requests
  • Input sanitization

Backend

  • Input validation on all endpoints
  • Auth guards on protected routes
  • Parameterized queries (no raw SQL)
  • Secrets in environment variables

Performance Targets

MetricTarget
Initial bundle<100KB gzipped
Page load<1s
Interaction latency<100ms
Lighthouse Performance95+
AccessibilityWCAG AA minimum

Accessibility (WCAG AA)

  • Semantic HTML structure
  • Alt text for meaningful images
  • Keyboard navigation (Tab, Enter, Escape)
  • Focus indicators visible
  • Color contrast >= 4.5:1
  • ARIA labels on interactive elements
  • Touch targets >= 44x44px

Testing Strategy

TypeLocationWhen
Unitsrc/**/__tests__/Every function
ComponentSameEvery component
Integrationtests/integration/Critical paths
E2Etests/e2e/Before release

Before committing: npm run test && npm run type-check


Thinking Frameworks

Use structured decision-making for complex choices:

Decision TypeFramework
Long-term implications/consider:10-10-10
Root cause analysis/consider:5-whys
Prioritization/consider:eisenhower-matrix
Innovation/consider:first-principles
Risk identification/consider:inversion
Simplification/consider:occams-razor
Focus/consider:one-thing
Tradeoffs/consider:opportunity-cost
Optimization/consider:pareto
Consequences/consider:second-order

Debugging Protocol

Standard Issues

  1. Reproduce the issue
  2. Read relevant code
  3. Identify root cause
  4. Fix + add test
  5. Verify fix

Intermittent/Complex Issues

Use debug-like-expert skill for systematic approach.


Build vs Buy Philosophy

We build features. We use utilities.

  • Build: All feature logic, business rules, UI/UX, data models
  • Use: Low-level abstractions (D3, Recharts, Lexical, Konva)
  • Criterion: We own the feature, library handles complexity

License Requirements

  • Must use: MIT, Apache 2.0, BSD
  • Never use: GPL, AGPL (blocks commercialization)

Communication Standards

Progress Updates

Give high-level updates, not spam:

✅ Added authentication middleware (3 files)
✅ Updated user store with new fields
⏳ Testing login flow...

When to Ask

Use AskUserQuestion when:

  • Requirements are ambiguous
  • Multiple valid architectures exist
  • Scope might expand
  • Design decisions need validation

Directness Protocol

  • Logic over feelings
  • Correctness over validation
  • Direct feedback over diplomacy
  • Best solution over agreement

Context Hygiene

Reduce Token Usage

  • Short, high-signal summaries over long logs
  • Don't @-embed large docs by default
  • Reference paths + when to read them
  • Use /clear after completing work units

Delegation Patterns

SituationAction
Context >100k tokensCreate prompt → delegate to fresh context
Moderate complexity/create-prompt/run-prompt
Multi-stage features/create-meta-prompt
Approaching limits/whats-next for handoff document

Quick Reference

Common Commands

bash
npm run dev          # Start dev server
npm run build        # Production build
npm run test         # Run tests
npm run type-check   # TypeScript check

File Naming

TypeConventionExample
ComponentsPascalCaseUserCard.tsx
Hooksuse prefixuseAuth.ts
UtilitiescamelCaseutils.ts
Tests.test.tsutils.test.ts

Resources

Official

Community


See Also

  • ~/.claude/docs/reference/checklists/ - Task-type specific checklists
  • ~/.claude/docs/reference/stacks/ - Stack-specific patterns
  • ~/.claude/docs/reference/tooling/ - Tool configuration guides
  • ~/.claude/skills/MASTER_INDEX.md - Full skills catalog
  • ~/.claude/agents/README.md - Agents directory

Frequently asked questions

What does the Core Workflow AI skill do?

Detailed development workflow patterns, checklists, and standards. Auto-loads for complex tasks, planning, debugging, testing, or when explicit patterns are needed. Contains session protocols, git conventions, security checklists, testing strategy, and communication standards.

Why use Core Workflow on TypingMind?

Because you install it once and use it with any model. Core Workflow 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 Core Workflow in TypingMind?

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

Which AI models can use Core Workflow?

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 Core Workflow?

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

Is the Core Workflow AI skill free?

Yes. It is published on GitHub by travisjneuman 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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