Dev Workflow logo

Dev Workflow

OrganizationPopular
iflytek
dev-workflow

The complete development workflow for SkillHub contributors including local dev, staging validation, testing, and PR creation. Ensures agents follow the correct sequence of steps.

Overview

Publisheriflytek
Repositoryskillhub
Skill namedev-workflow
Stars
5.2K
Forks
841
Bundled files
Instructions only
LicenseApache-2.0
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 iflytek on GitHub. Read the source before you install it.

Installation

Install the Dev 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.
    https://github.com/iflytek/skillhub/tree/main/.agents/skills/dev-workflow
  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/iflytek/skillhub.git /tmp/skillhub
mkdir -p .claude/skills
cp -r /tmp/skillhub/.agents/skills/dev-workflow .claude/skills/dev-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dev 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 Dev 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 Dev 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.

Development Workflow Skill

Trigger

Use this skill when:

  • Starting local development
  • Running tests or validation
  • Preparing a pull request
  • Setting up the development environment
  • Working with parallel agent worktrees

Prerequisites

  • Java 21+ (java -version)
  • Maven wrapper (./mvnw in server/)
  • Node.js + pnpm
  • Docker + docker compose
  • gh CLI (for PR creation)
  • curl (for smoke tests and health checks)

Workflow Stages

Stage 1: Local Development (fast iteration)

One-command start:

bash
make dev-all          # Start full stack: Postgres, Redis, MinIO, scanner, backend, frontend
make dev-all-down     # Stop everything
make dev-all-reset    # Full reset (clears data volumes)
make dev-status       # Check service status

Access points:

  • Web UI: http://localhost:3000
  • Backend API: http://localhost:8080
  • Scanner: http://localhost:8000

Individual components:

bash
make dev              # Start dependency services only (Postgres, Redis, MinIO, scanner)
make dev-server       # Start backend in foreground (blocking)
make dev-web          # Start Vite dev server (HMR enabled)
make dev-server-restart # Restart backend process
make dev-down         # Stop dependency services
make dev-logs         # View backend logs (use SERVICE=frontend for frontend logs)

Backend development: After editing Java code, run make dev-server-restart.

Frontend development: Vite HMR enabled — save a file for instant browser updates.

Scanner: The security scanner is enabled by default in dev. Health checked at http://localhost:8000/health.

Stage 2: Testing

CommandScopeNotes
make test-backend-appBackend unit testsskillhub-app + dependencies (-am)
make test-backendAll backend modulesAll modules via ./mvnw test
make test-frontendFrontend unit testsVitest (pnpm run test)
make test-e2e-frontendFrontend E2E testsPlaywright
make test-e2e-smoke-frontendFrontend E2E smokePlaywright subset
make typecheck-webTypeScript type checktsc --noEmit
make lint-webESLint checkFrontend linting

Important: Never run ./mvnw -pl skillhub-app clean test directly under server/. Use -am or Makefile targets to include dependent modules.

Stage 3: Staging Regression (pre-PR validation)

bash
make staging          # Build backend Docker image + frontend static + smoke test
make staging-down     # Tear down
make staging-logs     # View backend logs
SERVICE=web make staging-logs  # View Nginx logs

Staging validates the containerized deployment path:

  • Backend: built as Docker image from local source (Dockerfile.dev)
  • Frontend: built as static files (pnpm build), served by Nginx
  • Dependencies: same Postgres/Redis/MinIO as local dev
  • Smoke test runs against staging via scripts/smoke-test.sh

Staging URLs:

  • Web UI: http://localhost
  • Backend API: http://localhost:8080

Staging credentials (for bootstrap admin):

  • Username: admin
  • Password: Admin@staging2026

Stage 4: Pull Request

bash
make pr               # Push branch + create PR (requires gh CLI)

Requirements:

  • gh CLI installed and authenticated
  • Not on main/master branch
  • All changes committed (will prompt if uncommitted changes exist)

Useful Commands

CommandDescription
make generate-apiRegenerate OpenAPI types from running backend
make namespace-smokeNamespace workflow smoke test
make db-resetReset database only (Flyway migrate)
make validate-release-configValidate release env vars (.env.release)
./scripts/smoke-test.shBasic API smoke test (health, auth, labels)
./scripts/namespace-smoke-test.shNamespace CRUD + membership smoke test
./scripts/check-openapi-generated.shVerify OpenAPI types are not stale
make parallel-init TASK=nameCreate parallel worktree for agent

Mock Auth Users

User IDRoleHeader
local-userRegular userX-Mock-User-Id: local-user
local-adminSuper adminX-Mock-User-Id: local-admin

Bootstrap admin (local profile):

  • Username: admin
  • Password: ChangeMe!2026

Smoke Test Coverage

scripts/smoke-test.sh validates:

  1. Health endpoint (/actuator/health → 200)
  2. Prometheus metrics (/actuator/prometheus → 200)
  3. Namespaces API (/api/v1/namespaces → 200)
  4. Auth required (/api/v1/auth/me → 401 without session)
  5. User registration flow (with CSRF)
  6. Auth me with session
  7. Password change
  8. Logout + verify 401 after
  9. Admin login
  10. Label CRUD (admin only)

Additional smoke tests:

  • scripts/namespace-smoke-test.sh — Namespace creation, membership, publishing
  • scripts/governance-smoke-test.sh — Governance and moderation
  • scripts/promotion-smoke-test.sh — Skill promotion between scopes

Parallel Agent Workflow

For parallel agent development with isolated worktrees:

bash
make parallel-init TASK=feature-name           # Create worktree
make parallel-sync SOURCES="feat1 feat2"       # Merge feature branches
make parallel-up SOURCES="feat1 feat2"         # Merge + start dev environment
make parallel-down                             # Stop parallel environment

See docs/13-parallel-workflow.md for full details.

Commit Style

Use conventional commit format:

<type>(<scope>): <description>

Examples:

feat(auth): add local account login
fix(publish): resolve null pointer when skill metadata is missing name
docs(deploy): clarify runtime image usage
test(namespace): add membership service edge case tests
refactor(review): extract query repository for governance list
chore(ci): add parallel workflow scripts

Common Issues

IssueSolution
Backend won't startCheck Java version (java -version), must be 21+
Port 8080 in uselsof -i :8080 to find and kill the process
Maven download timeoutConfigure mirror in ~/.m2/settings.xml
Frontend won't startRun make web-deps to ensure node_modules exist
Staging build failsCheck Dockerfile.dev and ensure Maven build succeeds first
CSRF errors in testsEnsure cookie jar is shared and CSRF token refreshed after login

Frequently asked questions

What does the Dev Workflow AI skill do?

The complete development workflow for SkillHub contributors including local dev, staging validation, testing, and PR creation. Ensures agents follow the correct sequence of steps.

Why use Dev Workflow on TypingMind?

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

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

Which AI models can use Dev 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 Dev Workflow?

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

Is the Dev Workflow AI skill free?

Yes. It is published on GitHub by iflytek under the Apache-2.0 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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