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AI Dev Workflow (tkhongsap)

ai-dev-workflow-tkhongsap · tkhongsap/ai-dev-workflow · ★ 1 · last commit 2026-03-02

PRD-driven development workflow + multi-specialist code review system with GitLab MR integration, designed for enterprise teams mentoring junior developers.

Best whenAI must ask clarifying questions before writing a PRD and must never start implementing — keeping humans in the requirements loop.
Skip ifStarting implementation before spec review, AI inferring requirements without clarification
vs seeds
spec-kit(Archetype 2 — spec workflow + specialist skills) but adds GitLab MR auto-posting via review-publisher (unique in this b…
Primitive shape 29 total
Commands 4 Skills 25
00

Summary

AI Dev Workflow (tkhongsap) — Summary

AI Developer Workflow Collection by Teerasak Khongsap is an enterprise-grade dual-workflow markdown system for AI-assisted development: a PRD-Driven Workflow (requirements → tasks → implementation) and a Review-Driven Workflow (multi-specialist code review with GitLab integration). The repo ships 25+ skill files under .claude/skills/ covering both workflows, plus commands and agents for specialized orchestration. The PRD workflow uses three sequential markdown files (01-create-prd.md, 02-generate-tasks.md, 03-process-task-list.md) that each specify a stage's instructions; the review workflow uses 8 specialist reviewer skills (code-quality, security, performance, API design, educational MR review) coordinated by a planner+executor pattern. The framework is AI-tool-agnostic (described as working with "Claude, ChatGPT, and Cursor") and explicitly designed for team onboarding and GitLab integration. Bilingual (English + Thai), 1 star, Apache-2.0 license.

Differs from seeds: Most similar to spec-kit (Archetype 2 — spec workflow with command surface + specialist skills) but operates via plain markdown invocation rather than slash commands. Unlike taskmaster-ai (JSON task file + MCP), this is pure markdown with no CLI. The multi-specialist parallel reviewer pattern (5 domain specialists) is closer to bmad-method's persona approach but focused on code review rather than feature authoring. The GitLab automation integration is unique in the batch.

01

Overview

AI Dev Workflow (tkhongsap) — Overview

Origin

By Teerasak Khongsap. 1 star, Apache-2.0 license, last commit 2026-03-02. Bilingual: English and Thai (README-TH.md).

Mission (verbatim)

"Enterprise-grade AI-powered workflows for automating software development processes, from requirements to deployment." "Built specifically for AI assistants like Claude, ChatGPT, and Cursor" "Transform ad-hoc development into systematic, repeatable workflows" "Perfect for onboarding junior developers and maintaining standards"

Two workflow systems

1. PRD-Driven Workflow

Transform ideas → implementation via structured requirements:

Idea → Create PRD → Generate Tasks → Process Tasks → Implementation

Components: 01-create-prd.md, 02-generate-tasks.md, 03-process-task-list.md (also Replit variants)

2. Review-Driven Workflow

Multi-specialist code review with GitLab integration:

Code Changes → Branch Review → {5 Specialists} → Comprehensive Report → GitLab Feedback

Specialists: Code Quality, Security, Performance, API Design, Educational (for junior devs)

Distinguishing philosophy

  • Clarifying-questions-first PRD: Before generating the PRD, the AI must ask clarifying questions; it cannot proceed without them.
  • Junior developer target: PRDs are explicitly written for junior developers who may lack context.
  • Educational reviews: The mr-reviewer specialist provides mentoring-style feedback, not just bug detection.
  • GitLab automation: MR comments are posted directly via GitLab API after review.
02

Architecture

AI Dev Workflow (tkhongsap) — Architecture

Distribution

standalone-repo — clone and use via AI chat invocation.

Install

git clone https://github.com/tkhongsap/ai-dev-workflow
# Copy workflow files to your project, or reference directly

Directory structure

.
├── .claude/
│   ├── agents/                    (not confirmed — may exist)
│   ├── commands/
│   │   ├── branch-review.md
│   │   ├── context.md
│   │   ├── extract-issues.md
│   │   └── parallel.md
│   └── skills/
│       ├── prd-creator.md
│       ├── prd-task-generator.md
│       ├── prd-task-processor.md
│       ├── review-api-reviewer.md
│       ├── review-executor.md
│       ├── review-mr-reviewer.md
│       ├── review-performance-reviewer.md
│       ├── review-planner.md
│       ├── review-publisher.md
│       ├── review-security-reviewer.md
│       ├── review-task-generator.md
│       ├── system-prompt.md
│       ├── [17 domain skills — mdx, xlsx, pdf, pptx, etc.]
│       └── [theme-factory, webapp-testing, slack-gif-creator, etc.]
├── .cursor/                       (Cursor rules)
├── prd-driven-workflow/
│   ├── 01-create-prd.md
│   ├── 01-create-prd-replit.md
│   ├── 02-generate-tasks.md
│   ├── 02-generate-tasks-replit.md
│   ├── 03-process-task-list.md
│   ├── 03-process-tasks-replit.md
│   └── README.md
├── review-driven-workflow/
│   ├── (reviewer docs)
│   └── README.md
├── CLAUDE.md
├── README.md
└── README-TH.md

Target AI tools

Claude Code (primary, .claude/ directory), Cursor (.cursor/ rules), ChatGPT (markdown invocation).

Runtime requirements

None for the workflow files themselves. GitLab API access for the review publisher.

03

Components

AI Dev Workflow (tkhongsap) — Components

Skills (~25 in .claude/skills/)

PRD workflow skills

Name Purpose
prd-creator.md PRD creation (clarifying questions → document)
prd-task-generator.md Task generation from PRD
prd-task-processor.md Task execution and management

Review workflow skills

Name Purpose
review-planner.md Review orchestration — decides which reviewers to invoke
review-task-generator.md Generates review tasks
review-executor.md Executes review tasks
review-api-reviewer.md RESTful API design analysis
review-security-reviewer.md Security vulnerability analysis (OWASP Top 10)
review-performance-reviewer.md Performance bottleneck analysis
review-mr-reviewer.md Educational MR review for junior devs
review-publisher.md Posts review to GitLab MR

Domain/utility skills (17+)

Category Names
Document processing pdf, docx, xlsx, pptx
Design artifacts-builder, brand-guidelines, canvas-design, theme-factory
Communication internal-comms, slack-gif-creator
Development webapp-testing, mcp-builder, skill-creator
Localization english-to-thai-cultural-translation
Template template-skill

Commands (4 in .claude/commands/)

Name Purpose
branch-review.md Trigger branch review workflow
context.md Context management
extract-issues.md Extract GitLab issues → branch mapping
parallel.md Parallel execution coordination

Workflow files (6 in prd-driven-workflow/)

Name Purpose
01-create-prd.md PRD creation instruction (asks clarifying questions first)
02-generate-tasks.md Task generation from PRD
03-process-task-list.md Task execution
*-replit.md variants Replit-specific versions of each
05

Prompts

AI Dev Workflow (tkhongsap) — Prompts

Prompt 1: PRD creation rule (prd-driven-workflow/01-create-prd.md excerpt)

Source: prd-driven-workflow/01-create-prd.md

Technique: Clarifying-questions-first guard + junior-developer target framing

# Rule: Generating a Product Requirements Document (PRD)

## Goal
To guide an AI assistant in creating a detailed PRD in Markdown format, based on an initial user prompt. The PRD should be clear, actionable, and suitable for a junior developer to understand and implement the feature.

## Process
1. **Receive Initial Prompt**
2. **Ask Clarifying Questions**: The AI *must* ask clarifying questions to gather sufficient detail. Make sure to provide options in letter/number lists so I can respond easily.
3. **Generate PRD**: Based on the prompt and answers.
4. **Save PRD**: Save as `prd-[feature-name].md` inside the `/tasks` directory.

## Target Audience
Assume the primary reader of the PRD is a **junior developer**. Requirements should be explicit, unambiguous, and avoid jargon where possible.

## Final instructions
1. Do NOT start implementing the PRD
2. Make sure to ask the user clarifying questions
3. Take the user's answers to the clarifying questions and improve the PRD

Key design: The "Do NOT start implementing" and mandatory clarifying-questions rules are the strongest anti-vibe-coding constraints in this batch.


Prompt 2: Vibe coding vs SDD comparison (from README)

Technique: Comparison table highlighting failure modes

Vibe Coding Spec Driven Development
Who decides requirements AI infers them You define them upfront
Control Low — AI leads High — you lead
Failure mode Spaghetti code you don't fully understand Over-specified, rigid if requirements change
Debugging approach Ask AI to fix it Check against spec to find drift

Note: This is similar to avinashmyerolkar's SDD notes — may indicate a shared community-level consensus on how to frame spec-driven development.

09

Uniqueness

AI Dev Workflow (tkhongsap) — Uniqueness

Differs from seeds

Most similar to spec-kit (Archetype 2 — spec workflow + specialist skills) but with key differences: (1) GitLab automation via review-publisher is unique in this batch — no other framework posts results to a VCS platform automatically; (2) the mandatory clarifying-questions constraint in PRD creation is the strongest anti-vibe-coding guard in the batch; (3) the educational MR reviewer for junior developer mentoring is not present in any seed. Unlike bmad-method (persona-based skills for feature authoring), this framework's specialists are all code-review focused. The bilingual Thai/English documentation suggests a Southeast Asian enterprise dev team context.

Positioning

Enterprise team workflow automation with GitLab integration. Designed for teams that have standardized on GitLab and want consistent, multi-perspective code review with educational mentoring for junior developers.

Observable failure modes

  1. GitLab only: The review-publisher integration is GitLab-specific; GitHub users would need to adapt.
  2. 1 star, low adoption: Minimal community validation; unknown production battle-testing.
  3. Auto-apply described, not enforced: The file-type-based auto-apply rules are described in documentation but not implemented via hooks.
  4. Clarifying questions as bottleneck: The mandatory question phase slows rapid iteration; teams under deadline pressure may bypass it.
  5. Replit variants: Having *-replit.md variants for each workflow file suggests platform-specific adaptation is needed — the base workflow may not be universally portable.
04

Workflow

AI Dev Workflow (tkhongsap) — Workflow

Workflow 1: PRD-Driven Development

Phase File Artifact Gate
Receive idea User prompt Brief description
Clarifying questions 01-create-prd.md Q&A exchange AI must ask before writing
Generate PRD 01-create-prd.md tasks/prd-[feature].md Human review implied
Generate tasks 02-generate-tasks.md Task list in /tasks/
Process tasks 03-process-task-list.md Implementation Per-task verification

PRD creation rule (verbatim)

"Make sure to ask the user clarifying questions. Take the user's answers to the clarifying questions and improve the PRD. Do NOT start implementing the PRD."

PRD output location

/tasks/prd-[feature-name].md — uses /tasks/ directory convention.

Workflow 2: Review-Driven Development

Phase Trigger Output
Extract issues @extract-issues issues-extraction-[date].md
Branch review @branch-review #123 or @branch-review branch-name Invokes specialist reviewers
Specialist reviews review-planner → 5 specialists Parallel/sequential analysis
Publish review-publisher → GitLab API MR comment with comprehensive report

Review output location

/workflow/review-[issue-num]-[date].md

Auto-applies patterns (from review-driven workflow README)

  • Code reviewers auto-apply to: *.py, *.ts, *.tsx, *.js, *.jsx
  • Security reviewers auto-apply to: code files, Docker files, env configs
  • API reviewers auto-apply to: **/api/**, **/routes/**, **/endpoints/**
06

Memory Context

AI Dev Workflow (tkhongsap) — Memory & Context

State files

  • CLAUDE.md — project-level persistent instructions
  • /tasks/prd-[feature-name].md — PRD artifacts written to project /tasks/ directory
  • /workflow/review-[issue-num]-[date].md — review artifacts

Persistence mechanism

File-based. PRD documents and review reports are written to the project filesystem and serve as cross-session reference.

GitLab integration

  • extract-issues.md command generates issues-extraction-[date].md mapping GitLab issues to branches
  • review-publisher posts review results to GitLab MR comments (external state)

No database or vector memory.

07

Orchestration

AI Dev Workflow (tkhongsap) — Orchestration

Multi-agent

Yes — the review workflow uses a planner + 5 specialist reviewers + executor + publisher pattern.

Orchestration pattern

hierarchicalreview-planner.md coordinates the specialist reviewers; review-publisher.md aggregates and posts results.

Subagent definition format

skill-md — specialist reviewers are skill files with role descriptions.

Spawn mechanism

Manual invocation via commands (@branch-review) or the planner skill orchestrating other skills.

PRD workflow orchestration

sequential — three phases (create PRD → generate tasks → process tasks).

Isolation mechanism

none — all operations are in-place.

Multi-model

No explicit model configuration.

Execution mode

interactive-loop — PRD workflow requires user clarification before proceeding.

Cross-tool portability

Medium — .claude/ directory for Claude Code, .cursor/ rules for Cursor, plain markdown workflow files for any AI.

Auto-validators

File-type-based auto-apply rules (described in README but not enforced via hooks):

  • Code reviewers for *.py, *.ts, *.tsx, *.js, *.jsx
  • Security reviewers for Docker/env files
08

Ui Cli Surface

AI Dev Workflow (tkhongsap) — UI & CLI Surface

Dedicated CLI binary

None.

Local web dashboard

None.

IDE integration

  • Claude Code (.claude/commands/, .claude/skills/)
  • Cursor (.cursor/ rules directory)

GitLab integration

review-publisher skill posts MR comments via GitLab API — the only external API integration in the review workflow.

Observability

File-based artifacts: review reports written to /workflow/ directory, issue extractions to project root.

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