TaskFlow: Human-Agent Task Orchestration Platform

Human-Agent Task Orchestration Platform where humans and AI agents collaborate as equals.

One-liner: Your AI workforce — assign tasks to humans or agents, track everything, ship together.

This project fulfills all hackathon requirements while solving a real problem: fragmented work across projects with no unified visibility or agent collaboration.

Works directly with every web and local AI Coding Agent, SpecKitPlus, SpecKit, Google AntiGravity, and any other AI agent that implements the MCP protocol.

Why TaskFlow (The Real Problem)

Current State: Data Silos Everywhere

Silo	What's Trapped
Each GitHub repo	Specs, context, implementation details
Personal notes	Tasks that never become actionable
Chat with Claude	Context lost after each session
Team communication	Decisions buried in WhatsApp/Slack

TaskFlow Solution: Unified Orchestration Layer

┌─────────────────────────────────────────────────────────────────┐
│                         TASKFLOW                                 │
│                                                                  │
│   HUMANS                              AI AGENTS                  │
│   @muhammad                           @claude-code               │
│   @hammad                             @qwen                      │
│   @wania                              @gemini                    │
│                                                                  │
│   ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│   │  PROJECT:   │  │  PROJECT:   │  │  PROJECT:   │            │
│   │  taskflow   │  │  personal   │  │ robolearn │            │
│   │             │  │             │  │             │            │
│   │  Tasks      │  │  Tasks      │  │  Tasks      │            │
│   │  Subtasks   │  │  Subtasks   │  │  Subtasks   │            │
│   │  Audit logs │  │  Audit logs │  │  Audit logs │            │
│   └─────────────┘  └─────────────┘  └─────────────┘            │
│                                                                  │
│   Everything tracked. Everyone accountable. Full audit trail.   │
└─────────────────────────────────────────────────────────────────┘

What Makes TaskFlow Different

Traditional Task Manager	TaskFlow
Humans only	Humans AND Agents as first-class workers
Static assignment	Dynamic delegation (agent → agent)
Manual status updates	Agents report progress via MCP
Single project view	Cross-project visibility
Trust the artifact	Trust the audit trail
Personal OR work	Unified: "Message Ahmad" + "Implement MCP server"

The Killer Feature: Agent-to-Agent Delegation

Task: "Research authentication patterns"
Assigned to: @claude-code
│
├── Subtask: "Survey existing solutions"
│   Delegated to: @gemini (by @claude-code)
│   Status: ✅ Completed
│
├── Subtask: "Analyze security tradeoffs"
│   Delegated to: @qwen (by @claude-code)
│   Status: 🔄 In Progress (60%)
│
└── Subtask: "Draft recommendation"
    Kept by: @claude-code
    Status: ⏳ Blocked (waiting on analysis)

AUDIT TRAIL shows every delegation, every decision.

Data Model (Core Schema)

# === CORE ENTITIES ===

class Project(SQLModel, table=True):
    """Container for related tasks"""
    id: str                          # "taskflow", "personal"
    name: str
    description: str | None
    owner_id: str
    created_at: datetime

class Worker(SQLModel, table=True):
    """Both humans and agents are workers"""
    id: str                          # "@muhammad", "@claude-code"
    type: Literal["human", "agent"]
    name: str
    agent_type: str | None           # "claude", "qwen", "gemini"
    capabilities: list[str] | None   # ["coding", "research"]
    api_key_hash: str | None         # For agent authentication
    created_at: datetime

class Task(SQLModel, table=True):
    """Unit of work — can be assigned to human or agent"""
    id: int
    title: str
    description: str | None
    project_id: str
    
    # Assignment
    assignee_id: str | None          # "@claude-code", "@muhammad"
    created_by_id: str
    
    # Hierarchy
    parent_task_id: int | None       # For subtasks
    
    # Status
    status: Literal["pending", "in_progress", "review", "completed", "blocked"]
    progress: int = 0                # 0-100
    
    # Organization (Intermediate features)
    priority: Literal["low", "medium", "high", "urgent"] | None
    tags: list[str] | None
    due_date: datetime | None
    
    # Recurrence (Advanced features)
    recurrence: str | None           # "daily", "weekly", "monthly"
    
    # Timestamps
    created_at: datetime
    updated_at: datetime
    started_at: datetime | None
    completed_at: datetime | None

class LinkedResource(SQLModel, table=True):
    """Generic linking — anything to anything"""
    id: int
    owner_type: Literal["project", "task", "blueprint"]
    owner_id: str
    resource_type: str               # "repo", "spec", "doc", "url", "task"
    resource_uri: str
    name: str
    description: str | None
    access: Literal["read", "write"] = "read"
    created_by: str
    created_at: datetime

class Blueprint(SQLModel, table=True):
    """Reusable task patterns"""
    id: str
    name: str
    description: str | None
    template_tasks: list[dict]
    created_by: str
    created_at: datetime

class AuditLog(SQLModel, table=True):
    """Every action tracked — this is the proof"""
    id: int
    entity_type: str                 # "task", "project", "worker"
    entity_id: str
    action: str                      # "created", "assigned", "delegated", "completed"
    actor_id: str
    actor_type: Literal["human", "agent"]
    details: dict | None
    created_at: datetime

class Conversation(SQLModel, table=True):
    """Chat sessions with TaskFlow AI"""
    id: int
    user_id: str
    created_at: datetime
    updated_at: datetime

class Message(SQLModel, table=True):
    """Individual messages in conversations"""
    id: int
    conversation_id: int
    role: Literal["user", "assistant", "system"]
    content: str
    tool_calls: list[dict] | None
    created_at: datetime

Phase Breakdown: The Evolution of TaskFlow

Each phase powers up a specific capability while meeting all hackathon requirements.

Phase I: Local CLI with File Storage (Dec 7)

Points: 100 | Core Proof of Concept

Objective: Build command-line TaskFlow that proves human-agent task management works.

Storage: Local files (.taskflow/config.yaml + .taskflow/data.json)

Technology Stack:

Python 3.13+
UV
Typer (CLI framework)
Pydantic (data validation)
Claude Code + Spec-Kit Plus

Deliverables:

Initialization & Configuration

$ taskflow init
✓ Created .taskflow/config.yaml
✓ Created .taskflow/data.json
TaskFlow initialized!

Project Management

$ taskflow project add taskflow --name "TaskFlow Platform"
$ taskflow project add personal --name "Personal Tasks"
$ taskflow project list

Worker Management (Humans + Agents)

$ taskflow worker add @muhammad --type human --name "Muhammad"
$ taskflow agent add @claude-code --capabilities coding,architecture
$ taskflow agent add @qwen --capabilities research,analysis
$ taskflow agent add @gemini --capabilities research,summarization
$ taskflow worker list

Task CRUD (Basic Level Features)

# Add Task
$ taskflow add "Implement MCP server" --project taskflow --assign @claude-code
✓ Created task #1

# View Task List
$ taskflow list
$ taskflow list --project taskflow
$ taskflow list --assignee @claude-code
$ taskflow list --status pending

# Update Task
$ taskflow edit 1 --title "Implement MCP server v2" --priority high

# Delete Task
$ taskflow delete 1

# Mark Complete
$ taskflow complete 1

Intermediate Features

# Priorities & Tags
$ taskflow add "Fix auth bug" --priority urgent --tags bug,security

# Search & Filter
$ taskflow list --tag bug
$ taskflow list --priority urgent

# Sort
$ taskflow list --sort due_date
$ taskflow list --sort priority

Human-Agent Workflow

# Agent starts task and breaks down into subtasks
$ taskflow start 1
Starting task #1...
Enter subtasks (or empty to let agent decompose):
> Design protocol
> Implement handlers
> Add authentication
✓ 3 subtasks created

# Progress tracking
$ taskflow progress 1 --percent 30 --note "Protocol designed"

# Agent delegates to another agent
$ taskflow delegate 1.2 @qwen --note "Need research first"
✓ Subtask 1.2 delegated to @qwen by @claude-code

# Request review
$ taskflow review 1

# Human approves/rejects
$ taskflow approve 1
$ taskflow reject 1 --reason "Missing tests"

Audit Trail

$ taskflow audit 1
TASK #1: Implement MCP server
──────────────────────────────────────────────────
[2025-12-06 10:00] created by @muhammad
[2025-12-06 10:00] assigned to @claude-code
[2025-12-06 10:05] started by @claude-code
[2025-12-06 10:05] subtask added: "Design protocol"
[2025-12-06 10:05] subtask added: "Implement handlers"
[2025-12-06 10:05] subtask added: "Add authentication"
[2025-12-06 12:00] progress: 30% "Protocol designed"
[2025-12-06 13:00] subtask 1.2 delegated to @qwen
[2025-12-06 15:00] review requested

Linked Resources

$ taskflow link 1 --type spec --uri "./specs/mcp.md" --name "MCP Spec"
$ taskflow links 1

File Structure:

.taskflow/
├── config.yaml      # Projects, workers, settings
├── data.json        # Tasks, audit logs, links
└── .env             # API keys (gitignored)

Repository Structure:

taskflow/
├── .spec-kit/
│   └── config.yaml
├── specs/
│   ├── constitution.md
│   ├── overview.md
│   ├── phase-1/
│   │   ├── cli-interface.md
│   │   ├── data-model.md
│   │   └── storage.md
│   └── features/
│       ├── task-crud.md
│       ├── human-agent-assignment.md
│       └── audit-trail.md
├── cli/
│   ├── CLAUDE.md
│   ├── pyproject.toml
│   └── src/taskflow/
│       ├── __init__.py
│       ├── main.py
│       ├── models.py
│       ├── storage.py
│       └── commands/
├── CLAUDE.md
└── README.md

Phase II: Full-Stack Web Application (Dec 14)

Points: 150 | Multi-User + Persistence

Objective: Transform CLI into multi-user web app with real database.

What Changes:

Storage moves from local files to Neon PostgreSQL
Web UI for humans (Next.js)
REST API (FastAPI)
Authentication via Better Auth (reuse from Hackathon 1 SSO)

Technology Stack:

Layer	Technology
Frontend	Next.js 16+ (App Router), TypeScript, Tailwind
Backend	Python FastAPI
ORM	SQLModel
Database	Neon Serverless PostgreSQL
Auth	Better Auth with JWT/JWKS

Architecture:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   Next.js UI    │────▶│   FastAPI       │────▶│   Neon DB       │
│   (Humans)      │     │   Backend       │     │   (PostgreSQL)  │
└────────┬────────┘     └─────────────────┘     └─────────────────┘
         │                      │
         │                      ▼
         │              ┌─────────────────┐
         └─────────────▶│ Hackathon 1 SSO │
                        │ (Better Auth)   │
                        └─────────────────┘

API Endpoints:

Method	Endpoint	Description
GET	/api/projects	List user's projects
POST	/api/projects	Create project
GET	/api/projects/{id}/tasks	List tasks in project
POST	/api/projects/{id}/tasks	Create task
GET	/api/tasks/{id}	Get task with subtasks
PUT	/api/tasks/{id}	Update task
DELETE	/api/tasks/{id}	Delete task
POST	/api/tasks/{id}/start	Start task
POST	/api/tasks/{id}/subtasks	Add subtask
PATCH	/api/tasks/{id}/progress	Update progress
POST	/api/tasks/{id}/delegate	Delegate subtask
POST	/api/tasks/{id}/review	Request review
POST	/api/tasks/{id}/approve	Approve
POST	/api/tasks/{id}/reject	Reject
GET	/api/tasks/{id}/audit	Get audit trail
GET	/api/workers	List workers
POST	/api/workers	Register worker/agent

CLI Migration:

# One command migrates local data to cloud
$ taskflow migrate --to neon
Migrating 15 tasks, 3 projects, 45 audit logs...
✓ Migration complete. Storage: neon

Repository Structure:

taskflow/
├── specs/
│   ├── phase-2/
│   │   ├── api-endpoints.md
│   │   ├── database-schema.md
│   │   ├── authentication.md
│   │   └── frontend-components.md
├── cli/                    # Phase 1 CLI (still works)
├── frontend/
│   ├── CLAUDE.md
│   └── ... (Next.js app)
├── backend/
│   ├── CLAUDE.md
│   └── ... (FastAPI app)
├── docker-compose.yml
└── README.md

Phase III: AI-Powered TaskFlow with MCP (Dec 21)

Points: 200 | Agent Gateway

Objective: Agents connect via MCP, humans chat via natural language.

What Changes:

MCP Server for agent connections
Chat interface with OpenAI ChatKit
OpenAI Agents SDK for orchestration
Agents can work autonomously

Technology Stack:

Component	Technology
Chat UI	OpenAI ChatKit
AI Framework	OpenAI Agents SDK
MCP Server	Official MCP SDK
Backend	FastAPI
Database	Neon PostgreSQL

Architecture:

┌─────────────────┐     ┌─────────────────────────────────────────────────┐
│   ChatKit UI    │     │              TaskFlow Backend                    │
│   (Human Chat)  │────▶│                                                 │
└─────────────────┘     │  ┌─────────────┐    ┌─────────────────────────┐│
                        │  │ Chat API    │    │     MCP Server          ││
┌─────────────────┐     │  │ /api/chat   │    │  (Agent Connections)    ││
│   Web UI        │────▶│  └──────┬──────┘    └───────────┬─────────────┘│
│   (Dashboard)   │     │         │                       │              │
└─────────────────┘     │         ▼                       ▼              │
                        │  ┌──────────────────────────────────────────┐  │
                        │  │         OpenAI Agents SDK                │  │
                        │  │         (Orchestration Layer)            │  │
                        │  └──────────────────────────────────────────┘  │
                        └─────────────────────────────────────────────────┘
                                              │
              ┌───────────────────────────────┼───────────────────────────┐
              │                               │                           │
              ▼                               ▼                           ▼
      ┌───────────────┐               ┌───────────────┐           ┌───────────────┐
      │ @claude-code  │               │    @qwen      │           │   @gemini     │
      │ (MCP Client)  │               │ (MCP Client)  │           │ (MCP Client)  │
      └───────────────┘               └───────────────┘           └───────────────┘

MCP Tools Specification:

Tool	Purpose	Parameters
`list_my_tasks`	Get tasks assigned to this agent	`agent_id`, `status?`, `project?`
`get_task_details`	Get full task context	`task_id`
`claim_task`	Start working on task	`task_id`
`add_subtask`	Break down work	`task_id`, `title`, `assignee?`
`delegate_subtask`	Assign to another agent	`subtask_id`, `agent_id`, `note?`
`update_progress`	Report status	`task_id`, `percent`, `note?`
`complete_subtask`	Mark subtask done	`subtask_id`
`request_review`	Flag for human approval	`task_id`

Chat Interface Examples:

You: "What's on my plate today?"

TaskFlow AI: Based on your tasks:

URGENT:
- #12 "Phase 1 submission" (due today)
- #15 "Message Ahmad about RTJ" (personal)

IN PROGRESS (agents working):
- #8 @claude-code: "MCP implementation" (70%)
- #9 @qwen: "Auth research" (completed, awaiting review)

BLOCKED:
- #11 "Choose auth pattern" — waiting for your decision

Shall I show details on any of these?

You: "Assign the research task to @gemini"

TaskFlow AI: Created task #23 "Research task" assigned to @gemini.
@gemini will report progress. I'll notify you when complete.

Agent Authentication:

Agents authenticate with API keys (generated in UI)
API key tied to agent identity
All actions logged with agent actor

Phase IV: Local Kubernetes Deployment (Jan 4)

Points: 250 | Cloud-Native Local

Objective: Deploy TaskFlow on local Kubernetes with AI-assisted operations.

Technology Stack:

Component	Technology
Containerization	Docker + Gordon
Orchestration	Kubernetes (Minikube)
Package Manager	Helm Charts
AI DevOps	kubectl-ai, Kagent

Architecture:

┌────────────────────────────────────────────────────────────────────────┐
│                         MINIKUBE CLUSTER                                │
│                                                                         │
│  ┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐      │
│  │   Frontend      │   │   Backend       │   │   MCP Server    │      │
│  │   Deployment    │   │   Deployment    │   │   Deployment    │      │
│  │   (Next.js)     │   │   (FastAPI)     │   │   (Python)      │      │
│  └────────┬────────┘   └────────┬────────┘   └────────┬────────┘      │
│           │                     │                     │                │
│           └─────────────────────┴─────────────────────┘                │
│                                 │                                       │
│                    ┌────────────▼────────────┐                         │
│                    │    Ingress Controller   │                         │
│                    └────────────┬────────────┘                         │
└─────────────────────────────────┼──────────────────────────────────────┘
                                  │
                    ┌─────────────▼─────────────┐
                    │   Neon DB (External)      │
                    └───────────────────────────┘

AIOps Commands:

# Docker AI (Gordon)
$ docker ai "build the taskflow backend image"
$ docker ai "optimize the Dockerfile for production"

# kubectl-ai
$ kubectl-ai "deploy taskflow with 2 backend replicas"
$ kubectl-ai "check why the mcp-server pod is failing"
$ kubectl-ai "scale frontend to handle more traffic"

# Kagent
$ kagent "analyze cluster health"
$ kagent "suggest resource optimizations"

Helm Charts:

helm/
├── Chart.yaml
├── values.yaml
└── templates/
    ├── frontend-deployment.yaml
    ├── backend-deployment.yaml
    ├── mcp-server-deployment.yaml
    ├── services.yaml
    ├── ingress.yaml
    └── configmap.yaml

Phase V: Production Cloud Deployment (Jan 18)

Points: 300 | Event-Driven + Global

Objective: Deploy to production Kubernetes with event-driven architecture.

What's Added:

DigitalOcean Kubernetes (DOKS)
Kafka via Redpanda Cloud
Dapr for service mesh
Advanced features (recurring tasks, reminders)
CI/CD via GitHub Actions

Technology Stack:

Component	Technology
Cloud K8s	DigitalOcean DOKS
Event Streaming	Kafka (Redpanda Cloud)
Service Mesh	Dapr
CI/CD	GitHub Actions

Event-Driven Architecture:

┌────────────────────────────────────────────────────────────────────────┐
│                    DIGITALOCEAN KUBERNETES (DOKS)                       │
│                                                                         │
│  ┌────────────────────────────────────────────────────────────────┐   │
│  │                     DAPR SIDECARS                               │   │
│  │  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────────────┐   │   │
│  │  │Frontend │  │Backend  │  │MCP      │  │Notification     │   │   │
│  │  │+ Dapr   │  │+ Dapr   │  │+ Dapr   │  │Service + Dapr   │   │   │
│  │  └────┬────┘  └────┬────┘  └────┬────┘  └────────┬────────┘   │   │
│  │       │            │            │                │             │   │
│  └───────┴────────────┴────────────┴────────────────┴─────────────┘   │
│                                    │                                    │
│                       ┌────────────▼────────────┐                      │
│                       │     DAPR COMPONENTS     │                      │
│                       │  ┌──────────────────┐   │                      │
│                       │  │ pubsub.kafka     │───┼──▶ Redpanda Cloud    │
│                       │  │ state.postgresql │───┼──▶ Neon DB           │
│                       │  │ bindings.cron    │   │   (Reminders)        │
│                       │  │ secretstores.k8s │   │   (API Keys)         │
│                       │  └──────────────────┘   │                      │
│                       └─────────────────────────┘                      │
└────────────────────────────────────────────────────────────────────────┘

Kafka Topics:

Topic	Producer	Consumer	Purpose
`task-events`	Backend	Audit Service	All CRUD operations
`agent-progress`	MCP Server	Dashboard	Real-time progress
`review-requests`	MCP Server	Notification	Alert humans
`reminders`	Cron Binding	Notification	Due date alerts

Advanced Features:

# Recurring tasks
$ taskflow add "Weekly standup" --recurrence weekly --assign @muhammad

# Due dates with reminders
$ taskflow add "Submit phase 5" --due "2025-01-18 20:00" --remind "1 hour before"

# Priority-based sorting
$ taskflow list --sort priority --status pending

CI/CD Pipeline:

# .github/workflows/deploy.yaml
name: Deploy TaskFlow
on:
  push:
    branches: [main]
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build images
        run: docker-compose build
      - name: Push to registry
        run: docker-compose push
      - name: Deploy to DOKS
        run: helm upgrade --install taskflow ./helm

Bonus Features Implementation

Bonus	Points	Implementation
Human-Agent Platform	+300	Core feature — agents are first-class workers
Reusable Intelligence (Blueprints)	+200	`taskflow blueprint` commands
Cloud-Native Blueprints	+200	Helm charts + kubectl-ai patterns
Multi-language (Urdu)	+100	Chat understands Urdu commands
Voice Commands	+200	Browser speech-to-text → chat
TOTAL	+1000

Blueprint Example:

# Create blueprint
$ taskflow blueprint add feature-dev --name "Feature Development"
$ taskflow blueprint tasks feature-dev
> Research existing solutions [@gemini]
> Write spec [@human]
> Implement backend [@claude-code]
> Implement frontend [@claude-code]
> Write tests [@claude-code]
> Deploy [@claude-code]

# Use blueprint
$ taskflow add "Add recurring tasks" --blueprint feature-dev
✓ Created task #50 with 6 subtasks from blueprint

Repository Final Structure

taskflow/
├── .spec-kit/
│   └── config.yaml
├── specs/
│   ├── constitution.md
│   ├── overview.md
│   ├── architecture.md
│   ├── data-model/
│   │   ├── core-entities.md
│   │   └── audit-system.md
│   ├── features/
│   │   ├── task-crud.md
│   │   ├── human-agent-assignment.md
│   │   ├── agent-delegation.md
│   │   ├── audit-trail.md
│   │   ├── linked-resources.md
│   │   ├── blueprints.md
│   │   └── chat-interface.md
│   ├── api/
│   │   ├── rest-endpoints.md
│   │   └── mcp-tools.md
│   └── phases/
│       ├── phase-1-cli.md
│       ├── phase-2-web.md
│       ├── phase-3-mcp.md
│       ├── phase-4-k8s.md
│       └── phase-5-cloud.md
├── cli/
│   ├── CLAUDE.md
│   ├── pyproject.toml
│   └── src/taskflow/
├── frontend/
│   ├── CLAUDE.md
│   └── ... (Next.js)
├── backend/
│   ├── CLAUDE.md
│   └── ... (FastAPI)
├── mcp-server/
│   ├── CLAUDE.md
│   └── ... (MCP SDK)
├── helm/
│   ├── Chart.yaml
│   ├── values.yaml
│   └── templates/
├── .github/
│   └── workflows/
│       └── deploy.yaml
├── docker-compose.yml
├── CLAUDE.md
└── README.md

Submission Checklist

Phase	Due	Deliverables
I	Dec 7	CLI + GitHub + Demo Video
II	Dec 14	Web App + Vercel + API URL
III	Dec 21	Chat + MCP Server
IV	Jan 4	Minikube Instructions
V	Jan 18	DOKS URL + Full Demo

The Meta-Lesson

This project proves:

AI-native learning works — spec evolution shows real understanding
The constraint is the curriculum — "cannot write code manually" forces thinking
Human-agent collaboration is the future — TaskFlow demonstrates it
Process is proof — audit trail shows who did what

Name		Name	Last commit message	Last commit date
Latest commit History 214 Commits
.agent/workflows		.agent/workflows
.claude		.claude
.cursor/commands		.cursor/commands
.github/workflows		.github/workflows
.specify		.specify
apps		apps
docs		docs
history		history
infrastructure		infrastructure
scripts		scripts
skills-ref		skills-ref
specs		specs
.cursorrules		.cursorrules
.env.example		.env.example
.env.prod.example		.env.prod.example
.gitignore		.gitignore
.mcp.json		.mcp.json
CLAUDE.md		CLAUDE.md
GEMINI.md		GEMINI.md
README.md		README.md

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TaskFlow: Human-Agent Task Orchestration Platform

Why TaskFlow (The Real Problem)

Current State: Data Silos Everywhere

TaskFlow Solution: Unified Orchestration Layer

What Makes TaskFlow Different

The Killer Feature: Agent-to-Agent Delegation

Data Model (Core Schema)

Phase Breakdown: The Evolution of TaskFlow

Phase I: Local CLI with File Storage (Dec 7)

Phase II: Full-Stack Web Application (Dec 14)

Phase III: AI-Powered TaskFlow with MCP (Dec 21)

Phase IV: Local Kubernetes Deployment (Jan 4)

Phase V: Production Cloud Deployment (Jan 18)

Bonus Features Implementation

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mjunaidca/taskforce

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TaskFlow: Human-Agent Task Orchestration Platform

Why TaskFlow (The Real Problem)

Current State: Data Silos Everywhere

TaskFlow Solution: Unified Orchestration Layer

What Makes TaskFlow Different

The Killer Feature: Agent-to-Agent Delegation

Data Model (Core Schema)

Phase Breakdown: The Evolution of TaskFlow

Phase I: Local CLI with File Storage (Dec 7)

Phase II: Full-Stack Web Application (Dec 14)

Phase III: AI-Powered TaskFlow with MCP (Dec 21)

Phase IV: Local Kubernetes Deployment (Jan 4)

Phase V: Production Cloud Deployment (Jan 18)

Bonus Features Implementation

Repository Final Structure

Submission Checklist

The Meta-Lesson

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