TaskFlow - Integration Plan

Version: 1.1.0
Date: January 2026
Author: Atlas (Team Brain)
For: Logan Smith / Metaphy LLC

🎯 INTEGRATION GOALS

This document outlines how TaskFlow integrates with:

Team Brain agents (Forge, Atlas, Clio, Nexus, Bolt)
Existing Team Brain tools
BCH (Beacon Command Hub) - planned
Logan's personal workflows

📦 BCH INTEGRATION

Overview

TaskFlow can be integrated into BCH as a task management backend for agent coordination.

BCH Commands (Planned)

@taskflow list                   # List all tasks
@taskflow add "Title"            # Add new task
@taskflow done <id>              # Complete task
@taskflow sprint-1               # Show sprint-1 tagged tasks

Implementation Steps

Add to BCH imports:
```
from taskflow import TaskFlow
```

Create command handlers:

def handle_taskflow_command(args):
    tf = TaskFlow(BCH_TASK_FILE)
    # Parse command and execute

Test integration with BCH agent client
Update BCH documentation

Current Status

BCH integration not yet implemented
Python API ready for integration
CLI interface compatible with subprocess calls

🤖 AI AGENT INTEGRATION

Integration Matrix

Agent	Primary Use Case	Integration Method	Priority
Forge	Sprint planning, task assignment	Python API	HIGH
Atlas	Tool build tracking, quality gates	Python API + CLI	HIGH
Clio	Linux automation tasks, BCH tasks	CLI + Python	MEDIUM
Nexus	Cross-platform task sync	Python API	MEDIUM
Bolt	Bulk task execution	CLI batch mode	LOW

Agent-Specific Workflows

Forge (Orchestrator / Reviewer)

Role: Orchestrator - Plans tasks, assigns to agents, reviews completion

Primary Use Cases:

Sprint planning and organization
Task assignment across Team Brain
Progress tracking and reporting
Quality gate verification

Integration Pattern:

from taskflow import TaskFlow

# Forge's sprint planning workflow
tf = TaskFlow("team_brain_tasks.json")

# Create sprint tasks
tf.add_task(
    "Build PathBridge tool",
    priority="high",
    tags=["sprint-1", "atlas", "tool"],
    due_date="2026-01-25"
)

tf.add_task(
    "Review DevSnapshot",
    priority="medium",
    tags=["sprint-1", "forge", "review"],
    due_date="2026-01-26"
)

# Check agent's assigned tasks
atlas_tasks = tf.list_tasks(tag="atlas")
for task in atlas_tasks:
    print(f"Atlas: [{task['id']}] {task['title']} ({task['status']})")

# Mark review complete
tf.mark_done(task_id)

# Generate sprint report
tf.export_markdown("SPRINT_1_REPORT.md")

Forge's Tag System:

forge - Forge's own tasks
atlas, clio, nexus, bolt - Agent assignments
sprint-N - Sprint organization
review, blocked, waiting - Status tags
tool, bug, docs - Task type tags

Atlas (Executor / Builder)

Role: Tool Creator - Builds tools, runs tests, manages quality

Primary Use Cases:

Track tool build progress
Quality gate checklists
Phase completion tracking
Bug and test tracking

Integration Pattern:

from taskflow import TaskFlow

# Atlas tool build workflow
tf = TaskFlow(".taskflow.json")  # Per-tool tracking

# Create build phases as tasks
phases = [
    ("Phase 1: Planning", "high", "phase-1"),
    ("Phase 2: Core Development", "high", "phase-2"),
    ("Phase 3: Documentation", "medium", "phase-3"),
    ("Phase 4: Examples", "medium", "phase-4"),
    ("Phase 5: Testing", "high", "phase-5"),
    ("Phase 6: Branding", "low", "phase-6"),
    ("Phase 7: Integration Docs", "medium", "phase-7"),
    ("Phase 8: Quality Audit", "high", "phase-8"),
    ("Phase 9: Deployment", "medium", "phase-9"),
]

for title, priority, tag in phases:
    tf.add_task(title, priority=priority, tags=[tag, "build"])

# Track progress
tf.mark_in_progress(1)  # Start Phase 1

# Mark phase complete
tf.mark_done(1)
tf.mark_in_progress(2)  # Start Phase 2

# Check overall progress
tf.export_markdown("BUILD_PROGRESS.md")

Atlas Quality Gate Integration:

def check_quality_gates(tf):
    """Verify all quality gates pass before deployment."""
    gates = {
        "tests_passing": False,
        "docs_complete": False,
        "examples_written": False,
        "phase7_done": False,
    }
    
    # Check each gate
    for task in tf.list_tasks(status="done"):
        if "phase-5" in task.get("tags", []):
            gates["tests_passing"] = True
        if "phase-3" in task.get("tags", []):
            gates["docs_complete"] = True
        # ... check other gates
    
    return all(gates.values())

Clio (Linux / Ubuntu Agent)

Role: Linux specialist - CLI automation, shell scripts

Primary Use Cases:

Track automation scripts
Manage cron job tasks
ABL/ABIOS task lists
BCH Linux-side tasks

Integration Pattern:

# Clio CLI workflow
cd ~/automation
python taskflow.py init

# Add automation tasks
python taskflow.py add "Update ABIOS service" --priority high --tags automation,abios
python taskflow.py add "Check cron logs" --priority medium --tags maintenance

# Daily check
python taskflow.py list --status todo --priority high

# After completing
python taskflow.py done 1

# Weekly export
python taskflow.py export --output ~/reports/tasks_$(date +%Y%m%d).md

Platform Considerations:

TaskFlow works identically on Linux
Uses ~/.taskflow.json for home directory tasks
Shell aliases recommended: alias tf='python3 ~/tools/taskflow.py'

Nexus (Multi-Platform Agent)

Role: Cross-platform testing and coordination

Primary Use Cases:

Track multi-platform testing
Sync tasks across environments
Platform-specific bug tracking
Cross-platform compatibility tests

Integration Pattern:

from taskflow import TaskFlow
import platform

# Platform-aware task management
tf = TaskFlow("crossplatform_tasks.json")

current_os = platform.system().lower()

# Add platform-specific tasks
tf.add_task(
    f"Test on {current_os}",
    priority="medium",
    tags=["testing", current_os]
)

# Filter by current platform
platform_tasks = tf.list_tasks(tag=current_os)
for task in platform_tasks:
    print(f"  [{task['id']}] {task['title']}")

Multi-Platform Sync Strategy:

Keep .taskflow.json in cloud-synced directory
Or commit to shared Git repository
Use platform tags: windows, linux, macos

Bolt (Free Executor)

Role: Cost-free task execution via Cline

Primary Use Cases:

Bulk task processing
Repetitive operations
Free API usage
Batch task completion

Integration Pattern:

# Bolt batch workflow
# Process multiple tasks without API costs

# List assigned tasks
python taskflow.py list --tag bolt

# Complete tasks in sequence
for id in 1 2 3 4 5; do
    echo "Processing task $id..."
    # ... do work ...
    python taskflow.py done $id
done

# Report completion
python taskflow.py stats

Cost Considerations:

TaskFlow has zero dependencies
Runs without API calls
Perfect for Bolt's cost-free execution

🔗 INTEGRATION WITH OTHER TEAM BRAIN TOOLS

With AgentHealth

Use Case: Correlate task completion with agent health metrics

from agenthealth import AgentHealth
from taskflow import TaskFlow

health = AgentHealth()
tf = TaskFlow("agent_tasks.json")

# Start session
session_id = health.start_session("ATLAS")

# Track task work
task = tf.get_task(1)
tf.mark_in_progress(task['id'])

# Log health heartbeat
health.heartbeat("ATLAS", status="working", context=task['title'])

# Complete task
tf.mark_done(task['id'])
health.end_session("ATLAS", session_id=session_id)

With SynapseLink

Use Case: Notify team when tasks complete

from synapselink import quick_send
from taskflow import TaskFlow

tf = TaskFlow()

# Complete a task
task = tf.get_task(5)
tf.mark_done(5)

# Notify team
quick_send(
    "FORGE,TEAM",
    f"Task Complete: {task['title']}",
    f"Task [{task['id']}] has been completed.\n"
    f"Priority: {task['priority']}\n"
    f"Tags: {', '.join(task.get('tags', []))}",
    priority="NORMAL"
)

With SessionReplay

Use Case: Record task operations for debugging

from sessionreplay import SessionReplay
from taskflow import TaskFlow

replay = SessionReplay()
tf = TaskFlow()

session_id = replay.start_session("ATLAS", task="Task management")

# Log task operations
replay.log_event(session_id, "task_add", {"title": "New task"})
task = tf.add_task("New task")

replay.log_event(session_id, "task_start", {"id": task['id']})
tf.mark_in_progress(task['id'])

replay.end_session(session_id, status="COMPLETED")

With TokenTracker

Use Case: Track task work alongside token usage

from tokentracker import TokenTracker
from taskflow import TaskFlow

tracker = TokenTracker()
tf = TaskFlow()

# Start task tracking
task = tf.get_task(1)
tf.mark_in_progress(1)

# Do work... (token usage tracked separately)

# Complete and log
tf.mark_done(1)
tracker.add_entry(
    agent="ATLAS",
    input_tokens=500,
    output_tokens=1000,
    task_id=str(task['id']),
    notes=f"Task: {task['title']}"
)

With ContextCompressor

Use Case: Compress task exports for sharing

from contextcompressor import ContextCompressor
from taskflow import TaskFlow

compressor = ContextCompressor()
tf = TaskFlow()

# Generate task list
tasks = tf.list_tasks()
task_text = "\n".join([
    f"[{t['id']}] {t['title']} ({t['status']})"
    for t in tasks
])

# Compress for sharing
compressed = compressor.compress_text(task_text, query="status")
print(f"Compressed from {len(task_text)} to {len(compressed.compressed_text)} chars")

With MemoryBridge

Use Case: Persist task history to memory core

from memorybridge import MemoryBridge
from taskflow import TaskFlow

memory = MemoryBridge()
tf = TaskFlow()

# Load previous task history
history = memory.get("taskflow_history", default=[])

# Complete a task
task = tf.get_task(1)
tf.mark_done(1)

# Log to history
history.append({
    "task_id": task['id'],
    "title": task['title'],
    "completed": datetime.now().isoformat(),
    "agent": "ATLAS"
})

# Persist
memory.set("taskflow_history", history)
memory.sync()

With ConfigManager

Use Case: Centralize TaskFlow configuration

from configmanager import ConfigManager
from taskflow import TaskFlow

config = ConfigManager()

# Load TaskFlow config
tf_config = config.get("taskflow", {
    "default_priority": "medium",
    "task_file": ".taskflow.json",
    "auto_export": False
})

# Use configured task file
tf = TaskFlow(tf_config["task_file"])

# Add with configured default priority
tf.add_task("New task", priority=tf_config["default_priority"])

🚀 ADOPTION ROADMAP

Phase 1: Core Adoption (Week 1)

Goal: All agents aware and can use basic features

Steps:

✅ Tool deployed to GitHub
☐ Quick-start guides sent via Synapse
☐ Each agent tests basic workflow
☐ Feedback collected

Success Criteria:

All 5 agents have used TaskFlow at least once
No blocking issues reported
Basic add/list/done workflow understood

Phase 2: Integration (Week 2-3)

Goal: Integrated into daily workflows

Steps:

☐ Add to agent session start routines
☐ Implement Synapse notifications on completion
☐ Create per-project task files
☐ Sprint planning workflow established

Success Criteria:

Used daily by at least 3 agents
Sprint tracking implemented
Team Brain coordination improved

Phase 3: Advanced Integration (Week 4+)

Goal: Full ecosystem integration

Steps:

☐ BCH integration implemented
☐ Automated task assignment from Synapse
☐ Quality gate integration with Atlas builds
☐ Metrics dashboard

Success Criteria:

BCH commands working
Automated workflows running
Measurable productivity improvement

📊 SUCCESS METRICS

Adoption Metrics

Metric	Target	Current
Agents using TaskFlow	5/5	0/5
Tasks created (weekly)	20+	0
Task completion rate	80%+	N/A
Sprint tracking active	Yes	No

Efficiency Metrics

Metric	Before TaskFlow	With TaskFlow
Task tracking method	Ad-hoc/mental	Centralized
Sprint visibility	Low	High
Cross-agent coordination	Manual	Automated
Status reporting	Time-consuming	Instant

Quality Metrics

Metric	Target
Bug reports	< 5/month
Feature requests	Track all
User satisfaction	High

🛠️ TECHNICAL INTEGRATION DETAILS

Import Paths

# Standard import
from taskflow import TaskFlow

# For subprocess/CLI
import subprocess
result = subprocess.run(
    ["python", "taskflow.py", "list"],
    capture_output=True, text=True
)

Configuration

Config File: None (uses .taskflow.json data file)

Environment Variables: None required

Shared Config with ConfigManager:

{
  "taskflow": {
    "default_file": ".taskflow.json",
    "default_priority": "medium",
    "export_format": "markdown"
  }
}

Error Handling

Standardized Exit Codes:

0: Success
1: General error
2: Task not found
3: Invalid arguments

Error Messages:

[X] Task {id} not found
[X] Error saving tasks: {details}
[X] Export failed
[!] No changes specified

File Locations

Per-Project:

project/
├── .taskflow.json    # Task data
├── TASKS.md          # Export (optional)
└── ...

Global (optional):

~/.taskflow/
├── global_tasks.json
└── config.json       # Future: global settings

🔧 MAINTENANCE & SUPPORT

Update Strategy

Minor updates (v1.x): Monthly
Major updates (v2.0+): Quarterly
Bug fixes: As needed

Support Channels

GitHub Issues: Bug reports and features
Synapse: Team Brain discussions
Direct: Message Atlas for tool issues

Known Limitations

Single-file storage - Not suited for massive task lists (1000+)
No real-time sync - Manual Git sync required for teams
Basic conflict handling - Manual merge for concurrent edits
No subtasks - Flat task structure only

Planned Improvements

v1.2: Subtask support
v1.3: Task templates
v2.0: Real-time sync option
v2.1: Web UI (optional)

📚 ADDITIONAL RESOURCES

README: README.md
Examples: EXAMPLES.md
Cheat Sheet: CHEAT_SHEET.txt
Quick Start: QUICK_START_GUIDES.md
Integration Examples: INTEGRATION_EXAMPLES.md
GitHub: https://github.com/DonkRonk17/TaskFlow

Last Updated: January 2026
Maintained By: Atlas (Team Brain)
For: Logan Smith / Metaphy LLC

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TaskFlow - Integration Plan

🎯 INTEGRATION GOALS

📦 BCH INTEGRATION

Overview

BCH Commands (Planned)

Implementation Steps

Current Status

🤖 AI AGENT INTEGRATION

Integration Matrix

Agent-Specific Workflows

Forge (Orchestrator / Reviewer)

Atlas (Executor / Builder)

Clio (Linux / Ubuntu Agent)

Nexus (Multi-Platform Agent)

Bolt (Free Executor)

🔗 INTEGRATION WITH OTHER TEAM BRAIN TOOLS

With AgentHealth

With SynapseLink

With SessionReplay

With TokenTracker

With ContextCompressor

With MemoryBridge

With ConfigManager

🚀 ADOPTION ROADMAP

Phase 1: Core Adoption (Week 1)

Phase 2: Integration (Week 2-3)

Phase 3: Advanced Integration (Week 4+)

📊 SUCCESS METRICS

Adoption Metrics

Efficiency Metrics

Quality Metrics

🛠️ TECHNICAL INTEGRATION DETAILS

Import Paths

Configuration

Error Handling

File Locations

🔧 MAINTENANCE & SUPPORT

Update Strategy

Support Channels

Known Limitations

Planned Improvements

📚 ADDITIONAL RESOURCES