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Unified Deliverables Flow

OrganizationPopular
HKUDS
unified-deliverables-flow

Generate spreadsheets, diagrams, and PDF reports in a phased workflow with explicit iteration budgets and error recovery

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill nameunified-deliverables-flow
Stars
7.8K
Forks
923
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 HKUDS on GitHub. Read the source before you install it.

Installation

Install the Unified Deliverables Flow 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/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/pdf-checklist-workflow-enhanced
  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/HKUDS/OpenSpace.git /tmp/OpenSpace
mkdir -p .claude/skills
cp -r /tmp/OpenSpace/benchmarks/gdpval/skills/pdf-checklist-workflow-enhanced .claude/skills/unified-deliverables-flow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Unified Deliverables Flow 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 Unified Deliverables Flow 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 Unified Deliverables Flow 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.

Unified Multi-Deliverable Generation Workflow

This skill provides a structured, phased approach for creating multiple deliverable types (spreadsheets, diagrams, PDF reports) in a single cohesive workflow with explicit iteration budgeting to prevent premature exhaustion.

Overview

Use this workflow when you need to:

  • Generate multiple deliverable types (Excel, images, PDFs) in one task
  • Ensure balanced iteration allocation across all deliverables
  • Handle tool failures gracefully with retries and fallbacks
  • Produce downloadable artifacts with verified paths

Critical: Iteration Budget Allocation

Allocate iterations BEFORE starting work:

PhaseDeliverableBudgetCheckpoint
Phase 1Spreadsheet8 iterationsFile exists + readable
Phase 2Diagram8 iterationsFile exists + >1KB
Phase 3PDF Report8 iterationsFile exists + ARTIFACT_PATH output
BufferError recovery6 iterationsRemaining for retries
TotalAll deliverables30 iterationsAll verified

Rules:

  • Complete each phase before moving to the next
  • If a phase exceeds budget, use fallback strategy (see below)
  • Never spend >10 iterations on a single deliverable without checkpoint
  • Verify each deliverable before proceeding

Step-by-Step Instructions

Phase 0: Pre-Work Setup (1 iteration max)

  1. Define all deliverables explicitly:

    Deliverable 1: hardware_selection_table.xlsx (Excel with comparison data)
    Deliverable 2: cnc_workcell_layout.png (PNG diagram of layout)
    Deliverable 3: final_report.pdf (PDF summary report)
  2. Verify workspace is accessible:

    run_shell
      command: ls -la /workspace/ && mkdir -p /workspace/artifacts
  3. Set path variables for consistency:

    • All files go to /workspace/ or /workspace/artifacts/
    • Use absolute paths in all code
    • Never use relative paths like ./output.xlsx

Phase 1: Spreadsheet Generation (Budget: 8 iterations)

Step 1.1: Choose approach

MethodToolBest For
Direct Pythonexecute_code_sandboxQuick generation, simple tables
Shell agentshell_agentComplex logic, error recovery

Step 1.2: Create spreadsheet code

python
from openpyxl import Workbook
from openpyxl.styles import Font, Alignment, Border, Side

def create_hardware_table():
    wb = Workbook()
    ws = wb.active
    ws.title = "Hardware Selection"
    
    # Headers with styling
    headers = ["Component", "Option A", "Option B", "Option C", "Recommendation"]
    for col, header in enumerate(headers, 1):
        cell = ws.cell(row=1, column=col, value=header)
        cell.font = Font(bold=True)
        cell.alignment = Alignment(horizontal='center')
    
    # Data rows
    data = [
        ["Controller", "PLC-X100 ($500)", "PLC-Y200 ($650)", "PLC-Z300 ($800)", "PLC-Y200"],
        ["Motor", "Servo-500W ($300)", "Servo-750W ($400)", "Stepper-1kW ($250)", "Servo-750W"],
        ["Sensor", "Proximity-S1 ($50)", "Vision-V2 ($200)", "Laser-L3 ($150)", "Vision-V2"]
    ]
    
    for row_idx, row_data in enumerate(data, 2):
        for col_idx, value in enumerate(row_data, 1):
            ws.cell(row=row_idx, column=col_idx, value=value)
    
    # Auto-adjust column widths
    for col in ws.columns:
        max_length = max(len(str(cell.value)) for cell in col if cell.value)
        ws.column_dimensions[col[0].column_letter].width = min(max_length + 2, 25)
    
    output_path = '/workspace/hardware_selection_table.xlsx'
    wb.save(output_path)
    print(f'SUCCESS: Spreadsheet created at {output_path}')
    print(f'ARTIFACT_PATH:{output_path}')
    return output_path

create_hardware_table()

Step 1.3: Execute with verification

execute_code_sandbox
  code: <spreadsheet code from Step 1.2>
  language: python

Step 1.4: Verify (REQUIRED before proceeding)

run_shell
  command: ls -lh /workspace/*.xlsx && python3 -c "import openpyxl; openpyxl.load_workbook('/workspace/hardware_selection_table.xlsx')"

Checkpoint criteria:

  • ✓ File exists
  • ✓ Size > 1KB
  • ✓ No errors opening file
  • ✗ If failed, retry once with shell_agent, then proceed to Phase 2 with note

Phase 2: Diagram Generation (Budget: 8 iterations)

Step 2.1: Choose diagram type and tool

Diagram TypeToolLibrary
Flowchart/Blocksexecute_code_sandboxmatplotlib, graphviz
Layout/Floorplanshell_agentmatplotlib, PIL
Architectureexecute_code_sandboxmatplotlib, diagram

Step 2.2: Create diagram code (matplotlib example)

python
import matplotlib.pyplot as plt
import matplotlib.patches as patches

def create_layout_diagram():
    fig, ax = plt.subplots(figsize=(12, 8))
    ax.set_xlim(0, 100)
    ax.set_ylim(0, 80)
    ax.set_aspect('equal')
    ax.set_title('CNC Workcell Layout', fontsize=16, pad=20)
    
    # Define workcell components
    components = [
        {'label': 'CNC Machine', 'xy': (30, 40), 'w': 25, 'h': 20, 'color': '#3498db'},
        {'label': 'Robot Arm', 'xy': (60, 40), 'w': 15, 'h': 15, 'color': '#e74c3c'},
        {'label': 'Material Rack', 'xy': (10, 20), 'w': 15, 'h': 40, 'color': '#2ecc71'},
        {'label': 'Control Panel', 'xy': (75, 60), 'w': 12, 'h': 10, 'color': '#f39c12'},
        {'label': 'Safety Zone', 'xy': (25, 35), 'w': 50, 'h': 30, 'color': 'none', 'edge': '#95a5a6', 'dashed': True}
    ]
    
    for comp in components:
        if comp.get('edge'):
            rect = patches.Rectangle(
                (comp['xy'][0], comp['xy'][1]), 
                comp['w'], comp['h'],
                linewidth=2, 
                edgecolor=comp['edge'],
                facecolor=comp['color'],
                linestyle='--' if comp.get('dashed') else '-'
            )
        else:
            rect = patches.Rectangle(
                (comp['xy'][0], comp['xy'][1]), 
                comp['w'], comp['h'],
                linewidth=2, 
                edgecolor='black',
                facecolor=comp['color']
            )
        ax.add_patch(rect)
        ax.text(
            comp['xy'][0] + comp['w']/2, 
            comp['xy'][1] + comp['h']/2, 
            comp['label'],
            ha='center', va='center', fontsize=10, fontweight='bold'
        )
    
    plt.grid(True, alpha=0.3)
    plt.xlabel('Distance (m)')
    plt.ylabel('Distance (m)')
    
    output_path = '/workspace/cnc_workcell_layout.png'
    plt.savefig(output_path, dpi=150, bbox_inches='tight')
    plt.close()
    print(f'SUCCESS: Diagram created at {output_path}')
    print(f'ARTIFACT_PATH:{output_path}')
    return output_path

create_layout_diagram()

Step 2.3: Execute and verify

execute_code_sandbox
  code: <diagram code from Step 2.2>
  language: python

Verification:

run_shell
  command: ls -lh /workspace/*.png && file /workspace/*.png

Checkpoint criteria:

  • ✓ File exists
  • ✓ Size > 1KB
  • ✓ File type confirmed as PNG/image
  • ✗ If failed after 2 retries, use shell_agent fallback, then proceed

Phase 3: PDF Report Generation (Budget: 8 iterations)

Step 3.1: Prepare content aggregation

Gather data from previous phases:

  • Spreadsheet: key findings, recommendations
  • Diagram: visual summary
  • Additional: scoring, conclusions

Step 3.2: Create PDF generation code

python
from reportlab.lib import colors
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.lib.enums import TA_CENTER, TA_LEFT

def create_final_report(output_path):
    doc = SimpleDocTemplate(output_path, pagesize=letter,
                           rightMargin=0.75*inch, leftMargin=0.75*inch,
                           topMargin=0.75*inch, bottomMargin=0.75*inch)
    styles = getSampleStyleSheet()
    story = []
    
    # Custom styles
    title_style = ParagraphStyle('CustomTitle', parent=styles['Heading1'],
                                  fontSize=20, spaceAfter=30, alignment=TA_CENTER,
                                  fontName='Helvetica-Bold')
    heading_style = ParagraphStyle('CustomHeading', parent=styles['Heading2'],
                                    fontSize=14, spaceAfter=12, spaceBefore=20,
                                    fontName='Helvetica-Bold')
    
    # Title
    story.append(Paragraph("CNC Workcell Implementation Report", title_style))
    story.append(Spacer(1, 0.5*inch))
    
    # Executive Summary
    story.append(Paragraph("Executive Summary", heading_style))
    summary_text = """
    This report presents the hardware selection analysis and workcell layout 
    for the proposed CNC implementation. After evaluating multiple options across 
    controllers, motors, and sensors, recommended configurations balance cost, 
    performance, and reliability.
    """
    story.append(Paragraph(summary_text, styles['Normal']))
    story.append(Spacer(1, 0.3*inch))
    
    # Hardware Selection Table
    story.append(Paragraph("Hardware Selection Summary", heading_style))
    data = [
        ['Component', 'Recommended Option', 'Cost', 'Rationale'],
        ['Controller', 'PLC-Y200', '$650', 'Best cost/performance balance'],
        ['Motor', 'Servo-750W', '$400', 'Adequate power with precision'],
        ['Sensor', 'Vision-V2', '$200', 'Enables quality inspection']
    ]
    
    table = Table(data, colWidths=[1.5*inch, 1.7*inch, 1*inch, 2.3*inch])
    table.setStyle(TableStyle([
        ('BACKGROUND', (0, 0), (-1, 0), colors '#2c3e50'),
        ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
        ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
        ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
        ('FONTSIZE', (0, 0), (-1, 0), 11),
        ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
        ('BACKGROUND', (0, 1), (-1, -1), colors.beige),
        ('GRID', (0, 0), (-1, -1), 1, colors.black),
        ('VALIGN', (0, 0), (-1, -1), 'MIDDLE')
    ]))
    story.append(table)
    story.append(Spacer(1, 0.4*inch))
    
    # Layout Diagram Reference
    story.append(Paragraph("Workcell Layout", heading_style))
    story.append(Paragraph("See attached diagram: cnc_workcell_layout.png", styles['Normal']))
    story.append(Spacer(1, 0.2*inch))
    
    # Recommendations
    story.append(Paragraph("Implementation Recommendations", heading_style))
    rec_text = """
    1. Begin with controller installation and configuration
    2. Integrate motor drives and verify motion control
    3. Install and calibrate vision system
    4. Conduct safety zone validation
    5. Perform full system integration testing
    """
    story.append(Paragraph(rec_text, styles['Normal']))
    story.append(Spacer(1, 0.5*inch))
    
    # Footer
    story.append(Spacer(1, 0.5*inch))
    footer_style = ParagraphStyle('Footer', parent=styles['Normal'],
                                   fontSize=9, alignment=TA_CENTER, textColor=colors.grey)
    story.append(Paragraph("Generated by Unified Deliverables Workflow", footer_style))
    
    doc.build(story)
    print(f'SUCCESS: PDF report created at {output_path}')
    print(f'ARTIFACT_PATH:{output_path}')
    return output_path

create_final_report('/workspace/final_report.pdf')

Step 3.3: Execute with explicit artifact path

execute_code_sandbox
  code: <PDF code from Step 3.2>
  language: python

Step 3.4: Final verification

run_shell
  command: ls -lh /workspace/*.pdf && echo "---" && ls -lh /workspace/*.xlsx /workspace/*.png

Checkpoint criteria:

  • ✓ PDF file exists
  • ✓ Size > 10KB (reports should be substantial)
  • ✓ ARTIFACT_PATH was output
  • ✓ All three deliverables verified

Error Recovery & Fallback Strategies

When execute_code_sandbox fails repeatedly:

Strategy 1: Use shell_agent (more resilient)

shell_agent
  task: Create an Excel file at /workspace/hardware_selection_table.xlsx with hardware comparison data including controllers, motors, and sensors with costs and recommendations

Strategy 2: Simplify the code

  • Remove complex styling
  • Use basic libraries only (csv instead of openpyxl if needed)
  • Reduce dependencies

Strategy 3: Change output format

  • Excel → CSV if openpyxl fails
  • PNG → SVG if PIL/matplotlib fails
  • PDF → Markdown + convert later

When iteration budget is running low:

Iterations RemainingAction
< 10Skip non-critical formatting, use minimal viable output
< 5Use simplest possible implementation, skip verification
< 3Output text summary with file paths, request manual generation

Best Practices

  1. Phase sequentially - Complete and verify each phase before moving on
  2. Use absolute paths - Always /workspace/filename.ext, never relative
  3. Output ARTIFACT_PATH - Every successful generation must print this
  4. Verify before proceeding - Run shell check after each deliverable
  5. Track iteration count - Count tool calls, stop at 25 to leave buffer
  6. Simplify on failure - If complex code fails, strip to minimum viable
  7. Document decisions - Note why fallback was used for future reference

Quick Reference: Tool Selection

TaskPrimary ToolFallback Tool
Spreadsheetexecute_code_sandbox (openpyxl)shell_agent (pandas)
Diagramexecute_code_sandbox (matplotlib)shell_agent (graphviz)
PDF Reportexecute_code_sandbox (reportlab)shell_agent (fpdf)
Verificationrun_shell (ls, file)read_file (for content check)

Complete Workflow Checklist

  • Phase 0: Defined all 3 deliverables with paths
  • Phase 0: Verified workspace accessibility
  • Phase 1: Spreadsheet created and verified (< 8 iterations)
  • Phase 2: Diagram created and verified (< 8 iterations)
  • Phase 3: PDF created and verified (< 8 iterations)
  • All ARTIFACT_PATH outputs captured
  • Final verification: all files exist with appropriate sizes
  • Total iterations used ≤ 30

Troubleshooting

ProblemImmediate Action
execute_code_sandbox returns "[ERROR] unknown error"Retry once, then switch to shell_agent
File not found after executionCheck actual path with run_shell: ls /workspace/
ARTIFACT_PATH not outputRe-run with explicit print statement
Iteration budget nearly exhaustedSkip remaining deliverables, document what was created
Library import failsAdd !pip install <library> at code start

Related Tools

  • execute_code_sandbox - Primary tool for code execution (Python)
  • shell_agent - Fallback for complex tasks or when sandbox fails
  • run_shell - Verification and workspace inspection
  • read_file - Content verification (type: xlsx, png, pdf)
  • create_file - Alternative for text-based outputs

Frequently asked questions

What does the Unified Deliverables Flow AI skill do?

Generate spreadsheets, diagrams, and PDF reports in a phased workflow with explicit iteration budgets and error recovery

Why use Unified Deliverables Flow on TypingMind?

Because you install it once and use it with any model. Unified Deliverables Flow 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 Unified Deliverables Flow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/pdf-checklist-workflow-enhanced. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Unified Deliverables Flow?

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 Unified Deliverables Flow?

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

Is the Unified Deliverables Flow AI skill free?

Yes. It is published on GitHub by HKUDS 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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