OpenSpace is a skill-management layer for AI agents that stores, retrieves, evaluates, shares, and improves reusable workflows. It is intended for people using multiple coding agents who want skills to be reused and refined based on task outcomes. The catalogue provides 200 skills for use with OpenSpace and the agents it supports.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HKUDS/OpenSpace --skill pdf-checklist-workflow-enhanced-780c5dgit clone --depth 1 https://github.com/HKUDS/OpenSpaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hkuds/openspace/pdf-checklist-workflow-enhanced-780c5d)<a href="https://agentmods.dev/skills/hkuds/openspace/pdf-checklist-workflow-enhanced-780c5d"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdf-checklist-workflow-enhanced-780c5d/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/openspace/pdf-checklist-workflow-enhanced-780c5d"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdf-checklist-workflow-enhanced-780c5d.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 92 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.03740 |
| Opus 5 | $0.00011 | $0.01870 |
| Sonnet 5 | $0.00004 | $0.00748 |
| Haiku 4.5 | $0.00002 | $0.00374 |
Grade A, and why
unified-deliverable-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 457 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unified Multi-Deliverable Workflow
This skill provides a structured pattern for creating multiple document artifacts (spreadsheets, diagrams, PDF reports) in a single workflow with explicit iteration budgeting and robust error handling.
Overview
Use this workflow when you need to:
- Generate multiple related deliverables (Excel, PNG diagrams, PDF reports)
- Manage iteration budget across different artifact types
- Handle execute_code_sandbox failures gracefully
- Ensure all deliverables are completed before budget exhaustion
- Create cohesive documentation packages with cross-referenced content
Iteration Budget Template
Default Allocation (adjust based on task complexity):
| Phase | Deliverable | Iterations | Validation |
|---|---|---|---|
| 1 | Spreadsheet (.xlsx) | 8-10 | File exists, readable, correct structure |
| 2 | Diagram (.png) | 8-10 | File exists, viewable, correct dimensions |
| 3 | PDF Report (.pdf) | 8-10 | File exists, downloadable, proper formatting |
| Buffer | Error recovery | 4-6 | Retry failed phases |
Total recommended budget: 30-36 iterations
Pre-Flight Checks (Iteration 1-2)
Before generating any deliverables:
- Verify workspace path: Always use
/workspace/as base directory - Test execute_code_sandbox: Run a simple print statement to confirm tool works
- Check required libraries: Plan to install via
!pip installif needed - Standardize filenames: Use consistent naming pattern (e.g.,
projectname_deliverable.ext)
# Quick sandbox test
print("SANDBOX_OK")
print(f"WORKSPACE_PATH:/workspace")
If this fails, use run_shell as fallback for file operations.
Phase 1: Spreadsheet Creation (Iterations 3-12)
Step 1.1: Plan Structure
Define your spreadsheet structure before coding:
- Sheet names
- Column headers
- Data types
- Any formulas or formatting
Step 1.2: Write Generation Code
Using openpyxl (recommended for .xlsx):
# Install if needed
# !pip install openpyxl
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment
wb = Workbook()
ws = wb.active
ws.title = "Data"
# Header row with styling
headers = ["Item", "Description", "Value", "Status"]
for col, header in enumerate(headers, 1):
cell = ws.cell(row=1, column=col, value=header)
cell.font = Font(bold=True)
cell.fill = PatternFill(start_color="CCCCCC", end_color="CCCCCC", fill_type="solid")
cell.alignment = Alignment(horizontal="center")
# Data rows
data = [
["Item 1", "Description 1", 100, "Complete"],
["Item 2", "Description 2", 200, "Pending"],
]
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 column in ws.columns:
max_length = 0
column_letter = column[0].column_letter
for cell in column:
try:
if len(str(cell.value)) > max_length:
max_length = len(str(cell.value))
except:
pass
adjusted_width = min(max_length + 2, 50)
ws.column_dimensions[column_letter].width = adjusted_width
# Save with standard path
output_path = "/workspace/hardware_selection_table.xlsx"
wb.save(output_path)
print(f"ARTIFACT_PATH:{output_path}")
print("SPREADSHEET_GENERATED")
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 457 lines · 22 tokens per session scan A c5b9036f0058
unified-deliverable-workflow is a skill published in the GitHub repository HKUDS/OpenSpace (7,552 stars, last pushed 28d ago), licensed MIT. It adds 22 tokens to every session and 3,740 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
doc-reader
Read any common document/data file — PDF, Word (.docx), Excel (.xlsx/.xls), PowerPoint (.pptx), images (OCR), CSV/TSV, plain text, JSON/YAML/TOML, HTML/XML, and most source-code files. Use the readdocument tool.
markdown-converter
Markdown conversion: PDF, Office, HTML, data, OCR, audio, ZIP, YouTube.
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
report-generator
A report-generation tool for producing SEO and GEO analysis reports in formats such as Markdown, HTML, PDF, JSON, and Excel. It also supports charts, templates, data processing, and interactive report elements.
smart-data-collection
A workflow for extracting structured information from images and documents such as PDFs, Word files, and spreadsheets, then storing it in a database.
meta-multi-format-export-pack
From one piece of source content, render four deliverables: .docx report, .pptx slides, .xlsx data, and an HTML/PDF public version.