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 pptx-debug-workflowgit 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/pptx-debug-workflow)<a href="https://agentmods.dev/skills/hkuds/openspace/pptx-debug-workflow"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pptx-debug-workflow.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00017 | $0.01107 |
| Opus 5 | $0.00009 | $0.00553 |
| Sonnet 5 | $0.00003 | $0.00221 |
| Haiku 4.5 | $0.00002 | $0.00111 |
Grade A, and why
pptx-debug-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 4d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PPTX Debugging Workflow
A systematic approach to creating and debugging PowerPoint presentations using the python-pptx library. This workflow ensures reliable presentation generation through iterative verification and error resolution.
Step 1: Verify Installation
Before writing any code, verify that python-pptx is installed:
pip show python-pptx
If not installed:
pip install python-pptx
Step 2: Test Minimal PPTX Creation
Create and run a minimal test script to verify the library works:
from pptx import Presentation
prs = Presentation()
slide = prs.slides.add_slide(prs.slide_layouts[0])
slide.shapes.title.text = "Test Slide"
prs.save("test.pptx")
print("Test presentation created successfully")
Run this first to confirm basic functionality before building complex presentations.
Step 3: Write Full Script to File
Always write your presentation script to a .py file instead of using heredoc or inline execution. This enables:
- Better error messages with line numbers
- Easier iteration and debugging
- Preserved state between error fixes
Example structure:
from pptx import Presentation
from pptx.util import Inches, Pt
from pptx.enum.shapes import MSO_SHAPE
from pptx.enum.chart import XL_CHART_TYPE, XL_LEGEND_POSITION
from pptx.chart.data import CategoryChartData
def create_presentation():
prs = Presentation()
# Build slides here
prs.save("output.pptx")
print("Presentation saved successfully")
if __name__ == "__main__":
create_presentation()
Step 4: Execute and Capture Full Traceback
Run the script with full error output:
python your_script.py 2>&1 | tee debug_output.log
This captures the complete Python traceback including:
- Error type and message
- Line numbers where errors occurred
- Stack trace for debugging
Step 5: Fix API Errors Iteratively
Common python-pptx API patterns to remember:
Chart Legend Positioning
Use XL_LEGEND_POSITION (NOT XL_CHART_TYPE) for legend positioning:
from pptx.enum.chart import XL_LEGEND_POSITION
chart.chart.has_legend = True
chart.chart.legend.position = XL_LEGEND_POSITION.RIGHT
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.
- 4d ago First seen · 159 lines · 17 tokens per session scan A e44fce274a47
pptx-debug-workflow is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 26d ago), licensed MIT. It adds 17 tokens to every session and 1,107 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
pdf-backends
Change or diagnose Xberg PDF extraction, native/Pdfium backend selection, PDF rendering sessions, encrypted documents, OCR fallback, or backend-specific capability gaps. Load for PDF engine work, not generic image OCR.
fastgpt-docker-deploy
FastGPT Docker Compose self-hosting deployment workflow. Use when a user asks an AI agent to deploy FastGPT with Docker by referencing /deploy/SKILL.md, including creating an empty fastgpt directory, running the install script in non-interactive mode, starting Docker Compose, monitoring service health, troubleshooting…
sk-create-diff
Local, Git-free before/after review of an edited document (text, Markdown, HTML, DOCX, text PDF) as a self-contained HTML report.
documentation-build
Validates documentation builds successfully. Use when checking Sphinx/RTD build integrity or diagnosing build failures. Reports errors, warnings, and build configuration issues.
vault-lint
Use when the user wants a health audit of the vault — stale content, provenance drift, frontmatter compliance, or MOC coverage gaps. Triggers on "lint vault", "vault health", "check vault", "stale notes", "vault audit", "vault quality", or "check conventions".
wiki-doctor
Run the wiki system doctor to validate wiki structure, skills, rules, automations, and config sync. Use when the user says "doctor", "audit", "health check", "validate wiki", "check my system", "run doctor", or when you suspect something is misconfigured after a large edit session.