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 audio-track-production-enhanced-enhanced-enhancedgit 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/audio-track-production-enhanced-enhanced-enhanced)<a href="https://agentmods.dev/skills/hkuds/openspace/audio-track-production-enhanced-enhanced-enhanced"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production-enhanced-enhanced-enhanced/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/audio-track-production-enhanced-enhanced-enhanced"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production-enhanced-enhanced-enhanced.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00023 | $0.05796 |
| Opus 5 | $0.00012 | $0.02898 |
| Sonnet 5 | $0.00005 | $0.01159 |
| Haiku 4.5 | $0.00002 | $0.00580 |
Grade A, and why
diagnostic-stem-delivery 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 12d 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 — 634 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnostic Stem Audio Production Workflow
This skill provides a resilient pattern for audio production that emphasizes diagnostic analysis before editing, explicit timecode extraction from documents, incremental verification, fail-fast principles, and mandatory deliverable verification. Each major step produces verified outputs before proceeding, with comprehensive audio diagnostics at specified timecodes.
Overview
Follow these steps in strict order. Each step must complete successfully and pass verification before proceeding to the next:
- Parse timecodes from source documents - Extract edit spots/timecodes from DOCX/text sources
- Perform diagnostic audio analysis - Analyze reference audio at each timecode (pitch, clicks, frequency)
- Calculate timing parameters - Derive section transitions from BPM and duration
- Verify reference audio - Validate input file properties and extract target duration
- Generate and verify each stem individually - One stem at a time with immediate verification
- Detect and resolve duration mismatches - Apply appropriate extension strategy
- Apply edits based on diagnostics - Make informed edits using analysis results
- Mix with verification - Combine stems and verify mix integrity
- Export and verify deliverable - Generate final output with comprehensive checks
Key Principles
- Diagnostics first: Analyze audio at edit points BEFORE making any changes
- Document-driven: Parse timecodes directly from source documents (DOCX, TXT)
- Incremental verification: Verify each stem immediately after generation
- Fail-fast approach: Stop and report errors at each step
- Mandatory export: Final step MUST produce verified deliverable file
- Tool reliability: Use run_shell with inline Python for audio processing (avoid execute_code_sandbox for audio)
Step 0: Parse Timecodes from Source Documents
Extract edit spots and timecodes from document sources. Use python-docx via run_shell for reliable DOCX parsing:
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.
- 12d ago First seen · 634 lines · 23 tokens per session scan A 45e513b583fe
diagnostic-stem-delivery is a skill published in the GitHub repository HKUDS/OpenSpace (7,561 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 5,796 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 2026-08-30.
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