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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/HKUDS/OpenSpacenpx agentmods add skills/hkuds/openspace/audio-track-production-enhanced-enhanced-b8f537Wrote 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-b8f537)<a href="https://agentmods.dev/skills/hkuds/openspace/audio-track-production-enhanced-enhanced-b8f537"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production-enhanced-enhanced-b8f537/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-b8f537"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production-enhanced-enhanced-b8f537.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 | $0.00025 | $0.06855 |
| Opus 5 | $0.00013 | $0.03427 |
| Sonnet 5 | $0.00005 | $0.01371 |
| Haiku 4.5 | $0.00003 | $0.00685 |
Grade A, and why
adaptive-stem-alignment 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 — 739 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adaptive Stem Alignment Workflow
This skill provides a resilient pattern for audio production that emphasizes incremental verification, fail-fast principles, and adaptive duration handling. Each major step produces verified outputs before proceeding, with explicit strategies for handling stems of different durations.
Overview
Follow these steps in strict order. Each step must complete successfully and pass verification before proceeding to the next:
- Early timing calculation - Derive section transitions from BPM and duration first
- Verify reference audio - Validate input file properties and establish target duration
- Generate and verify each stem individually - One stem at a time with immediate verification
- Generate drum stem separately - Dedicated drum extension with rhythm patterns
- Align stem durations - Handle duration mismatches with adaptive extension strategies
- Apply effects with verification - Process each stem and verify output
- Export master track - Mix all verified stems
- Archive and final verification - Package deliverables with comprehensive checks
Key Differences from Standard Workflow
- Incremental verification: Verify each stem immediately after generation, not just at the end
- Fail-fast approach: Stop and report errors at each step rather than accumulating failures
- Early timing: Calculate section transitions before any audio generation
- Separated drums: Drum stem generation is a distinct step with rhythm-specific processing
- Memory-efficient: Process stems individually to avoid large array operations that cause sandbox failures
- Adaptive duration handling: Explicit strategies for mismatched stem durations (zero-padding, looping, crossfade extension)
- Pre-mix alignment: Verify all stems match target duration before mixing
Step 1: Calculate Timing Parameters (Early)
Calculate all timing parameters before generating any audio. This ensures consistent timing across all stems:
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 · 739 lines · 25 tokens per session scan A 84df122a0278
adaptive-stem-alignment is a skill published in the GitHub repository HKUDS/OpenSpace (7,565 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 6,855 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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