dispatching parallel agents
Explore dispatching parallel agents as a productivity & planning workflow from obra/superpowers. Compare its source, maintenance and review signals before choosing it for your agent.
Explore digital health clinical asr eval as a productivity & planning workflow from NVIDIA/skills. Compare its source, maintenance and review signals before choosing it for your agent.
This source provides a task-specific instruction workflow for Productivity & planning. Its source file is linked below so you can inspect the exact procedure, tools and dependencies. A full editorial review has not been completed.
Exploring Productivity & planning workflows with an agent. Confirm that the source matches your task and environment.
The automated catalog record does not establish task performance or runtime safety. Check supporting scripts and external services in the source before use.
NVIDIA_API_KEY (required) for hosted ASR NIMs via NVCF. A NeMo-format manifest produced by /digital-health-clinical-asr-build (or an externally-provided manifest carrying the clinical-extension fields). All ASR call shapes and WER/CER/KER/SER scoring recipes are inlined — no sibling agent skill required.
Inspect the pinned source snapshot and its supporting files first. The command selects this skill through the open-source skills CLI. Node.js and agent-specific setup may be required. Installation can download code; this catalog never runs it.
npx skills add https://github.com/NVIDIA/skills/tree/f5337e8a388a87bec81ccd960d70174ec2c189bc/skills/digital-health-clinical-asr-eval --skill digital-health-clinical-asr-evalReview pinned skill folder ↗ · CLI documentation ↗
After source review, use npx skills check and npx skills update to inspect and apply upstream updates. Re-check changed instructions before running them.
Static review only. This is not a security audit or a guarantee of safety.
SKILL.md text only; scripts, dependencies and runtime behavior not audited.
Allowed tools declaration: Unknown. Runtime permissions and network behavior: Unknown.
86/100 · Metadata coverage 100% · Model 1.0
Scores summarize observable discovery signals. Popularity and repository activity apply to the whole repository. Unknown runtime safety earns no certification.
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