DSPAlgorithmAgent_Skill

Develop fixed-point DSP algorithms for embedded audio DSP platforms.

Updated Jun 8, 2026
One-click install
npx skills add https://github.com/Qiuu2/algo --skill dspalgorithmagent-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: DSPAlgorithmAgent_Skill
Source: https://github.com/Qiuu2/algo/tree/main/agents/dsp-algorithm
Command: npx skills add https://github.com/Qiuu2/algo --skill dspalgorithmagent-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill removes the guesswork and repetitive trial-and-error in developing digital signal processing algorithms for embedded audio products, ensuring algorithms meet strict real-time performance, memory, and audio quality requirements on target DSP hardware.

Core Features & Use Cases

  • Full algorithm lifecycle support: Covers workflow from floating-point MATLAB prototyping to fixed-point C implementation and formal delivery.
  • Chip-specific optimization guides: Includes verified performance data, constraints, and best practices for common audio DSPs including ADAU1467, Tensilica HiFi4, and ADSP-21569.
  • Fixed-point risk mitigation: Provides a curated library of common overflow, precision loss, and stability pitfalls with proven solutions to avoid costly hardware bring-up failures.
  • Use Case: An audio engineer building a conference microphone array can use this Skill to implement an MVDR beamformer optimized for the SHARC21569 chip, avoiding common fixed-point errors and meeting a 5ms end-to-end latency budget.

Quick Start

Use the DSPAlgorithmAgent_Skill skill to design a fixed-point 8-channel DSB beamformer for the ADAU1467 chip with a 5ms latency budget and 70% MIPS utilization target.

Frequently Asked Questions about DSPAlgorithmAgent_Skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to optimize an MVDR beamformer for the ADSP-21569 chip?▼

Optimizing an MVDR beamformer for the ADSP-21569 chip requires applying chip-specific performance data and constraints to meet strict latency budgets, such as a 5ms end-to-end requirement. This Skill provides verified performance data, constraints, and best practices for common audio DSPs including the ADSP-21569. It guides you through chip adaptation and fixed-point optimization to ensure your beamforming algorithm meets real-time performance, memory, and audio quality requirements.

Does this Skill support adaptation for the Tensilica HiFi4 and ADAU1467 platforms?▼

Yes, this Skill supports chip adaptation for the Tensilica HiFi4 and ADAU1467 platforms, providing verified performance data and specific constraints. It includes chip-specific optimization guides with best practices for these common audio DSPs. You can use it to design and verify algorithms like a fixed-point 8-channel DSB beamformer optimized specifically for the ADAU1467 chip with defined latency and MIPS targets.

How do I avoid fixed-point overflow and precision loss when developing real-time audio algorithms?▼

Avoiding fixed-point overflow and precision loss when developing real-time audio algorithms requires using a curated library of common pitfalls with proven solutions to prevent costly hardware bring-up failures. This Skill provides a fixed-point risk mitigation library detailing common overflow, precision loss, and stability issues. It supplies standardized verification processes to ensure algorithms meet strict real-time performance and audio quality requirements before deployment.

Why do my spatial audio rendering algorithms fail to meet real-time latency budgets on embedded DSP hardware?▼

Spatial audio rendering algorithms fail to meet real-time latency budgets on embedded DSP hardware due to unoptimized fixed-point implementations and lack of chip-specific performance tuning. This Skill removes repetitive trial-and-error by providing standardized workflows, chip adaptation guides, and verification processes for platforms like ADSP-21569. It ensures your algorithms meet strict real-time performance, memory, and audio quality requirements on target hardware.

DSPAlgorithmAgent_Skill: what embedded audio DSP use cases are supported?▼

Supported embedded audio DSP use cases include beamforming, adaptive noise cancellation, spatial audio rendering, and fixed-point implementation for DSP platforms such as ADAU1467, Tensilica HiFi4, and ADSP-21569. The Skill solves the challenge of developing production-grade digital signal processing algorithms for these specific audio applications. It provides the necessary workflows and chip adaptation guides to ensure algorithms meet real-time performance and audio quality requirements.