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/dsp-algorithm-guide

Implementation guide for common DSP algorithms. Use when the user asks "implement a lowpass filter", "how do I write a compressor", "reverb algorithm", "FFT convolution", "pitch shifter", or "oscillator with no aliasing". Trigger on phrases like "biquad filter", "state variable

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sonic-skills
2018 skills
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$ npx -y skills add kunitoki/sonic-skills --skill dsp-algorithm-guide --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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Implementation guide for common DSP algorithms. Use when the user asks "implement a lowpass filter", "how do I write a compressor", "reverb algorithm", "FFT convolution", "pitch shifter", or "oscillator with no aliasing". Trigger on phrases like "biquad filter", "state variable

SKILL.md

dsp-algorithm-guide.SKILL.md
name: dsp-algorithm-guide
description: >
  Implementation guide for common DSP algorithms. Use when the user asks "implement a
  lowpass filter", "how do I write a compressor", "reverb algorithm", "FFT convolution",
  "pitch shifter", or "oscillator with no aliasing". Trigger on phrases like "biquad
  filter", "state variable filter", "feedback delay network", "phase vocoder",
  "overlap-add", "PolyBLEP", or any request to implement a specific DSP processing block.

DSP Algorithm Guide

Three steps: pick the right algorithm family, address implementation concerns, then verify correctness.

Step 1 — Identify the algorithm family

| User request | Algorithm family | Recommended starting point | |---|---|---| | Lowpass / highpass / bandpass / notch | Biquad (IIR) | Direct Form II transposed | | High-quality parametric EQ, resonant filter | Biquad cascade or SVF | State Variable Filter (SVF) | | Shelving / high-shelf / low-shelf | Biquad (Audio EQ Cookbook) | Peaking EQ or shelf coefficients | | Reverb (algorithmic) | FDN or Schroeder/Moorer | Feedback Delay Network (FDN) | | Reverb (convolution / IR) | OLA / OLS | Overlap-Add with FFT | | Compressor / limiter / expander / gate | Level detector + gain computer | RMS or peak detector + knee curve | | Pitch shift / time stretch | Phase vocoder or granular | Phase vocoder for tonal material | | Anti-aliased oscillator | BLIT / PolyBLEP / wavetable | PolyBLEP for simple waveforms | | Spectral processing / analysis | STFT pipeline | Overlap-Add / Overlap-Save | | Waveshaper / saturation | Memoryless nonlinearity | Soft-clip with oversampling |

Step 2 — Implementation concerns

Work through this checklist before writing any DSP code:

  • [ ] **Stability** — confirm the algorithm's stability condition and check it after every coefficient update (e.g., biquad poles inside unit circle).
  • [ ] **Coefficient precision** — use `double` for coefficient computation; cast to `float` for the inner loop only if profiling demands it.
  • [ ] **Denormal protection** — use `ScopedNoDenormals`/FTZ where available, or add explicit state clamps/noise around feedback paths.
  • [ ] **Latency reporting** — if the algorithm introduces latency (e.g., FFT block size), call `setLatencySamples()` so the host can compensate.
  • [ ] **Initialisation** — clear all delay-line and state memory in `prepareToPlay`; never assume zero-init across transport loops.
  • [ ] **Parameter smoothing** — ramp filter coefficients or gain values over a block; abrupt changes cause clicks and, for IIR filters, instability.
  • [ ] **Oversampling** — nonlinear stages (waveshapers, saturators) need at least 2x oversampling to suppress alias products.

Step 3 — Test the implementation

  • [ ] Frequency response: compare magnitude/phase against the reference formula at multiple frequencies (e.g., plot with Python or MATLAB).
  • [ ] Step response: feed a unit step; verify overshoot and decay match the algorithm's expected behaviour.
  • [ ] Silence-in → silence-out: verify the output reaches and stays at zero after silent input (no denormal crawl).
  • [ ] No NaN / Inf: run with extreme parameter values (fc = 1 Hz, fc = Nyquist − 1 Hz, gain = 0 dB, gain = +24 dB).
  • [ ] Impulse response (convolution / reverb): verify IR matches the target via inverse FFT.
  • [ ] CPU profiling: measure per-block worst case before shipping; compare against the buffer deadline on the target CPU instead of relying on fixed ns/sample budgets.

See `references/algorithm-cookbook.md` for difference equations, stability conditions, coefficient mappings, and numerical tips for each family.

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Precision audio-engineering skills for AI agents. Sonic Skills is a curated pack of Markdown skills for reviewing, debugging, explaining, and implementing audio software.

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