Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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benchflow-ai/skillsbench18612026年10月13日 更新
免费在 GitHub 公开
radar-signal-processing
Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis. Use when Claude needs to read interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, or debug why a radar pipeline is returning garbage. Not for pulse/UWB range gating, MIMO beamforming, or Doppler-only gesture radar.
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查看安装方法包含的文件（4）
- SKILL.md2.3 KB
- references/debugging.md2.8 KB
- references/iq-formats.md1.9 KB
- references/range-bin.md2.5 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
Radar Signal Processing
Get from a raw binary I/Q capture to a clean 1-D phase trace that downstream vital-signs / motion analysis can consume.
Pipeline (do every step in order)
- Parse binary I/Q to complex samples. Use the JSON/YAML sidecar to determine format — never assume. See references/iq-formats.md.
- (FMCW only) Range FFT across fast time → 2-D range matrix. CW skips this step.
- Remove static clutter. Subtract the temporal mean:
- CW:
iq -= iq.mean() - FMCW:
R -= R.mean(axis=0, keepdims=True)on the range matrix
- CW:
- (FMCW only) Pick the subject range bin. See references/range-bin.md.
- Extract phase with unwrap:
phase = np.unwrap(np.angle(iq_or_bin)) phase -= phase.mean() - If
fs >= 500 Hzand downstream needs sub-Hz filtering, decimate first:from scipy.signal import decimate phase_ds = decimate(phase, q=int(fs/50), ftype='iir', zero_phase=True) fs_new = fs / int(fs/50) # ~50 Hz target
Critical rules
| rule | why (short) |
|---|---|
| Use phase, not magnitude | 1 mm motion at 24 GHz ≈ 1 rad; magnitude is ~40 dB worse SNR |
Clutter removal goes before np.angle | DC offset anchors phase off zero, eats the unwrap budget |
| Never design a 0.1 Hz filter against a 2 kHz signal | SciPy biquad silently NaNs; decimate to ~50 Hz first |
Never argmax(magnitude) across all range bins | DC bin (bin 0) and static reflectors dominate — restrict to a physical subject-range window |
When things go wrong
If the output is garbage, walk through references/debugging.md in order — it's fast and catches most ingestion/SNR bugs.
Not in scope
Pulse/UWB time-of-flight, MIMO angle-of-arrival, Doppler-only gesture radar.
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