Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xuansenpa1/skillrevise --skill radar-signal-processinggit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote 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/xuansenpa1/skillrevise/radar-signal-processing)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/radar-signal-processing"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/radar-signal-processing/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/xuansenpa1/skillrevise/radar-signal-processing"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/radar-signal-processing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00109 | $0.00626 |
| Opus 5 | $0.00055 | $0.00313 |
| Sonnet 5 | $0.00022 | $0.00125 |
| Haiku 4.5 | $0.00011 | $0.00063 |
Grade A, and why
radar-signal-processing 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 9d 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.
This is a copy
100% identical to radar-signal-processing — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
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.
What ships with it
3 files 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.
- 9d ago First seen · 46 lines · 109 tokens per session scan A 0f1194ad676b
radar-signal-processing is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 7d ago), licensed MIT. It adds 109 tokens to every session and 626 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to radar-signal-processing, differing in 0 lines, and is treated as a copy.
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