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-vital-signsgit 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-vital-signs)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/radar-vital-signs"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/radar-vital-signs/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-vital-signs"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/radar-vital-signs.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 | $0.00171 | $0.01843 |
| Opus 5 | $0.00086 | $0.00922 |
| Sonnet 5 | $0.00034 | $0.00369 |
| Haiku 4.5 | $0.00017 | $0.00184 |
Grade A, and why
radar-vital-signs 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-vital-signs — 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.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Radar Vital-Sign Extraction
End-to-end pipeline: raw radar I/Q → cleaned phase signal → HR and BR in bpm.
Full pipeline (every step, in order)
- Parse binary I/Q into a complex 1-D array (CW) or 2-D range matrix (FMCW). Use the JSON/YAML sidecar to determine format — never assume. See references/iq-formats.md.
- (FMCW only) Range FFT across fast-time samples of each chirp → range matrix
R[n_chirp, n_range_bin]. CW skips this step. - Remove static clutter. Subtract the temporal mean:
- CW:
iq -= iq.mean() - FMCW:
R -= R.mean(axis=0, keepdims=True)
- CW:
- (FMCW only) Pick the subject range bin within a physical prior window (e.g., 0.3–1.5 m for a seated subject). See references/range-bin.md.
- Extract phase with unwrap:
phase = np.unwrap(np.angle(iq_or_bin)) phase -= phase.mean() - Decimate to ~50 Hz if
fs >= 500 Hz(sub-Hz filtering at kHz is numerically unstable):from scipy.signal import decimate phase_ds = decimate(phase, q=int(fs/50), ftype='iir', zero_phase=True) fs_new = fs / int(fs/50) - Two separate bandpasses — BR and HR:
b_br, a_br = butter(4, [0.08, 0.5], btype='band', fs=fs_new) b_hr, a_hr = butter(4, [0.7, 3.0], btype='band', fs=fs_new) br_sig = filtfilt(b_br, a_br, phase_ds) hr_sig = filtfilt(b_hr, a_hr, phase_ds) - Peak frequency via zero-padded Welch PSD (each band):
nperseg = min(len(x), int(fs_new * 25)) f, p = welch(x, fs=fs_new, nperseg=nperseg, noverlap=nperseg//2, nfft=8*nperseg, detrend='constant') mask = (f >= lo) & (f <= hi) peak_hz = f[mask][np.argmax(p[mask])] - HR harmonic rejection — always run:
See references/harmonic-pitfalls.md for why this matters and mitigations for slow-breather respiration harmonics leaking into the HR band.f_sub = f_peak_hr / 2.0 if 0.7 <= f_sub <= 3.0: p_sub = np.interp(f_sub, f, p) p_top = np.interp(f_peak_hr, f, p) if p_sub > 0.5 * p_top: f_peak_hr = f_sub # peak was the 2nd harmonic hr_bpm = f_peak_hr * 60
What ships with it
5 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 · 115 lines · 171 tokens per session scan A b003f824a411
radar-vital-signs is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 7d ago), licensed MIT. It adds 171 tokens to every session and 1,843 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to radar-vital-signs, differing in 0 lines, and is treated as a copy.
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