radar-vital-signs

radar-vital-signs is a skill for Claude Code, Codex from xuansenpa1/skillrevise. It costs 171 tokens per session (1,843 once invoked), scanned A, a copy of radar-vital-signs, MIT.

A signal-processing pipeline for measuring heart rate and breathing rate from raw short-range radar recordings. It handles both continuous-wave and frequency-modulated radar data, including the raw in-phase and quadrature signal format.

In plain words
What is it for?
Use it to read radar binary captures, select a person's distance from the sensor, extract motion phase, and estimate heart and breathing rates in beats or breaths per minute.
Why use it?
It removes the difficult preparation work between radar hardware and physiological measurements. It cleans the recording and separates the parts of the signal linked to breathing and heartbeats.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to read radar binary captures, select a person's distance from the sensor, extract motion phase, and estimate heart and breathing rates in beats or breaths per minute.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xuansenpa1/skillrevise/radar-vital-signs
Install

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.

Any agent
npx skills add xuansenpa1/skillrevise --skill radar-vital-signs
Clone the repo
git clone --depth 1 https://github.com/xuansenpa1/skillrevise

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for radar-vital-signs

README.md
[![agentmods](https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/radar-vital-signs/github.svg)](https://agentmods.dev/skills/xuansenpa1/skillrevise/radar-vital-signs)
Your own site
<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.

agentmods 80×15 button for radar-vital-signs

Your own site · 80×15
<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>
Per session 171 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,843 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash b003f824a411, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

Origin

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.

data/skillsbench/tasks/radar-vital-signs/environment/skills/radar-vital-signs/SKILL.md · 115 lines

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)

  1. 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.
  2. (FMCW only) Range FFT across fast-time samples of each chirp → range matrix R[n_chirp, n_range_bin]. CW skips this step.
  3. Remove static clutter. Subtract the temporal mean:
    • CW: iq -= iq.mean()
    • FMCW: R -= R.mean(axis=0, keepdims=True)
  4. (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.
  5. Extract phase with unwrap:
    phase = np.unwrap(np.angle(iq_or_bin))
    phase -= phase.mean()
    
  6. 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)
    
  7. 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)
    
  8. 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])]
    
  9. HR harmonic rejection — always run:
    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
    
    See references/harmonic-pitfalls.md for why this matters and mitigations for slow-breather respiration harmonics leaking into the HR band.

Read the full file on GitHub · 115 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 115 lines · 171 tokens per session scan A b003f824a411

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

neuroskill-bci

Use live BCI cognitive and mood state from NeuroSkill.

NousResearch/hermes-agent · 18 tokens

ruview-advanced-sensing

Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection…

ruvnet/RuView · 84 tokens

ruview-applications

Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually do something…

ruvnet/RuView · 79 tokens

lab-hardware-cad

Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…

K-Dense-AI/scientific-agent-skills · 106 tokens

opentrons-integration

Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow…

K-Dense-AI/scientific-agent-skills · 79 tokens

pylabrobot

Develop and review PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Use for PyLabRobot protocols or API questions; keep physical execution behind an explicit operator safety gate.

K-Dense-AI/scientific-agent-skills · 49 tokens