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Agent skill

Radar Vital Signs

by benchflow-ai in benchflow-ai/skillsbench

End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI…

Apache-2.0Auto-check passed

Install Radar Vital Signs

skills CLI
$ npx skills add benchflow-ai/skillsbench --skill radar-vital-signs -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install benchflow-ai/skillsbench radar-vital-signs --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/radar-vital-signs/environment/skills/radar-vital-signs .claude/skills/radar-vital-signs && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
radar-vital-signs
GitHub stars
1.8k
Token cost
~1.6k tokens
SKILL.md length
573 words
Files
6 (incl. references)
Skills in repo
189
Repo updated
First seen
Licence
Apache-2.0

At a glance

End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI…

  • Works in 10 steps: Parse binary I/Q into a complex 1-D… → (FMCW only) Range FFT across fast-time… → Remove static clutter. Subtract the… → …
  • Claude needs to parse interleaved I/Q binary
  • SKILL.md covers Full pipeline (every step, in…, Critical rules, Band edges: use these, not… and Decision rules, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Radar Vital Signs is an agent skill from benchflow-ai/skillsbench. End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI IWR/AWR). Use when Claude needs to parse interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, design HR/BR bandpass filters, pick a peak frequency via PSD, reject the HR second harmonic that often dominates the fundamental, or handle…

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/band-rationale.md`, `references/debugging.md` and `references/harmonic-pitfalls.md`).

The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.

When your agent uses it

  • Claude needs to parse interleaved I/Q binary
  • Do a Range FFT on FMCW chirps
  • Remove static clutter
  • Pick a subject range bin

Example prompts

  • “/radar-vital-signs”

Requirements

  • Python 3

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  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…
  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
  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
  6. Decimate to ~50 Hz if fs >= 500 Hz (sub-Hz filtering at kHz is numerically unstable)
  7. Two separate bandpasses — BR and HR
  8. Peak frequency via zero-padded Welch PSD (each band)
  9. HR harmonic rejection — always run
  10. Cross-check with autocorrelation (optional but recommended)

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Radar Vital Signs loads about 1.6k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 179 tokens; SKILL.md has 573 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~179
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 573 words, ~1,569 tokens.

Download SKILL.mdSave it as .claude/skills/radar-vital-signs/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
radar-vital-signs
description
End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI IWR/AWR). Use when Claude needs to parse interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, design HR/BR bandpass filters, pick a peak frequency via PSD, reject the HR second harmonic that often dominates the fundamental, or handle respiration-harmonic leakage on slow breathers. Not for pulse/UWB range gating, MIMO beamforming, Doppler-only gesture radar, arrhythmia detection, or multi-subject source separation.

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:

    python
    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):

    python
    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:

    python
    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):

    python
    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:

    python
    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.

  10. Cross-check with autocorrelation (optional but recommended):

    python
    ac = np.correlate(x - x.mean(), x - x.mean(), mode='full')
ac = ac[len(ac)//2:] / ac[len(ac)//2]
lag = int(fs_new/f_hi) + np.argmax(ac[int(fs_new/f_hi):int(fs_new/f_lo)])
bpm_ac = 60 * fs_new / lag

    If abs(bpm_ac - bpm_psd) > 5, flag as low confidence.

Critical rules

rulewhy
Use phase, not magnitude1 mm motion at 24 GHz ≈ 1 rad; magnitude costs ~40 dB of SNR. np.abs(iq) is almost always wrong for mm-scale motion
Clutter removal before np.angleDC offset anchors phase off zero, eats the ±π unwrap budget
Decimate before sub-Hz bandpassSciPy biquad silently NaNs at very-low normalized cutoffs
Two separate BR / HR bandpassesHR is 10×–100× smaller than BR; single wide filter can't separate them
Zero-pad Welch PSD (nfft=8*nperseg)Raw bin spacing fs/nperseg is often coarser than tolerance
Always run HR sub-harmonic check2nd harmonic of cardiac pulse frequently dominates fundamental
Never argmax(magnitude) across all range bins (FMCW)DC bin and static reflectors dominate; restrict to subject-range window
Show full SKILL.md (260 more words)Show less

Band edges: use these, not textbook 0.1–0.5 / 0.8–2.5

bandusetextbookwhy
BR lower0.08 Hz (4.8 bpm)0.1 Hz (6 bpm)slow breathers (supine, meditation, sleep) routinely below 6 bpm
HR lower0.7 Hz (42 bpm)0.8 Hz (48 bpm)bradycardia (athletes, post-tilt-down, β-blockers) below 48 bpm
HR upper3.0 Hz (180 bpm)2.5 Hz (150 bpm)post-exercise and children exceed 150 bpm

Widen only with specific justification. See references/band-rationale.md.

Decision rules

IfThen
f_peak/2 in HR band and p_sub > 0.5 × p_topPick sub-harmonic (fundamental)
PSD and autocorrelation disagree > 5 bpmFlag low confidence; don't commit to one value
BR estimate < 10 bpm (slow breather)Expect HR-band contamination — notch 2·BR, 3·BR. See harmonic-pitfalls.md
HR > 150 bpm (tachycardia)Widen HR band upper to 3.3 Hz, re-estimate
Clip < 15 sPSD resolution > tolerance — prefer autocorrelation, or flag inconclusive

Sanity checks before reporting

  • BR < HR always for a live adult at rest. Violated ⇒ swapped bands.
  • HR × duration_minutes ≈ peak count in find_peaks(bandpassed_hr). Off by 2× ⇒ harmonic error slipped through.
  • Resting adult plausibility: HR 50–90 bpm, BR 10–20 bpm. Way outside ⇒ re-check band edges, decimation, and harmonic rejection.

When things go wrong

If output looks like garbage, walk through references/debugging.md — fast checks that catch most ingestion and SNR bugs.

Not in scope

  • Pulse / UWB range-gated radar (different pipeline entirely).
  • MIMO angle-of-arrival — needs beamforming first.
  • Doppler-only gesture radar — use slow-time FFT, not bin phase.
  • Arrhythmia / irregular rhythms — use R-peak / foot detection + RR-interval analysis, not PSD.
  • Multiple subjects in one signal — run source separation first.
  • Rapidly non-stationary rate (exercise ramp) — use a spectrogram, not single-window PSD.

© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in tasks/radar-vital-signs/environment/skills/radar-vital-signs of benchflow-ai/skillsbench.

  • SKILL.md
  • references/band-rationale.md
  • references/debugging.md
  • references/harmonic-pitfalls.md
  • references/iq-formats.md
  • references/range-bin.md

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Radar Vital Signs next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Radar Vital Signs compared with similar skills
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Radar Vital Signs this skillbenchflow-ai/skillsbench1.8k—~1.6kAutomated safety check: PassApache-2.0
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Document Signingasgeirtj/system_prompts_leaks69k—~1.5kAutomated safety check: PassCC0-1.0
Brand Extractnexu-io/open-design100k—~3.1kAutomated safety check: PassApache-2.0
Design Extractnexu-io/open-design100k—~549Automated safety check: PassApache-2.0
Kg Extractruvnet/ruflo74k—~751Automated safety check: NotesMIT

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Questions about Radar Vital Signs

What does Radar Vital Signs do?

End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI…. Radar Vital Signs is an agent skill from benchflow-ai/skillsbench. End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI IWR/AWR).

When should I use Radar Vital Signs?

Radar Vital Signs fits situations like: Claude needs to parse interleaved I/Q binary; do a Range FFT on FMCW chirps; remove static clutter; pick a subject range bin.

How do I install Radar Vital Signs in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill radar-vital-signs -a claude-code`. Or copy the skill folder (tasks/radar-vital-signs/environment/skills/radar-vital-signs in benchflow-ai/skillsbench) into .claude/skills/radar-vital-signs in your project. Claude Code loads it when a task matches its description.

How do I install Radar Vital Signs in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill radar-vital-signs -a codex`. Or copy the skill folder (tasks/radar-vital-signs/environment/skills/radar-vital-signs in benchflow-ai/skillsbench) into .agents/skills/radar-vital-signs in your project. Codex loads it when a task matches its description.

Can I use Radar Vital Signs in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill radar-vital-signs -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/radar-vital-signs, .gemini/skills/radar-vital-signs, .github/skills/radar-vital-signs and .opencode/skills/radar-vital-signs in your project.

What does Radar Vital Signs need to run?

SKILL.md names no scripts, command-line tools or credentials: Radar Vital Signs is instructions for the agent only. Our summary lists: Python 3.

Does Radar Vital Signs access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Radar Vital Signs safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Radar Vital Signs use?

Radar Vital Signs is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Radar Vital Signs use?

About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.5k tokens, read only when the agent opens those files.

What are the alternatives to Radar Vital Signs?

Skills that share tags, products or a category with Radar Vital Signs: Extract (alirezarezvani/claude-skills, 28k stars), Document Signing (asgeirtj/system_prompts_leaks, 69k stars), Brand Extract (nexu-io/open-design, 100k stars) and Design Extract (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Radar Vital Signs?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.