SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill radar-vital-signsgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/radar-vital-signs)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/radar-vital-signs"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/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/benchflow-ai/skillsbench/radar-vital-signs"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/radar-vital-signs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- radar-vital-signs — 100% identical, 0 lines differ
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.
- 8d ago First seen · 115 lines · 171 tokens per session scan A b003f824a411
radar-vital-signs is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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pylabrobot
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