Signals
PostHog/posthog
How to query the documentembeddings table for raw signal data using HogQL.
Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis.
Apache-2.0Auto-check passed
Install Radar Signal Processing
$ npx skills add benchflow-ai/skillsbench --skill radar-signal-processing -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench radar-signal-processing --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-signal-processing .claude/skills/radar-signal-processing && rm -rf skills-srcUse ~/.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/
Install the "radar-signal-processing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/radar-vital-signs/environment/skills/radar-signal-processing into .claude/skills/radar-signal-processing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-signal-processing", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/radar-vital-signs/environment/skills/radar-signal-processingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add benchflow-ai/skillsbench --skill radar-signal-processing -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench radar-signal-processing --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/radar-vital-signs/environment/skills/radar-signal-processing .agents/skills/radar-signal-processing && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "radar-signal-processing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/radar-vital-signs/environment/skills/radar-signal-processing into .agents/skills/radar-signal-processing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-signal-processing", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill radar-signal-processing -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench radar-signal-processing --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/radar-vital-signs/environment/skills/radar-signal-processing .cursor/skills/radar-signal-processing && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "radar-signal-processing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/radar-vital-signs/environment/skills/radar-signal-processing into .cursor/skills/radar-signal-processing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-signal-processing", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/radar-vital-signs/environment/skills/radar-signal-processing--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add benchflow-ai/skillsbench --skill radar-signal-processing -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench radar-signal-processing --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/radar-vital-signs/environment/skills/radar-signal-processing .gemini/skills/radar-signal-processing && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "radar-signal-processing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/radar-vital-signs/environment/skills/radar-signal-processing into .gemini/skills/radar-signal-processing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-signal-processing", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install benchflow-ai/skillsbench radar-signal-processingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add benchflow-ai/skillsbench --skill radar-signal-processing -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/radar-vital-signs/environment/skills/radar-signal-processing .github/skills/radar-signal-processing && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "radar-signal-processing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/radar-vital-signs/environment/skills/radar-signal-processing into .github/skills/radar-signal-processing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-signal-processing", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill radar-signal-processing -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench radar-signal-processing --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/radar-vital-signs/environment/skills/radar-signal-processing .opencode/skills/radar-signal-processing && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "radar-signal-processing" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/radar-vital-signs/environment/skills/radar-signal-processing into .opencode/skills/radar-signal-processing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radar-signal-processing", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
radar-signal-processing- GitHub stars
- 1.8k
- Token cost
- ~586 tokens
- SKILL.md length
- 217 words
- Files
- 4 (incl. references)
- Skills in repo
- 178
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis.
- Works in 6 steps: Parse binary I/Q to complex samples. Use… → (FMCW only) Range FFT across fast time →… → Remove static clutter. Subtract the… → …
- Claude needs to read interleaved I/Q binary
- SKILL.md covers Pipeline (do every step in…, Critical rules, When things go wrong and Not in scope
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Radar Signal Processing is an agent skill from benchflow-ai/skillsbench. Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis. Use when Claude needs to read interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, or debug why a radar pipeline is returning garbage. Not for pulse/UWB range gating, MIMO beamforming, or Doppler-only gesture radar.
Its SKILL.md is about 590 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/debugging.md`, `references/iq-formats.md` and `references/range-bin.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 read interleaved I/Q binary
- Do a Range FFT on FMCW chirps
- Remove static clutter
- Pick a subject range bin
Example prompts
- “/radar-signal-processing”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- 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
- (FMCW only) Pick the subject range bin. See references/range-bin.md.
- Extract phase with unwrap
- If fs >= 500 Hz and downstream needs sub-Hz filtering, decimate first
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 Signal Processing loads about 586 tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 114 tokens; SKILL.md has 217 words of instructions outside code blocks.
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). 217 words, ~586 tokens.
Download SKILL.mdSave it as
.claude/skills/radar-signal-processing/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- radar-signal-processing
- description
- Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis. Use when Claude needs to read interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, or debug why a radar pipeline is returning garbage. Not for pulse/UWB range gating, MIMO beamforming, or Doppler-only gesture radar.
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:
python phase = np.unwrap(np.angle(iq_or_bin)) phase -= phase.mean() - If
fs >= 500 Hzand downstream needs sub-Hz filtering, decimate first: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) # ~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.
© 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 3 other files (references) in tasks/radar-vital-signs/environment/skills/radar-signal-processing of benchflow-ai/skillsbench.
- SKILL.md
- references/debugging.md
- references/iq-formats.md
- references/range-bin.md
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Radar Signal Processing 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Radar Signal Processing this skillbenchflow-ai/skillsbench | 1.8k | — | ~586 | Automated safety check: Pass | Apache-2.0 | |
| SignalsPostHog/posthog | 40k | — | ~4.3k | Automated safety check: Pass | Custom licence | |
| Nutrient Document Processingaffaan-m/ECC | 275k | 4 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Ieee Transactions On Signal Processingbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Capturealirezarezvani/claude-skills | 28k | 1 repos | ~2.8k | Automated safety check: Pass | MIT | |
| Signal Detectorgarrytan/gbrain | 31k | — | ~2k | Automated safety check: Pass | MIT |
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Captures and organizes chaotic brain dumps into a structured, actionable system with zero information loss.
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Questions about Radar Signal Processing
What does Radar Signal Processing do?
Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis. Radar Signal Processing is an agent skill from benchflow-ai/skillsbench. Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis.
When should I use Radar Signal Processing?
Radar Signal Processing fits situations like: Claude needs to read interleaved I/Q binary; do a Range FFT on FMCW chirps; remove static clutter; pick a subject range bin.
How do I install Radar Signal Processing in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill radar-signal-processing -a claude-code`. Or copy the skill folder (tasks/radar-vital-signs/environment/skills/radar-signal-processing in benchflow-ai/skillsbench) into .claude/skills/radar-signal-processing in your project. Claude Code loads it when a task matches its description.
How do I install Radar Signal Processing in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill radar-signal-processing -a codex`. Or copy the skill folder (tasks/radar-vital-signs/environment/skills/radar-signal-processing in benchflow-ai/skillsbench) into .agents/skills/radar-signal-processing in your project. Codex loads it when a task matches its description.
Can I use Radar Signal Processing 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-signal-processing -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-signal-processing, .gemini/skills/radar-signal-processing, .github/skills/radar-signal-processing and .opencode/skills/radar-signal-processing in your project.
What does Radar Signal Processing need to run?
SKILL.md names no scripts, command-line tools or credentials: Radar Signal Processing is instructions for the agent only. Our summary lists: Python 3.
Does Radar Signal Processing 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 Signal Processing 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 Signal Processing use?
Radar Signal Processing 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 Signal Processing use?
About 586 tokens (SKILL.md is roughly 2.3k 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 1.8k tokens, read only when the agent opens those files.
What are the alternatives to Radar Signal Processing?
Skills that share tags, products or a category with Radar Signal Processing: Signals (PostHog/posthog, 40k stars), Nutrient Document Processing (affaan-m/ECC, 275k stars), Ieee Transactions On Signal Processing (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Capture (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Radar Signal Processing?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 178 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.