RuView is a WiFi sensing platform that uses disturbances in radio signals, captured by low-cost ESP32 sensors, to detect presence, movement, breathing, and heart rate without cameras or wearables. It is intended for spatial monitoring and smart-home integrations. The catalogue add-ons support workflows for operating and integrating RuView.
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 ruvnet/RuView --skill evolvegit clone --depth 1 https://github.com/ruvnet/RuViewWrote 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/ruvnet/ruview/evolve)<a href="https://agentmods.dev/skills/ruvnet/ruview/evolve"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/evolve/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/ruvnet/ruview/evolve"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/evolve.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 MCP Rug Pull · line 24 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00026 | $0.00707 |
| Opus 5.5 | $0.00010 | $0.00283 |
| Sonnet 5.5 | $0.00005 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
evolve 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 1mo 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:
- evolve — 95% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evolve — Darwin Mode self-improvement
wifi-densepose-sar-harness ships with Darwin Mode (@metaharness/darwin, ADR-070…146): the model
is frozen; the harness evolves. Each generation mutates ONE of the 7 surface files
(planner, contextBuilder, reviewer, retry/tool/memory/score policy), sandboxes each
child, scores it, and keeps only variants that measurably improve — building an
archive of successful descendants.
Run it
npm run evolve # real substrate: runs your test command per variant (deterministic mutator — no API key, no network)
npm run evolve:dry # mock substrate: fast, fully offline, no test execution
Or directly:
npx metaharness-darwin evolve . --sandbox real --generations 3 --children 4
Safety (secure by default)
- Deterministic mutator is the default — no network, no API key, air-gapped.
- Every mutation passes the
validateGeneratedCodegate: no new imports, network, filesystem, shell, env access, or dependencies — pure refactor/tuning only. - Mutations run in a sandbox; only variants that pass your tests are archived.
- Nothing is promoted without measured improvement (guard against Goodharting).
See @metaharness/darwin for selection strategies (--selection, --crossover,
--curriculum), statistical gates (--fdr, --bench), and the real-LLM mutator (library API).
What the benchmarks taught us (measured, full SWE-bench Lite 300)
Defaults worth carrying into how you evolve and run this harness (full evidence + CIs in
@metaharness/darwin's LEARNINGS.md / bench/results/RESULTS.md):
- Closed-loop repair is the #1 lever (~2×). Feeding test/compiler failure back and retrying took resolve-rate 7.7% → 15.3% on the same cheap model. Iterate against ground truth, don't single-shot.
- Cheap-first + cost-aware routing. Track $/resolve, not just resolve-rate; a cheap model resolved 31× cheaper per fix than a frontier one. Reserve frontier for measured capability gaps.
- Tier the models (Barbarian & Scholar). Cheap sweep + frontier on only the residual = 33.3% at ~6× lower cost than running frontier everywhere.
- Put the output-format contract in a system message + example, and size prompts to the model's real context window — this alone took a weak local model from 0% to ~50% valid output.
- Only trust batch evaluation of the final artifact — in-loop counters drift 1.5–5×.
- The harness multiplies the model; it can't rescue one below the task's reasoning floor. Pick the smallest model above the floor, then let evolution do the rest.
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.
- 1mo ago First seen · 54 lines · 26 tokens per session scan A 05965b34edd9
evolve is a skill published in the GitHub repository ruvnet/RuView (98,920 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 707 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
meraki-wireless-ops
Cisco Meraki wireless (read-only) — SSID configuration, RF profiles, Air Marshal, channel utilization, signal quality, client connectivity events via Cisco's official Meraki MCP. Use when inspecting Meraki SSIDs, auditing RF configuration, or investigating WiFi connectivity.
branch_health
Quick health check -- test counts and file stats for AIPass branches.
monitor-test-run
Watch a running Kobiton test run and narrate it to the user: read the org's live-remediation flag up front, poll the run until every execution is terminal, surface the live-remediation URL the moment an execution is blocked, and give a correct post-mortem so a COMPLETED-with-BLOCKERENCOUNTERED execution is never…
create-test-run
Create a Kobiton test run from a test case or suite, then offer to monitor it. When the user gives only partial details (or just a test case id), fill the rest with sensible defaults that match the createTestRun schema, show a summary of what will run, and ask to proceed or customize before creating. After the run is…