Borrowing it
Nothing to install: this file belongs to SensorsIot/Embedded-AI-Harness. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SensorsIot/Embedded-AI-Harness/main/.claude/skills/testbench-logging/SKILL.mdgit clone --depth 1 https://github.com/SensorsIot/Embedded-AI-HarnessWrote 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/sensorsiot/embedded-ai-harness/testbench-logging)<a href="https://agentmods.dev/skills/sensorsiot/embedded-ai-harness/testbench-logging"><img src="https://agentmods.dev/badge/skills/sensorsiot/embedded-ai-harness/testbench-logging/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/sensorsiot/embedded-ai-harness/testbench-logging"><img src="https://agentmods.dev/badge/skills/sensorsiot/embedded-ai-harness/testbench-logging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
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 →
- high Tool Misuse · line 216 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high Tool Misuse · line 231 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Privilege Escalation · line 17 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
- medium Data Exfiltration · line 53 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00122 | $0.02746 |
| Opus 5 | $0.00061 | $0.01373 |
| Sonnet 5 | $0.00024 | $0.00549 |
| Haiku 4.5 | $0.00012 | $0.00275 |
Grade B, and why
testbench-logging scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
export TESTBENCH_URL=$(sudo python3 .claude/skills/esp-idf-handling/discover-testbench.py \ Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "$TESTBENCH_URL/api/info" # confirm before anything else How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ESP32 Debug Logging
Base URL: $TESTBENCH_URL — see Step 0
Step 0: Point at a bench
There are several benches and their addresses move, so nothing here writes one
down. $BENCH is not usable either — a container cannot resolve mDNS.
Discover the bench and export its URL:
export TESTBENCH_URL=$(sudo python3 .claude/skills/esp-idf-handling/discover-testbench.py \
--url --name <bench-hostname>)
curl -s "$TESTBENCH_URL/api/info" # confirm before anything else
--url refuses to guess when more than one bench answers, so --name is
required whenever a second bench is powered on. TESTBENCH_URL is the same
variable pytest --wt-url falls back to.
Two logging methods are available. Choose based on your situation:
| Serial Monitor | UDP Logs | |
|---|---|---|
| Works without WiFi | Yes | No |
| Boot/crash output | Yes | No |
| Pattern matching | Built-in (regex + timeout) | Manual (poll + grep) |
| Blocks serial port | Yes (one session per slot) | No |
| Multiple devices | One slot at a time | All devices simultaneously |
| Long-running | Limited by timeout | Continuous (buffer persists) |
Endpoints
Request and response shapes: serial in FSD Appendix D.2, UDP log in D.7, the portal's own activity log in D.14.
Three different logs, and picking the wrong one wastes a test run: /api/serial/*
is what the device printed over USB, /api/udplog is what it sent over the
network, and /api/log is what the portal did — never device output.
Serial Monitor
Reads serial output via RFC2217 proxy. Optionally waits for a regex pattern.
# Wait up to 10s for a pattern match
curl -X POST $TESTBENCH_URL/api/serial/monitor \
-H 'Content-Type: application/json' \
-d '{"slot": "SLOT1", "pattern": "WiFi connected", "timeout": 10}'
# Just capture output for 5s (no pattern)
curl -X POST $TESTBENCH_URL/api/serial/monitor \
-H 'Content-Type: application/json' \
-d '{"slot": "SLOT1", "timeout": 5}'
What ships with it
1 file 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.
- 10d ago First seen · 234 lines · 122 tokens per session scan B 1664b671a22a
testbench-logging is a skill published in the GitHub repository SensorsIot/Embedded-AI-Harness (173 stars, last pushed 29d ago), licensed MIT. It adds 122 tokens to every session and 2,746 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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