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 autonomous-ai/autonomous-os --skill sensing-trackgit clone --depth 1 https://github.com/autonomous-ai/autonomous-osWrote 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/autonomous-ai/autonomous-os/sensing-track)<a href="https://agentmods.dev/skills/autonomous-ai/autonomous-os/sensing-track"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-os/sensing-track.svg" alt="Measured on agentmods" 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 Data Exfiltration · line 192 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 | $0.00048 | $0.03402 |
| Opus 5 | $0.00024 | $0.01701 |
| Sonnet 5 | $0.00010 | $0.00680 |
| Haiku 4.5 | $0.00005 | $0.00340 |
Grade A, and why
sensing-track scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "http://127.0.0.1:5000/api/openclaw/mood-history?date=$(date +%Y-%m-%d)&last=100" How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sensing Event History
Quick Start
The primary data source is the flow events JSONL at /root/local/flow_events_YYYY-MM-DD.jsonl. Each file covers one calendar day (7-day retention, no size-rotation mid-day). Use Bash + jq to query it.
Important: Always use the absolute path
/root/local/— thereadtool cannot access files outside the workspace, so useexec(Bash) for all JSONL queries.
Persistent camera snapshots are stored under /var/lib/hal/snapshots/sensing_<prefix>/<ms>.jpg (72h TTL, 50 MB cap) — one subdir per event kind:
| Event type | Folder |
|---|---|
presence.enter, presence.leave |
sensing_face/ |
motion.activity |
sensing_motion_activity/ |
emotion.detected |
sensing_emotion/ |
Reference these when the user asks what happened visually.
JSONL format
Each line is a JSON object:
{"kind":"enter","node":"sensing_input","ts":1712345678.123,"seq":42,"trace_id":"run-abc","data":{"type":"presence.enter","message":"Person detected — 1 face(s) visible (friend (gray))\n[snapshot: /var/lib/hal/snapshots/sensing_face/1712345678123.jpg]"},"version":"1.2.3"}
{"kind":"exit","node":"sensing_input","ts":1712345678.456,"seq":43,"trace_id":"run-abc","duration_ms":332,"data":{"path":"agent","run_id":"run-abc"},"version":"1.2.3"}
Key fields:
node— filter on"sensing_input"for sensing eventskind—"enter"= event received,"exit"= event processed (withduration_ms)data.type— event type:presence.enter,presence.leave,motion,motion.activity,sound,light.level,voice,voice_command,emotion.detected,speech_emotion.detecteddata.message— natural-language description; may contain[snapshot: /var/lib/hal/snapshots/sensing_<prefix>/<ms>.jpg]data.path— inexitrecords:"agent"(forwarded),"local"(handled locally), or has"error"key (failed/dropped)ts— Unix timestamp (seconds with fractional ms)trace_id— correlates enter/exit and links to agent turn
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.
- 8d ago First seen · 246 lines · 48 tokens per session scan A 69584638f6f8
sensing-track is a skill published in the GitHub repository autonomous-ai/autonomous-os (279 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 3,402 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (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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