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 addxai/ReflectWorld --skill reflectworld-vision-controllergit clone --depth 1 https://github.com/addxai/ReflectWorldWrote 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/addxai/reflectworld/reflectworld-vision-controller)<a href="https://agentmods.dev/skills/addxai/reflectworld/reflectworld-vision-controller"><img src="https://agentmods.dev/badge/skills/addxai/reflectworld/reflectworld-vision-controller/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/addxai/reflectworld/reflectworld-vision-controller"><img src="https://agentmods.dev/badge/skills/addxai/reflectworld/reflectworld-vision-controller.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.01207 |
| Opus 5.5 | $0.00000 | $0.00483 |
| Sonnet 5.5 | $0.00000 | $0.00241 |
| Haiku 4.5 | $0.00000 | $0.00121 |
Grade A, and why
reflectworld-vision-controller 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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VisionController — Adaptive Perception Strategy
VisionController dynamically adjusts camera sampling and analysis parameters based on perception results. Two policies:
- Frame Policy: Deterministic rules evaluated in milliseconds after frame detection. Adjusts camera sampling.
- Segment Policy: Fire-and-forget notification via Notifier when segment_policy conditions match. Agent decides what to do.
Configuration
perception.configure({
vision_controller: {
enabled: true,
baseline: { fps: 1 },
auto_rules: [
{
name: "person_present",
trigger: { type: "class_detected", class: "person" },
action: { fps: 2 },
cooldown_ms: 5000,
hold_ms: 30000,
},
],
segment_policy: {
consultation_triggers: [{ type: "security_risk" }],
consultation_cooldown_seconds: 60,
},
},
});
Action Parameters
| Parameter | Type | Effect |
|---|---|---|
fps |
number | Cam sampling rate (frames per second) |
mode |
"event" | "stream" | Cam capture mode |
max_level |
0 | 1 | 2 | Perception level cap |
vlm_model |
string | VLM model override (e.g. "gemini-2.5-flash-lite") |
Rule Matching
- Rules ordered by priority (index 0 = highest)
- All rules scanned; action fields merge with first-write-wins semantics
- Each rule must explicitly set all intended fields
- Unset fields = "don't care, keep current"
- No rules matched → baseline applied entirely
Trigger Conditions
| Type | Params | Description |
|---|---|---|
class_detected |
class: string | Class present in current segment |
class_absent |
class: string | Class NOT present |
class_disappeared |
class: string | Was present last segment, gone now (edge trigger) |
class_count_gte |
class, count | Count of class ≥ threshold |
face_detected |
— | Any face detected |
motion_sustained_ms |
duration | Continuous motion for N ms |
no_motion_ms |
duration | No motion for N ms |
voice_detected |
— | Speech detected (VAD/STT) |
unknown_entity |
— | New entity (first time seen) |
known_entity |
entity_id | Specific known entity present |
security_risk |
— | VLM flagged security risk |
static_scene_count |
count | N consecutive static scenes |
custom_field |
key, value | VLM custom_fields match |
all_of |
conditions[] | AND composite |
any_of |
conditions[] | OR composite |
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 · 137 lines · 0 tokens per session scan A 05737dcdf94e
reflectworld-vision-controller is a skill published in the GitHub repository addxai/ReflectWorld (103 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,207 tokens. 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 2026-08-30.
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