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 reflectworldgit 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)<a href="https://agentmods.dev/skills/addxai/reflectworld/reflectworld"><img src="https://agentmods.dev/badge/skills/addxai/reflectworld/reflectworld/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"><img src="https://agentmods.dev/badge/skills/addxai/reflectworld/reflectworld.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 | $0.01871 |
| Opus 5.5 | $0.00000 | $0.00748 |
| Sonnet 5.5 | $0.00000 | $0.00374 |
| Haiku 4.5 | $0.00000 | $0.00187 |
Grade A, and why
reflectworld 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ReflectWorld — Visual Perception + Memory for AI Agents
You have access to ReflectWorld, a visual perception and persistent memory system. It gives you eyes and memory for cameras.
Available Tools
| Tool | Purpose |
|---|---|
reflectworld_cam |
Camera control: add sources, snapshot, watch, stop, adjust sampling, switch mode, live view, recordings |
reflectworld_perception |
VLM analysis config: set scene description, focus targets, security rules, model override |
reflectworld_memory |
Memory search & storage: episodic events, semantic patterns, procedural rules |
reflectworld_act |
Automation: notifications, identify requests, rule management |
Scene Adaptation Workflow
When a user describes what they want to monitor, follow this flow:
User describes intent
→ 1. Configure perception (scene_description + focus_targets)
→ 2. Add camera source if not already connected
→ 3. Select watch params (mode, fps, resolution)
→ 4. Store procedural rules in memory for persistent behavior
→ 5. Start watch
Step-by-step
-
Configure perception first — Call
reflectworld_perceptionwithaction=configureto setscene_descriptionandfocus_targetsso the VLM knows what matters. This takes effect immediately on the next analyzed segment. -
Add source if needed — Call
reflectworld_camwithaction=add_sourceif the camera isn't registered yet. -
Choose watch parameters based on the scene:
- Slow-changing scenes (cooking, front door):
fps=1,960x540 - Security/motion:
mode=event(motion-triggered),fps=1 - Behavior analysis (poker, games):
mode=stream,fps=0.5 - Fast action (sports, pets playing):
fps=2-5
- Slow-changing scenes (cooking, front door):
-
Store procedural rules — Call
reflectworld_memorywithaction=add_note,memory_type=proceduralfor any persistent preferences. Procedural rules are automatically injected into the VLM prompt on every analysis cycle. -
Start watch — Call
reflectworld_camwithaction=watch.
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 · 171 lines · 0 tokens per session scan A 3f6dda8ecd4f
reflectworld 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 1,871 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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