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-memgit 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-mem)<a href="https://agentmods.dev/skills/addxai/reflectworld/reflectworld-mem"><img src="https://agentmods.dev/badge/skills/addxai/reflectworld/reflectworld-mem.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 Memory Poisoning · line 12 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.01332 |
| Opus 5 | $0.00000 | $0.00666 |
| Sonnet 5 | $0.00000 | $0.00266 |
| Haiku 4.5 | $0.00000 | $0.00133 |
Grade A, and why
reflectworld-mem 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 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.
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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
reflectworld_memory
Search and manage ReflectWorld video memories from ReflectWorld cameras.
When to use
Use reflectworld_memory when the user asks about:
- What happened in front of a camera ("who came to the door today?")
- Identifying recurring patterns ("does this person visit often?")
- Retrieving event history for a camera or entity
- Adding structured notes about observations
- Setting persistent rules/preferences that the VLM should follow
Actions
search
Semantic search across video memories. Searches episodic (raw events), semantic (behavioral patterns), and procedural (agent rules) collections.
{
"action": "search",
"query": "person carrying package at front door",
"camera_id": "cam-front-door",
"mode": "event",
"memory_type": "episodic",
"limit": 10,
"threshold": 0.7
}
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| query | string | yes | Natural language search query |
| camera_id | string | no | Filter to specific camera |
| mode | "snapshot" | "event" | "stream" | no | Filter by capture mode |
| memory_type | "episodic" | "semantic" | "procedural" | "all" | no | Collection to search (default: "all") |
| limit | integer | no | Max results (default: 20, max: 200) |
| threshold | number 0-1 | no | Minimum similarity score |
add_note
Store a manual observation or user-defined rule. Use memory_type="procedural" for notification rules/preferences — these are automatically injected into the VLM prompt on every analysis cycle.
{
"action": "add_note",
"note": "Alert user when animals or pets appear in the camera feed.",
"camera_id": "global",
"memory_type": "procedural"
}
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| note | string | yes | Note content to store |
| camera_id | string | no | Associated camera. Use "global" for rules applying to all cameras |
| memory_type | "episodic" | "procedural" | no | Collection to store in. Use "procedural" for user rules/preferences (default: episodic) |
| user_id | string | no | Associated user |
| agent_id | string | no | Agent making the note |
| mode | "snapshot" | "event" | "stream" | no | Context mode |
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 · 143 lines · 0 tokens per session scan A ec672e21e155
reflectworld-mem is a skill published in the GitHub repository addxai/ReflectWorld (98 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,332 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.
Other skills, from other repositories
ama-memory
Use AMA memory in OpenClaw to recall prior context, capture turns, inspect stored state, end sessions, or delete a user's memory when asked.
memx
Use when an agent needs to recall, save, inspect, or audit memX memories through the bundled MCP tools.
tencentdb-agent-memory
A long-term memory system for agent teams that stores reusable information from conversations, documents, and code.
ama-memory
Use AMA memory in OpenClaw to recall prior context, capture turns, inspect stored state, end sessions, or delete a user's memory when asked.
mem0-dream
Consolidates stored memories by merging duplicates, resolving contradictions, and pruning stale entries. Use when memory count is high, search results feel noisy or repetitive, or periodic cleanup is needed to maintain memory quality.
mem0-tour
Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.