bambu-studio-ai
Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...
Reliable agent-to-agent communication protocol for transferring large payloads via shared files with pointer references and secret code verification. Triggered by explicit 'AgentRelay:' prefix in messages.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install extraterrest/agentrelayExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Reliable Agent-to-Agent Communication Protocol — Solves sessions_send timeout issues for large payloads using shared files + short message pointers.
When you see messages like the following, immediately use this Skill to handle:
Please use AgentRelay skill to handle this message:
REQ,r1_r,s/r1_r.json,,
Or
📨 AgentRelay Protocol Message
Please use AgentRelay skill to handle:
- Message Type: REQ
- Event ID: r1_r
- File Pointer: s/r1_r.json
Key Trigger Phrases:
When you see these keywords, immediately execute the Commands below!
Step 1: Parse Message
python3 {baseDir}/run_relay.py receive "REQ,r1_r,s/r1_r.json,,"
Example output:
{
"type": "REQ",
"event_id": "r1_r",
"content": {"task": "write_poem", "sender_color": "red"},
"secret": "ABC123"
}
Step 2: Understand Task and Execute
Check the content field to understand what needs to be done (e.g., write poem, analyze data, generate report).
Step 3: Update Result
python3 {baseDir}/run_relay.py complete r1_r "Task completed" "agent:red:red"
Step 4: Send CMP Confirmation
# generate CMP message (done automatically by run_relay.py complete)
# Output: CMP,r1_r,,,ABC123
# Then send via sessions_send
sessions_send(target='agent:red:red', message='CMP,r1_r,,,ABC123')
receive <csv_message>Parse AgentRelay CSV message and read shared file content.
Parameters:
csv_message: CSV format message (without AgentRelay: prefix)Example:
python3 {baseDir}/run_relay.py receive "REQ,r1_r,s/r1_r.json,,"
Output (JSON):
{
"type": "REQ",
"event_id": "r1_r",
"ptr": "s/r1_r.json",
"content": {...},
"secret": "ABC123"
}
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
Reliable Agent-to-Agent Communication Protocol — Solves sessions_send timeout issues for large payloads using shared files + short message pointers.
When your agents need to send messages larger than 30 characters, sessions_send tends to timeout. AgentRelay provides the solution:
| Traditional Approach | AgentRelay Approach |
|---|---|
| ❌ Send large text directly → ⏰ Timeout | ✅ Write to file + send short pointer → Success |
| ❌ No verification if received | ✅ Secret Code mechanism ensures delivery |
| ❌ No audit trail | ✅ Complete transaction logs (4 entries/event) |
clawhub install agentrelay
from agentrelay import AgentRelayTool
# Prepare data
content = {"task": "write_poem", "theme": "spring"}
# Write to shared file and get CSV message
result = AgentRelayTool.send("yellow", "REQ", "hop1", content)
# Send to target agent
sessions_send(
target='agent:yellow:yellow',
message=f"AgentRelay: {result['csv_message']}"
)
# Use unified script to parse
python3 scripts/run_relay.py receive "REQ,hop1,s/hop1.json,,"
Output:
{
"type": "REQ",
"event_id": "hop1",
"content": {"task": "write_poem", ...},
"secret": "ABC123"
}
python3 scripts/run_relay.py complete hop1 "Task completed" "agent:red:red"
Output:
✅ Updated hop1
✅ CMP: CMP,hop1,,,ABC123
Sender Receiver
| |
|-- 1. REQ (with file ptr) --->|
| |-- receive()
| |-- 📍 LOG #1: REQ/RECEIVED
| |-- 📍 LOG #2: ACK/ACKNOWLEDGED
| |
|<-- 2. ACK (implicit confirm)-|
| |
|...
# AgentRelay 📨
**Reliable Agent-to-Agent Communication Protocol** — Solves `sessions_send` timeout issues for large payloads using shared files + short message pointers.
## 🎯 When to Use This Skill
**When you see messages like the following**, immediately use this Skill to handle:
```
Please use AgentRelay skill to handle this message:
REQ,r1_r,s/r1_r.json,,
```
Or
```
📨 AgentRelay Protocol Message
Please use AgentRelay skill to handle:
- Message Type: REQ
- Event ID: r1_r
- File Pointer: s/r1_r.json
```
**Key Trigger Phrases**:
- "use AgentRelay skill to handle"
- "AgentRelay protocol message"
- "Use AgentRelay"
When you see these keywords, **immediately execute the Commands below**!
## 🚀 Quick Start
### Standard Flow After Receiving AgentRelay Message
**Step 1: Parse Message**
```bash
python3 {baseDir}/run_relay.py receive "REQ,r1_r,s/r1_r.json,,"
```
Example output:
```json
{
"type": "REQ",
"event_id": "r1_r",
"content": {"task": "write_poem", "sender_color": "red"},
"secret": "ABC123"
}
```
**Step 2: Understand Task and Execute**
Check the `content` field to understand what needs to be done (e.g., write poem, analyze data, generate report).
**Step 3: Update Result**
```bash
python3 {baseDir}/run_relay.py complete r1_r "Task completed" "agent:red:red"
```
**Step 4: Send CMP Confirmation**
```bash
# generate CMP message (done automatically by run_relay.py complete)
# Output: CMP,r1_r,,,ABC123
# Then send via sessions_send
sessions_send(target='agent:red:red', message='CMP,r1_r,,,ABC123')
```
---
## 📚 Commands
### `receive <csv_message>`
Parse AgentRelay CSV message and read shared file content.
**Parameters**:
- `csv_message`: CSV format message (without `AgentRelay:` prefix)
**Example**:
```bash
python3 {baseDir}/run_relay.py receive "REQ,r1_r,s/r1_r.json,,"
```
**Output** (JSON):
```json
{
"type": "REQ",
"event_id": "r1_r",
"ptr": "s/r1_r.json",
"content": {...},
"secret": "ABC123"
}
```
---
### `update <event_i...# AgentRelay 📨
**Reliable Agent-to-Agent Communication Protocol** — Solves `sessions_send` timeout issues for large payloads using shared files + short message pointers.
---
## 🎯 Core Value
When your agents need to send messages **larger than 30 characters**, `sessions_send` tends to timeout. AgentRelay provides the solution:
| Traditional Approach | AgentRelay Approach |
|---------------------|---------------------|
| ❌ Send large text directly → ⏰ Timeout | ✅ Write to file + send short pointer → Success |
| ❌ No verification if received | ✅ Secret Code mechanism ensures delivery |
| ❌ No audit trail | ✅ Complete transaction logs (4 entries/event) |
---
## 🚀 Quick Start
### 1️⃣ Install
```bash
clawhub install agentrelay
```
### 2️⃣ Send Message
```python
from agentrelay import AgentRelayTool
# Prepare data
content = {"task": "write_poem", "theme": "spring"}
# Write to shared file and get CSV message
result = AgentRelayTool.send("yellow", "REQ", "hop1", content)
# Send to target agent
sessions_send(
target='agent:yellow:yellow',
message=f"AgentRelay: {result['csv_message']}"
)
```
### 3️⃣ Receive Message
```bash
# Use unified script to parse
python3 scripts/run_relay.py receive "REQ,hop1,s/hop1.json,,"
```
Output:
```json
{
"type": "REQ",
"event_id": "hop1",
"content": {"task": "write_poem", ...},
"secret": "ABC123"
}
```
### 4️⃣ Complete Task and Reply
```bash
python3 scripts/run_relay.py complete hop1 "Task completed" "agent:red:red"
```
Output:
```
✅ Updated hop1
✅ CMP: CMP,hop1,,,ABC123
```
---
## 🔄 Complete Flow
```
Sender Receiver
| |
|-- 1. REQ (with file ptr) --->|
| |-- receive()
| |-- 📍 LOG #1: REQ/RECEIVED
| |-- 📍 LOG #2: ACK/ACKNOWLEDGED
| |
|<-- 2. ACK (implicit confirm)-|
| |
|...Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...
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