data-flow
Rooms as pipeline nodes, exits as edges, objects as messages
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تعليمات المصدر · معاينة للقراءة فقط- name
- data-flow
- description
- Rooms as pipeline nodes, exits as edges, objects as messages
- allowed-tools
- ["read_file","write_file","run_terminal_cmd"]
- tier
- 2
- protocol
- DATA-FLOW
- tags
- ["moollm","pipeline","processing","kilroy","messaging"]
- related
- ["room","card","sister-script","coherence-engine"]
# Data Flow
> *"Rooms are nodes. Exits are edges. Thrown objects are messages."*
MOOLLM's approach to building processing pipelines using rooms and objects. The filesystem IS the data flow network.
## The Pattern
- **Rooms** are processing stages (nodes)
- **Exits** connect stages (edges)
- **Objects** flow through as messages
- **THROW** sends objects through exits
- **INBOX** receives incoming objects
- **OUTBOX** stages outgoing objects
## Commands
| Command | Effect |
|---------|--------|
| `THROW obj exit` | Send object through exit to destination |
| `INBOX` | List items waiting to be processed |
| `NEXT` | Get next item from inbox (FIFO) |
| `PEEK` | Look at next item without removing |
| `STAGE obj exit` | Add object to outbox for later throw |
| `FLUSH` | Throw all staged objects |
| `FLUSH exit` | Throw staged objects for specific exit |
## Room Structure
```
stage/
├── ROOM.yml # Config and processor definition
├── inbox/ # Incoming queue (FIFO)
├── outbox/ # Staged for batch throwing
└── door-next/ # Exit to next stage
```
## Processor Types
### Script (Deterministic)
```yaml
processor:
type: script
command: "python parse.py ${input}"
```
### LLM (Semantic)
```yaml
processor:
type: llm
prompt: |
Analyze this document:
- Extract key entities
- Summarize in 3 sentences
```
### Hybrid
```yaml
processor:
type: hybrid
pre_process: "extract.py ${input}"
llm_prompt: "Analyze extracted data"
post_process: "format.py ${output}"
```
**Mix and match.** LLM for reasoning, scripts for transformation.
## Example Pipeline
```
uploads/ # Raw files land here
├── inbox/
│ ├── doc-001.pdf
│ └── doc-002.pdf
└── door-parser/
parser/ # Extract text
├── script: parse.py
└── door-analyzer/
analyzer/ # LLM analyzes
├── prompt: "Summarize..."
├── door-output/
└── door-errors/
output/ # Final results
└── inbox/
├── doc-001-summary.yml
└── doc-002-summary.yml
```
## Processing Loop
```
> ENTER parser
Inbox: 2 items waiting.
> NEXT
Processing doc-001.pdf...
Text extracted.
> STAGE doc-001.txt door-analyzer
Staged.
> FLUSH
Throwing 2 items through door-analyzer...
```
## Fan-Out (one-to-many)
```yaml
routing_rules:
- if: "priority == 'high'"
throw_to: door-fast-track
- if: "type == 'archive'"
throw_to: door-archive
- default: door-standard
```
## Fan-In (many-to-one)
```yaml
batch_size: 10
on_batch_complete: |
Combine all results
Generate summary report
THROW report.yml door-output
```
## Kilroy Mapping
| MOOLLM | Kilroy |
|--------|--------|
| Room | Node |
| Exit | Edge |
| THROW | Message passing |
| inbox/ | Input queue |
| Script processor | Deterministic module |
| LLM processor | LLM node |
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