Skill v1.0.1

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simhacker/moollm/data-flow

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PublishedOctober 22, 2026 at 04:41 PM

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version: "1.0.1" 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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