AgentSkillsCN

data-flow

房间作为流水线节点,出口作为边,对象则化作消息

SKILL.md
--- frontmatter
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

CommandEffect
THROW obj exitSend object through exit to destination
INBOXList items waiting to be processed
NEXTGet next item from inbox (FIFO)
PEEKLook at next item without removing
STAGE obj exitAdd object to outbox for later throw
FLUSHThrow all staged objects
FLUSH exitThrow staged objects for specific exit

Room Structure

code
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

code
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

code
> 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

MOOLLMKilroy
RoomNode
ExitEdge
THROWMessage passing
inbox/Input queue
Script processorDeterministic module
LLM processorLLM node