smithery/simhacker

data-flow

Rooms as pipeline nodes, exits as edges, objects as messages

Installation

$ npx skills add smithery/simhacker --skill data-flow

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Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsread_file, write_file, run_terminal_cmd

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,200 B
  • docs SUMMARY.md 77 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

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)

processor:
  type: script
  command: "python parse.py ${input}"

LLM (Semantic)

processor:
  type: llm
  prompt: |
    Analyze this document:
    - Extract key entities
    - Summarize in 3 sentences

Hybrid

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)

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)

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