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data-flow

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

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SimHacker/moollm
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7. Februar 2026 um 14:40
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Englisch
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5

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
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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