| name | organ-yuri-decode |
| description | LLM-wielded decoder instrument: translate text into a deterministic math object (tokens, numerology channels, dimensional classification). Use when the model needs a deterministic structural reading of text to reason over — e.g. decoding a brain dump's surface features, analyzing token patterns, or extracting numerology/dimensional signatures from input. |
| triggers | ["organ-yuri-decode","how do I use yuri-decode","yuri-decode usage","yuri-decode guide","YURI Decode (LLM-wielded decoder instrument: text → math object)"] |
| generated | true |
| source_node | yuri-decode |
| source_file | _SYSTEM/Scripts/yuri-decode.mjs |
| scope | harness |
| invocation | ability |
Organ Guide — YURI Decode (LLM-wielded decoder instrument: text → math object)
Module: _SYSTEM/Scripts/yuri-decode.mjs · Layer: Cognition & Persona · Invocation: both · CLI: "<text>" [--json]
Purpose. The LLM-WIELDED decoder instrument: translate text → a deterministic math object. NOT an ingress pre-processor that mutates input before the LLM — it is an INSTRUMENT the LLM calls directly: decode('') returns a structured math representation (tokens, numerology channels, dimensional reading, feature surface) the LLM then reasons over. The control inverts — the model wields the decode engine.
Exports
decode(text, opts = {})
- in: a text string + optional opts
- out: { tokens, numerology (gematria hash / digital-root mod-9 / harmonic signature), dimension, features, advisory_only } — a deterministic math object for the same text
Security boundary
Pure / read-only and advisory_only — computes a math object from text; persists nothing, touches no protected paths. Deterministic + embedding-free: same text → same object. Reuses the shipped channels (yuri-jaccard tokenize, nexus-numerology, the Foundry dimension classifier).
When to use
When the model wants a deterministic structural reading of a piece of text (token surface, numerology channels, dimensional classification) to reason over — e.g. decoding a brain dump's surface features into a stable object.
Gotchas
- It is an INSTRUMENT the LLM calls, NOT an ingress mutator — it never rewrites the input before the model sees it.
- Deterministic + embedding-free: identical text yields an identical object (no RNG, no clock) — good for caching/equality.
- Output is advisory_only — a decoded reading is structure to reason over, not a verified claim.
Session Notes
2026-06-13
- session: 40m | peak ctx: 0% | compacts: 0
- tools: Bash×824, Read×163, Edit×17, StructuredOutput×16, Write×8, TodoWrite×4, ToolSearch×2, ScheduleWakeup×2, Workflow×1, mcp×1, AskUserQuestion×1
- corrections: none
- errors: none
