MindScript: The Language Layer for MindsEye (Ledger-First Automation, Domain Dialects, and Searchable Memory)' published: true

Source: Dev.to

MindsEye is the automation engine.

MindScript is the language layer that gives it a usable “voice.”
If MindsEye is a nervous system, MindScript is the syntax of intent — a structured way to describe what should happen, with guardrails, provenance, and domain‑specific vocabularies.

This post introduces MindScript as:

• a programmable prompt language (yes, prompt language — but with structure)
• a ledger‑first execution model (traceability is default, not optional)
• a multi‑domain dialect system (every automation can have its own “company‑native language”)
• a searchable memory layer (semantic recall across templates, ledgers, runtime outputs)

Repo Index (Core + Demos)

MindScript layer

• mindscript-core
• mindscript-templates
• mindscript-ledger
• mindscript-runtime
• mindscript-search
• mindscript-demos

MindsEye layer (selected)

• mindseye-workspace-automation
• mindseye-google-ledger
• mindseye-google-workflows
• minds-eye-core
• minds-eye-search-engine
• minds-eye-dashboard
• minds-eye-automations

1) What MindScript introduces (and why it matters)

Most automation stacks are either:

• GUI‑first (Zapier‑ish) and hard to version‑control, or
• code‑first (custom scripts) and hard to operate as a living system.

MindScript sits in the middle:

• ✅ declarative, human‑writable
• ✅ structured enough to compile
• ✅ safe enough to audit
• ✅ flexible enough to evolve

The core idea

Automations share the same runtime + ledger, but each automation has its own language surface.

Thus you can have finance, recruiting, compliance, and customer‑support dialects—all running on the same kernel.

2) MindsEye + MindScript architecture (ledger‑first, dialect‑driven)

Mental model

• MindScript → compiles into stages
• Stages → executed by domain automations
• Every stage → writes a ledger entry
• Ledger → searchable memory (semantic + structural search)

Diagram: end‑to‑end flow

flowchart TD
  A[Human Intent] --> B[MindScript Text]
  B --> C[Compiler / Parser]
  C --> D[Stages: typed + constrained]
  D --> E[Runtime Executor]
  E --> F[Ledger Append]
  F --> G[Search Index]
  G --> H[Recall + Reuse + Replay]

The important part

The ledger is the truth.
It records what was actually written—timestamped, tagged, and reproducible—rather than what an agent claims happened.

3) A minimal MindScript example

ledger(debit:AR, amount:1200 USD)

search{domain:docs, query:"ledger best practices", top_k:5}

task build-report uses analytics after search

metric coverage = agg(count(unique(accounts)))

This single script mixes:

• finance booking
• semantic search
• orchestration
• analytics

…without turning into spaghetti, because dialects prevent chaos.

4) The code structure behind it (what we’re actually building)

Pattern:
Dialect → Automation → Shared Runtime → Ledger Entry → Searchable Memory

Architecture sketch (Python)

# docs/mindseye_architecture.py (concept sketch)

# Immutable LedgerEntry
# Dialects validate + parse
# Automations execute stages
# Runtime remains domain‑agnostic

# NOTE: this is an architectural reference. Implementation lives across the repos.

Diagram: kernel vs. domain

flowchart LR
  subgraph Kernel[MindsEye Kernel]
    R[Runtime Executor]
    L[Ledger]
    S[Search Index]
  end

  subgraph Domains[Domain Automations]
    F[Finance Dialect + Automation]
    O[Orchestration Dialect + Automation]
    A[Analytics Dialect + Automation]
    Q[Search Dialect + Automation]
  end

  F --> R
  O --> R
  A --> R
  Q --> R
  R --> L --> S
  S --> R

The kernel is stable; domains can evolve fast. That’s the whole point.

5) Codex prompts (to generate the system in expansions)

Prompt 1 — Build the MindScript compiler + stage model

Goal: parse MindScript into typed stages, with ambiguity detection.
Generate a Python package mindscript_core that implements:

• Stage, Program, LedgerEntry (immutable payload + tags)
• compile_mindscript(source: str, registry) that parses line‑by‑line, matches each line to exactly one automation dialect, raises on ambiguity, and stores a program.automations list

Include unit tests for:

• finance lines match finance only
• search lines match search only
• ambiguous lines raise

Provide a clean folder structure, pyproject.toml, and tests.

Prompt 2 — Build the plugin registry + dialect versioning system

Goal: automation plugins register dialects; dialect lineage is preserved.
Generate Python modules that implement:

• DialectRegistry supporting multiple versions per dialect name, an active version pointer, and an evolve() method that registers new versions without deleting old ones
• AutomationRegistry that registers AutomationPlugins and ensures dialect discoverability

Add a dialect_lineage_example() demonstrating a dialect evolving from v1 → v2 while runtime can still validate old programs. Include type hints and tests.

Prompt 3 — Build the ledger‑first runtime + replay engine

Goal: everything writes to the ledger; you can replay deterministically.
Generate a runtime engine package mindscript_runtime that includes:

• ExecutionContext with a ledger list, shared memory dict, and recursion‑limit safety
• run(program) that validates via dialect, executes via automation, and records ledger entries
• replay(ledger_entries) that reconstructs state, checking ledger ordering and integrity

Include a demo that runs a program, then replays it.

Prompt 4 — Build mindscript-search indexing for “temporal recall”

Goal: index templates, ledgers, runtime outputs; retrieve fast.
Generate a service named mindscript_search with:

• a CLI and minimal API
• indexing of source templates, ledger entries, and runtime outputs
• support for semantic and temporal queries

(Implementation details omitted for brevity.)