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Stenography

Stenography is an agent communication and context-compression system designed to minimize token usage while maintaining human readability and strict technical accuracy.

Design Principles

  1. A/R/N Pattern: Replaces verbose natural language with structured blocks:
    • A: Answer (direct, no fluff)
    • R: Reason (logical derivation/facts)
    • N: Next step (actionable directions)
  2. Symbolic Logic Mapping: Replaces prose conjunctions/logic paths with logic operators ( for causality, for checks, != for inequality).
  3. Namespace Scoping: Translates narrative statements to key-value maps (sys.env: dev instead of "The system environment is configured for development").
  4. Standard Abbreviations: Uses world-standard short names (fn, db, config, pkg, repo, dev) that LLMs parse natively without loss of meaning.
  5. Lazy Resolution: Resolves references via file links rather than copy-pasting raw content.

Token & Speed Efficiency Comparison

Metric / Style Verbose (Default) Caveman Stenography (Symbolic)
Output Token Size 100% (baseline) ~25% (75% saved) ~30% (70% saved)
Input Context Size 100% (baseline) ~35% (65% saved) ~35% (65% saved)
Human Readability High Extremely low (broken prose) High (logical structure)
Technical Accuracy High Moderate (risks ambiguity) High (retains logical relationships)
Speed (TTFT/Generation) 1x ~3x ~3x

Skills

/compress-context

Automatically parses a target markdown file, strips grammatical fluff, maps logic to symbols, and rewrites the file in place to save active context space.


Installation & Integration

See INSTALL.md for details on setting up Stenography configuration files in Claude Code, GitHub Copilot, Cursor, and Codex / Antigravity.

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Stenography is an agent communication and context-compression system designed to minimize token usage while maintaining human readability and strict technical accuracy.

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