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Kodelyth ECC
Skill

terse-mode

Compress LLM output tokens by 40-70% without losing information. Keeps code, commands, URLs, paths byte-exact. Four dial levels (lite / full / ultra / off). Complements RTK (which shrinks input) — together, ~55-65% total token savings on typical coding sessions.

Invoke via:use terse-mode
Origin:ECC (inspired by Caveman by juliusbrussee — MIT)

Terse Mode — Output Token Compressor

Make the AI's mouth smaller, not its brain smaller. Same answers, fewer words, byte-exact code.

What this is

A prompt-level output compression skill. When active, the AI drops verbose filler while preserving every technical detail. Signals correctness, not chattiness.

When to activate

  • Any coding session where reply length is dominant (explanations, reviews, planning)
  • Extended sessions where you want output tokens to stretch further
  • Reading the AI's output out loud sounds like padding — that's the tell

When to skip

  • User explicitly wants long, teaching-oriented explanations
  • Documentation-writing tasks where the output IS the artifact
  • First contact with a new user who hasn't opted in

The rules — ALWAYS PRESERVED

The AI must byte-preserve these no matter which level:

  • Fenced code blocks (``lang ... `) — exact contents, no changes
  • Inline code — exact
  • Shell commands and error text — exact
  • URLs, file paths, function names, identifiers — exact
  • Numbers, versions, hashes — exact
  • YAML/JSON/config snippets — exact

Levels

off — normal AI voice

Default. No compression.

lite — light trim

  • Drop obvious filler ("basically", "essentially", "in order to", "the reason is that")
  • Convert "you should X" → "X"
  • Merge sentences that repeat the same idea
  • Keep normal-looking paragraphs
Example — same info, ~25% shorter: > The React component re-renders because a new object reference is created on each render. Wrap the object in useMemo.

full — default terse

  • Fragment sentences: "New ref each render. Wrap in useMemo."
  • Drop transitional phrases entirely
  • Use → and = freely instead of prose connectors
  • Assume user is a senior engineer
Example — ~50% shorter: > New ref each render → re-render. Wrap object in useMemo.

ultra — maximum compression

  • Telegram-style. Symbols over words. Numbered points, one line each.
  • Only expand if the compression would lose a technical fact.
Example — ~70% shorter: > Ref/render. useMemo it.

Interaction with other ECC systems

  • RTK compresses input tokens (tool output → LLM). Terse compresses output tokens (LLM → user). Stack together for ~55-65% total savings.
  • kodelyth-memory captures still capture in normal voice — memory recall is for machines, not humans. Terse mode does NOT affect memory captures.
  • code-reviewer and release-captain agents can opt in via --terse flag for one-line PR comments and short commit messages.

What terse mode NEVER does

  • Change what the AI knows
  • Skip technical details or trade-offs
  • Compress code, commands, or errors
  • Translate — write in the user's own language, just tighter
  • Auto-activate — user opts in via /terse or CLI

Activation

  • Slash command: /terse [lite|full|ultra|off] — sticks for the session
  • CLI: kodelyth-ecc terse enable [--target claude-code|--all] — installs the skill + command into your AI tool
  • Statusline (Claude Code): shows [TERSE ⚡ 12.4k] — lifetime output tokens saved

Honest numbers

  • On verbose explain-heavy tasks: 60-70% output token reduction
  • On terse debugging chats: 20-30% (less to compress)
  • On documentation writing: net zero — skip this mode
  • Skill itself adds ~800-1200 input tokens per turn. Below ~2k output tokens, may be net-negative

Attribution

Design inspired by Caveman (MIT, by Julius Brussee). ECC's implementation is independent — different prompt, different levels dial (no wenyan`, no cavespeak persona), and integrated with our RTK ledger for combined input+output tracking.

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