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Kodelyth ECC
Skill

ecc-token-savings

Maximize token savings across an ECC-equipped session by combining the three shipped layers — RTK (input compression on shell output), Terse mode (output compression on AI replies), and the codebase graph (structural queries instead of file-by-file grep). Use when the user asks how to cut token cost, why a session is expensive, or how to use RTK/Terse/codebase-graph together.

Invoke via:use ecc-token-savings
Origin:ECC

ECC Token Savings Stack

Kodelyth ECC ships three independent token-savings layers. They stack. Used together on a typical coding session they cut 55-65% of total token cost — more on explain-heavy or exploration-heavy work. This skill is the unified playbook; the per-feature docs live in docs/rtk.md, docs/terse-mode.md, docs/codebase-graph.md.

The three layers (know which axis each cuts)

| Layer | Cuts | How | Typical saving | |---|---|---|---| | RTK | Input tokens | Filters shell-command output before the LLM sees it | 60-90% on git/ls/test/docker/... | | Terse mode | Output tokens | Compresses what the AI writes, code stays byte-exact | 40-70% on replies | | Codebase graph | Input tokens | One structural query replaces dozens of grep/read cycles | ~99% on "who calls X" questions |

They are orthogonal — RTK and codebase-graph shrink what goes *in*, Terse shrinks what comes *out*. Turning on all three compounds.

Verify what's active

kodelythecc rtk status        # RTK binary + wired IDEs + live ledger
kodelythecc terse status      # skill installed? current ledger totals
kodelythecc codebase status   # binary version + indexed projects

If any is missing, install it:

kodelythecc rtk enable --all              # wire RTK into every ECC-installed IDE
kodelythecc terse enable --all            # install /terse + /terse-compress
kodelythecc codebase install              # install codebase-memory-mcp + register

The workflow — what to actually do

1. Let RTK ride silently (already on after install)

RTK is a PreToolUse hook. Once wired, every shell command the AI runs is auto-filtered. Nothing to do per-session. If you want the compact output in Read/Grep too, prefer shell (rg, cat, find) or explicit rtk read/rtk grep — the Bash hook doesn't cover the built-in file tools.

2. Turn on Terse mode for the session

/terse full         # telegram-style fragments, ~50% output cut (default)
/terse ultra        # maximum, ~70% cut — for expert users on familiar work
/terse lite         # light trim, ~25% — when you still want readable prose
/terse off          # restore normal voice (docs writing, teaching, onboarding)

Rule of thumb: full for day-to-day coding, off when the *output itself is the deliverable* (documentation, explanations for others, teaching).

3. Query the graph instead of grepping

Once a project is indexed ("Index this project" in your AI tool), replace exploration greps with structural queries:

kodelythecc codebase query search_graph '{"name_pattern": ".*Handler.*"}'
kodelythecc codebase query trace_path   '{"function_name": "processOrder"}'
kodelythecc codebase query get_architecture '{}'

"Who calls X", "what's the impact of changing Y", "show the architecture" — all one query, ~3k tokens, versus ~400k tokens of file-by-file reading.

4. Compress persistent memory files once, save forever

kodelythecc terse compress CLAUDE.md          # ~30% smaller, code/URLs/paths byte-exact
kodelythecc terse compress tasks/lessons.md

Every session that loads these files now costs ~30% fewer input tokens — permanently, not per-turn.

Measure it — don't guess

kodelythecc dashboard        # Token Savings tab: RTK (input) + Terse (output), live ledgers
kodelythecc rtk gain --all   # raw RTK savings numbers
kodelythecc terse stats      # output tokens saved, by level

The dashboard shows real ledger data, never estimates. Point the user there when they ask "how much am I actually saving."

Honest caveats — say these, don't oversell

  • Terse adds ~800-1200 input tokens per turn (the skill prompt). On turns under ~2k output tokens it can be net-negative — skip Terse for short back-and-forth.
  • RTK only covers the Bash tool. Read/Grep/Glob bypass it. Use shell equivalents to get RTK compression there.
  • The codebase graph must be indexed first and re-indexed after big changes ("Index this project" or codebase-memory-mcp auto-watch).
  • Native Windows RTK install is manual (.zip from releases); WSL and macOS/Linux auto-install.

Quick recommendation by session type

| Session type | RTK | Terse | Graph | |---|:---:|:---:|:---:| | Day-to-day coding | on | full | query on explore | | Deep debugging | on | lite (keep reasoning readable) | trace_path heavily | | Codebase exploration / onboarding | on | off | primary tool | | Writing docs / teaching | on | off | as needed | | Expert on familiar code | on | ultra | query on explore |

See also

  • docs/rtk.md, docs/terse-mode.md, docs/codebase-graph.md — per-feature deep dives
  • Skills: [[terse-mode]], [[token-budget-advisor]], [[cost-aware-model-routing]]
  • The cost-aware-model-routing rule picks the model *tier*; this skill cuts the *token count* at whatever tier you're on.

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