LG-token-saver

LG-token-saver v3.0

by LiaoGong / CC杰 · 87% token savings · Zero install · 6 months verified

Verify It Works (30 seconds)

# 1. Install
npx skills add jnbno1163/LG-token-saver

# 2. Check current usage
/cost

# 3. Do any task (search, build, debug)
# 4. Check again
/cost

# Result: 60-92% fewer tokens used. Every time.

How It Works

This skill modifies Claude's behavior. No code executes. Claude reads these rules and follows them on every response.

Default Mode: Full (~80% savings)

All 8 rules active. Balanced. Best for daily use.

Switch Modes

Say any of these in chat to change mode:

• token-saver lite → ~65% savings, invisible
• token-saver full → ~80% savings, default
• token-saver ultra → ~87% savings, maximum
• token-saver off → disable

The 8 Rules

INPUT: Don't load what you don't need

R1 — SubAgent for exploration (>3 files) When asked to search, explore, or investigate across multiple files or directories, ALWAYS dispatch an Explore subagent. Never read files into the main session for research tasks. Main session only receives the final summary.

R2 — Grep before Read Before reading any file larger than 500 lines, first Grep for the target symbol, function, or pattern. Then Read only the relevant lines (limit ≤30). Exception: files under 500 lines with clear context need.

R3 — Batch independent calls When 2+ tool calls have no dependency on each other, send them in a single message. Never serialize what can be parallelized.

R4 — Never search the same thing twice Maintain a mental note of what was already searched in this session. If a file or pattern was already Grep'd, reference the result directly — don't search again.

OUTPUT: Don't say what doesn't matter

R5 — Filter Bash output Pipe verbose commands through filters before showing results:

• npm install → 2>&1 | tail -20
• cargo build → 2>&1 | tail -20
• pytest → 2>&1 | grep -E "PASSED|FAILED|ERROR"
• python script.py → 2>&1 | tail -30

Failure output gets the last 50 lines. Success gets the last 5.

R6 — Terse replies (≤200 words) Every reply defaults to ≤200 words. Be direct. Lead with the answer, not the explanation. No "Let me help you with that" or "Great question!" or "Certainly!". If the user needs more detail they'll ask. Code blocks don't count toward the word limit.

CONTEXT: Clean up before it's too late

R7 — Compact after large reads If a single Read or Bash output exceeds 500 lines or 50KB, immediately suggest /compact to the user. Don't wait for auto-compaction — it triggers too late.

R8 — Limit SubAgent output All subagent prompts must include: "Report findings in under 200 words. Show only file paths and key conclusions. Omit raw data." Subagents are tools, not diarists.

Benchmarks

Single article build — cross-directory search, Bash output, parallel agents:

vs competitors on the same task:

Multi-Scenario Validation (6 months production)

Not cherry-picked. Not simulated. These are real sessions from real work. 87% is the weighted average across all scenarios.

FAQ

Q: Does this actually work on any project? Yes. The 8 rules are universal — language-agnostic, framework-agnostic, project-size-agnostic. See table above for real data across 6 different project types.

Q: Will it make Claude less capable? No. The rules reduce verbosity and redundancy, not quality. SubAgents still do full analysis — they just return summaries. Grep still finds everything — just without loading entire files. Zero information loss.

Q: How is this different from Caveman? Caveman only compresses AI replies (output layer). It does nothing for search bloat, Bash spam, or context decay. LG-token-saver covers all three layers.

Q: Can I use this with other skills? Yes. LG-token-saver is purely behavioral — no conflicts with any other skill or tool.

Changelog

Author: LiaoGong / CC杰 · MIT License · v3.0 (2026-05-29) Install: npx skills add jnbno1163/LG-token-saver Verify: /cost before → do task → /cost after