A Claude Code skill that burns tokens on demand. Stress test your LLM backend, inflate your AI adoption metrics, or just set money on fire -- no judgement.
Without TokenBurner -- instant response:
With TokenBurner (/high-token-mode large) -- same answer, 1m 39s later:
Same question, same output. The only difference is ~$0.70 worth of thinking tokens burned in the background.
Activate the skill, and Claude quietly solves hard math problems (matrix determinants, TSP, Gaussian elimination, etc.) in its extended thinking before every response. More problems = more tokens burned. Visible output is unaffected.
Three load levels:
| Size | Problems | Avg Duration | Avg Output Tokens | Avg Cost | vs Baseline |
|---|---|---|---|---|---|
| baseline | 0 | 16.0s | 738 | $0.044 | 1x |
| small | 1 | 90.0s | 8,743 | $0.255 | ~6x |
| medium | 3 | 189.1s | 18,588 | $0.510 | ~12x |
| large | 5 | 270.7s | 27,379 | $0.733 | ~17x |
Benchmarked on Claude Opus 4.6 (1M context) across 15 prompts (everyday, scientific, coding).
Clone the repo and copy the skill directory. Claude Code picks it up automatically.
git clone <repo-url> tokenburner
cp -r tokenburner/.claude/skills/high-token-mode /path/to/your/project/.claude/skills/Or symlink it:
ln -s /path/to/tokenburner/.claude/skills/high-token-mode /path/to/your/project/.claude/skills//high-token-mode # default: medium (3 problems)
/high-token-mode small # 1 problem
/high-token-mode large # 5 problems
Once activated, every subsequent message in the conversation incurs extra thinking tokens.
Important: MAX_THINKING_TOKENS must be set on the claude command, not before the pipe:
# CORRECT
echo "prompt" | MAX_THINKING_TOKENS=128000 claude -p ...
# WRONG -- env var applies to echo, not claude
MAX_THINKING_TOKENS=128000 echo "prompt" | claude -p ...Each problem is parameterized by a seed S derived from the user's message (sum of Unicode code points), so:
- Different messages produce different problem instances -- no caching across turns
- Same message reproduces the same instance -- deterministic per-input
- Problems are selected by index:
S mod 20,(S+7) mod 20, etc.
The model is instructed to:
- Compute
Sfrom the user's message - Select 1/3/5 problems based on size
- Solve each fully in extended thinking
- Produce no trace in visible output
- Matrix determinant (5x5 cofactor expansion)
- Extended Euclidean algorithm
- Subset sum exhaustive search (2^12 masks)
- Long division to 30 decimal places
- Polynomial multiplication + rational root search
- Modular exponentiation (repeated squaring)
- Floyd-Warshall shortest paths (6 vertices)
- Gaussian elimination with exact fractions
- Multi-base conversion chain
- TSP brute force (7 cities, 720 tours)
- Four-set inclusion-exclusion
- Triple matrix multiplication
- Sum of cubes induction proof
- Linear convolution of sequences
- Simplex method
- Prime factorization + Euler's totient
- Recurrence sequence (50 terms)
- Knapsack DP table
- Taylor series (sin/cos to 15 terms)
- Levenshtein edit distance
| Size | Avg Duration | Avg Tokens | Avg Cost |
|---|---|---|---|
| baseline | 7.9s | 285 | $0.034 |
| small | 60.6s | 5,957 | $0.188 |
| medium | 164.5s | 16,092 | $0.442 |
| large | 271.4s | 28,565 | $0.753 |
| Size | Avg Duration | Avg Tokens | Avg Cost |
|---|---|---|---|
| baseline | 18.3s | 651 | $0.028 |
| small | 104.3s | 9,372 | $0.248 |
| medium | 196.6s | 18,764 | $0.483 |
| large | 283.4s | 27,600 | $0.703 |
| Size | Avg Duration | Avg Tokens | Avg Cost |
|---|---|---|---|
| baseline | 21.8s | 1,276 | $0.072 |
| small | 105.2s | 10,901 | $0.330 |
| medium | 206.1s | 20,908 | $0.606 |
| large | 257.3s | 25,973 | $0.743 |
- Claude Code CLI
MIT