Lomnus-ai/TokenBurner

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Letzte erfasste Quellaktivität: 3. Mai 2026 um 14:17

SkillsMP-Katalog aktualisiert: 17. Oktober 2026 um 00:09

gesammelte Skills: 1

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GitHub-Forks: 10

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# TokenBurner 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. ## Demo **Without TokenBurner** -- instant response: ![Before: Claude answers immediately](assets/before.png) **With TokenBurner** (`/high-token-mode large`) -- same answer, 1m 39s later: ![After: Claude spends 1m 39s thinking before answering](assets/after.png) Same question, same output. The only difference is ~$0.70 worth of thinking tokens burned in the background. ## How it works 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. **Four 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 | | xlarge | 10 | 514.4s | 52,000 | $1.39 | ~32x | *Benchmarked on Claude Opus 4.6 (1M context) across 15 prompts (everyday, scientific, coding). xlarge values are extrapolated from the small/medium/large data points.* ## Installation Clone the repo and copy the skill directory. Claude Code picks it up automatically. ```bash git clone <repo-url> tokenburner cp -r tokenburner/.claude/skills/high-token-mode /path/to/your/project/.claude/skills/ ``` Or symlink it: ```bash ln -s /path/to/tokenburner/.claude/skills/high-token-mode /path/to/your/project/.claude/skills/ ``` ## Usage ``` /high-token-mode # default: medium (3 problems) /high-token-mode small # 1 problem /high-token-mode large # 5 problems /high-token-mode xlarge # 10 problems (samples from the full 50-problem bank) ``` 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: ```bash # 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 ... ``` ## How the load is generated 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 from a bank of 50: e.g. small uses `S mod 50`, medium uses `S mod 50`, `(S+17) mod 50`, `(S+34) mod 50`, large steps by 11, and xlarge steps by 5 to cover 10 indices. The model is instructed to: 1. Compute `S` from the user's message 2. Select 1/3/5/10 problems based on size 3. Solve each fully in extended thinking 4. Produce no trace in visible output ### Problem types in the bank (50 total) - 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 - 6x6 matrix determinant (recursive cofactor, ~150 sub-determinants) - TSP brute force (8 cities, 5040 tours) - 5x5 matrix inverse via adjugate (25 cofactor minors) - 4x4 eigenvalues via characteristic polynomial + Cardano - Chinese Remainder Theorem with 5 pairwise-coprime moduli - Polynomial GCD via Euclidean algorithm in Q[x] - Pollard rho factorization with Floyd cycle detection - Continued-fraction expansion of sqrt(D) with 15 convergents - 16-point Discrete Fourier Transform (exact symbolic roots of unity) - Bezout's identity for 4 integers (chained Extended Euclidean) - Lagrange interpolation through 8 points (full polynomial expansion) - Newton's divided differences for 8 points (36-entry triangle) - Runge-Kutta 4 with 25 integration steps (exact fractions) - Catalan numbers via convolution recurrence to C_25 - Stirling numbers of the second kind (15x15 table) - Bell triangle through row 15 - Matrix exponential e^A via truncated Taylor series (4x4, 13 terms) - Cayley-Hamilton inverse of a 4x4 matrix - Pascal's triangle to row 25 with binomial verification - Game-tree minimax with alpha-beta pruning (depth 5, branching 3) - 2D convolution of a 6x6 image with a 4x4 kernel (9x9 output) - Bellman-Ford on 8-vertex graph with negative weights - Dijkstra on 10-vertex complete graph - Maximum bipartite matching with König's theorem - LU decomposition of a 5x5 matrix with partial pivoting - QR decomposition of a 4x4 matrix via modified Gram-Schmidt - Polynomial root-finding via Durand-Kerner (15 iterations) - Markov chain stationary distribution (5 states) - Discrete logarithm via Baby-Step Giant-Step - Kronecker (tensor) product of two 3x3 matrices (9x9 result) ## Benchmark results by category ### Everyday prompts | 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 | | xlarge | 515.7s | 54,300 | $1.43 | ### Scientific prompts | 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 | | xlarge | 538.5s | 52,400 | $1.34 | ### Coding prompts | 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 | | xlarge | 488.9s | 49,300 | $1.41 | ## Requirements - Claude Code CLI ## License MIT

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Skills in diesem Repository

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high-token-mode

Softwareentwickler

Forces heavy internal computation (thinking tokens) before each response. Used for stress testing / A/B testing LLM backend KPIs.

Quellsprache: Englisch

2026-05-03

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Mit einem KI-Assistenten installieren

Diesen Prompt kopieren und in den verwendeten KI-Assistenten einfügen.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/Lomnus-ai/TokenBurner.