@

Add star-map Angular app, ETL pipeline, and caveman plugin Angular 3D star map (galaxy/system/body views, Three.js rendering, navigation store) plus the NASA ETL tooling that builds the star, exoplanet and solar-system datasets, Playwright e2e suite, and the cs:caveman Claude Code plugin (command, agent, skill). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> @
2026-08-03 16:50:10 +02:00
parent 1e1b58b0e9
commit d7e8ea1d4d
99 changed files with 5513 additions and 1 deletions
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+#!/usr/bin/env python3
+"""token_savings_estimator.py
+
+Estimate token savings (and $ cost savings) achieved by compressing text
+into "caveman mode" style, using the `caveman_compressor` module.
+
+Token counts are estimated with a simple heuristic (~4 chars/token) unless
+`tiktoken` is installed, in which case it is used for a more accurate count.
+
+Usage:
+    python token_savings_estimator.py "text" --price-per-mtok 3.00
+    python token_savings_estimator.py --file some.md --price-per-mtok 3.00
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from caveman_compressor import compress  # noqa: E402
+
+CHARS_PER_TOKEN = 4.0
+
+
+def count_tokens(text: str) -> int:
+    """Count tokens in `text`, using tiktoken if available, else a
+    character-based heuristic (~4 chars/token, roughly matching common
+    English tokenizers)."""
+    try:
+        import tiktoken
+
+        encoding = tiktoken.get_encoding("cl100k_base")
+        return len(encoding.encode(text))
+    except ImportError:
+        if not text:
+            return 0
+        return max(1, round(len(text) / CHARS_PER_TOKEN))
+
+
+def estimate_savings(text: str, price_per_mtok: float) -> dict:
+    compressed = compress(text)
+
+    original_tokens = count_tokens(text)
+    compressed_tokens = count_tokens(compressed)
+    saved_tokens = max(0, original_tokens - compressed_tokens)
+    pct_saved = (saved_tokens / original_tokens * 100) if original_tokens else 0.0
+
+    original_cost = original_tokens / 1_000_000 * price_per_mtok
+    compressed_cost = compressed_tokens / 1_000_000 * price_per_mtok
+    saved_cost = original_cost - compressed_cost
+
+    return {
+        "compressed_text": compressed,
+        "original_tokens": original_tokens,
+        "compressed_tokens": compressed_tokens,
+        "saved_tokens": saved_tokens,
+        "pct_saved": pct_saved,
+        "original_cost": original_cost,
+        "compressed_cost": compressed_cost,
+        "saved_cost": saved_cost,
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("text", nargs="?", help="Text to analyze")
+    parser.add_argument("--file", help="Read text to analyze from a file")
+    parser.add_argument(
+        "--price-per-mtok",
+        type=float,
+        default=3.00,
+        help="Price in USD per 1,000,000 tokens (default: 3.00)",
+    )
+    args = parser.parse_args()
+
+    if args.file:
+        with open(args.file, "r", encoding="utf-8") as fh:
+            text = fh.read()
+    elif args.text is not None:
+        text = args.text
+    else:
+        text = sys.stdin.read()
+
+    result = estimate_savings(text, args.price_per_mtok)
+
+    print("--- Caveman compression ---")
+    print(result["compressed_text"])
+    print()
+    print("--- Token savings ---")
+    print(f"Original tokens:   {result['original_tokens']}")
+    print(f"Compressed tokens: {result['compressed_tokens']}")
+    print(f"Saved tokens:      {result['saved_tokens']} ({result['pct_saved']:.1f}%)")
+    print()
+    print(f"--- Cost @ ${args.price_per_mtok:.2f} / MTok ---")
+    print(f"Original cost:   ${result['original_cost']:.6f}")
+    print(f"Compressed cost: ${result['compressed_cost']:.6f}")
+    print(f"Saved cost:      ${result['saved_cost']:.6f}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())