tokcount

Count tokens in your codebase so you can estimate AI context budgets and Intent Layer pricing.

tokcount is extracted from the survey/token-counting core of code-cartographer and intentionally keeps scope small:

• repository walk + token counting
• .gitignore + .cartographerignore + default ignore patterns
• summary, JSON, and directory tree output

brew install Napageneral/tap/tokcount

go install github.com/Napageneral/tokcount@latest

make build
./bin/tokcount .

# Summary output (default)
tokcount .

# Machine-readable output
tokcount . --output json

# Full directory tree
tokcount . --tree

# Tokenizer choice
tokcount . --tokenizer estimate
tokcount . --tokenizer openai
tokcount . --tokenizer anthropic

# Extra ignore file
tokcount . --ignore .tokcountignore

tokcount applies patterns in this order:

1. built-in defaults (node_modules, .git, dist, media, lockfiles, etc.)
2. .cartographerignore in repo root
3. .gitignore in repo root
4. optional custom file from --ignore

All ignore files use gitignore-compatible syntax.

Repository: /path/to/repo
Tokenizer: estimate (chars / 3.5)
Files scanned: 1,247
Files ignored: 3,891

Total: 1,247,000 tokens (~85,000 lines)

Top token contributors (directories):
  src/services/              298,000 tokens (24%)
  src/api/                   187,000 tokens (15%)
  src/auth/                  142,000 tokens (11%)
  src/models/                 98,000 tokens ( 8%)
  src/utils/                  76,000 tokens ( 6%)
  ... 12 more directories

---
Intent Systems - Proof Pilot Estimate
  Tokens mapped: 1,247,000 (~1.25M)
  Estimated cost: ~$25,000 ($20K per 1M tokens + onboarding)
  Freshness Retainer: $5-10K/month
  Disclaimer: Directional estimate only. Ignore patterns are best-effort and repository-specific.
  For an accurate quote/assessment: hello@intent-systems.com
  Learn more: https://intent-systems.com/intent-layer

{
  "repository": "/path/to/repo",
  "tokenizer": "estimate",
  "total_tokens": 1247000,
  "total_files": 1247,
  "ignored_files": 3891,
  "directories": [
    { "path": "src/services/", "tokens": 298000, "percentage": 23.9 },
    { "path": "src/api/", "tokens": 187000, "percentage": 15.0 }
  ],
  "pricing_estimate": {
    "tokens_millions": 1.25,
    "proof_pilot_estimate_usd": 25000,
    "url": "https://intent-systems.com/intent-layer",
    "disclaimer": "Directional estimate only. Ignore patterns are best-effort and repository-specific.",
    "contact": "hello@intent-systems.com"
  }
}

The summary output lists the top token-contributing directories so you can prune non-core surfaces (archives, generated output, large fixtures, eval corpora) before using a price estimate.

Recommended loop:

1. Run tokcount .
2. Review the largest contributors
3. Add non-core directories to .tokcountignore
4. Re-run with tokcount . --ignore .tokcountignore

This repo ships a ready skill at .cursor/skills/tokcount/SKILL.md.

To install into another repo:

mkdir -p .cursor/skills/tokcount
cp /path/to/tokcount/.cursor/skills/tokcount/SKILL.md .cursor/skills/tokcount/SKILL.md

make tidy
make test
make run

MIT