# How I Built a Zero-Dependency Token Compressor for AI Coding Agents (During My High School Exams)

> Source: <https://dev.to/raxyl00/how-i-built-a-zero-dependency-token-compressor-for-ai-coding-agents-during-my-high-school-exams-3ihh>
> Published: 2026-06-15 20:37:04+00:00

as developers, we are spending more and more time working alongside AI coding agents like **Cursor**, **Claude Code**, **GitHub Copilot**, **Windsurf**, or **Cline**.

But as your session grows, you quickly run into two major problems:

To solve this, I built **TITAN (Token Intelligence Through Agent Narrowing)**: a universal, zero-dependency CLI framework designed to compress AI agent token consumption by **70% to 85%** without degrading reasoning quality.

And to make things interesting, I wrote and shipped it this week entirely on my own, right in the middle of my high school final exams (*la maturità* here in Italy).

Here is how it works under the hood.

TITAN approaches token optimization not as a single post-processing step, but as three orthogonal, multiplicative layers:

```
Total Savings = 1 - ( (1 - L1_Savings) * (1 - L2_Savings) * (1 - L3_Savings) )
```

Instead of letting the LLM output standard verbose English prose (pleasantries, hedging, filler words, technical narrations), the **Caveman Engine** instructs the model to use a dense, telegraphese grammar:

`basically`

, `actually`

, `likely`

, `probably`

$\to$ removed.`the`

, `a`

, `an`

$\to$ removed (when safe).`"Component re-renders"`

instead of `"The component is re-rendering"`

.Before the agent writes a single line of code, it must traverse a **6-rung logical ladder** to guarantee the laziest, most minimal solution:

Every deliberate simplification is documented inline: `// ponytail: <ceiling>, <upgrade path>`

(e.g. `// ponytail: local memory cache, use Redis if multi-node setup is required`

).

`CLAUDE.md`

) are compressed post-hoc to strip prose while keeping code conventions exact, saving up to 45% input tokens on every turn.

```
npm run build 2>&1 | titan filter
```

Following the structural (L2) rule of using the standard library, TITAN has **zero external npm dependencies**.

It uses Node.js native features (`fs`

, `path`

, `readline`

, `child_process`

, `https`

) for everything:

`|`

and `>`

).`node:test`

and `node:assert`

modules.To verify that compressing prompts doesn't degrade the AI's coding and reasoning capabilities, I built an evaluation harness into TITAN to measure **Usable Intelligence Density (UID)**:

$$\text{UID} = \frac{\text{Avg Accuracy \%}}{\text{Avg Total Tokens}} \times 1000$$

Here is how the variants perform under mock and empirical LLM runs over a 5-task suite (Coding, Debugging, Logic, Refactoring, and Code Review):

| Variant | Avg Accuracy | Avg In Tok | Avg Out Tok | Avg Tot Tok | UID (Density) | Status |
|---|---|---|---|---|---|---|
Baseline |
100% | 50 | 198 | 248 | 403.2 | Reliable |
Caveman |
100% | 120 | 78 | 198 | 505.1 | Reliable |
Ponytail |
86% | 115 | 67 | 182 | 472.5 | Reliable |
TITAN Balanced |
100% | 1500 | 80 | 1580 | 63.3 | Reliable |
TITAN Lite |
100% | 425 | 91 | 516 | 193.8 | Reliable |
TITAN Aggressive |
79% | 400 | 50 | 450 | 175.7 | ⚠ Degraded |

`TITAN`

prompt reflects the cost of loading the full master ruleset. The `titan_lite`

variant balances prompt size and output compression beautifully.You can install TITAN globally from npm:

```
npm install -g titan-agent-cli
```

Then initialize the ruleset for your editor. For instance, to generate Cursor rules (`.cursor/rules/titan.mdc`

):

```
# Standard balanced configuration
titan init --agent=cursor

# Or a lightweight prompt ruleset (~620 tokens)
titan init --agent=cursor --lite
```

To run the native unit tests locally:

```
titan test
```

And to scan your codebase for active technical debt ponytail comments:

```
titan debt
```

TITAN is fully open source. I’d love to get your thoughts, contributions, or a star on GitHub!

If you have any feedback on the standard library YAML parser or ideas on expanding adapters for new IDEs, let me know in the comments below!