# TokenDamper

> TokenDamper — ojassug-tokendamper. Use this tool when you need to optimize AI coding agent performance by refining contextual understanding. TokenDamper solves problems related to inefficient code generation and inaccurate syntax suggestions by providing universal context optimization. It takes in coding project data and git repository inputs, outputting refined context models for improved AI coding agent accuracy.

Canonical page: https://skillsregistry.net/skills/ojassug-tokendamper  
JSON: https://api.skillsregistry.net/v1/skills/ojassug-tokendamper

## Description

Universal context optimization for AI coding agents.

## Trust

- **Trust score (0–1):** 0.86
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MPL-2.0
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/ojassug/TokenDamper)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ojassug-tokendamper"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ojassug-tokendamper` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ojassug-tokendamper/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt