Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/viknesh20-20/claude-code-tool-kit/convert-codenpx skills add viknesh20-20/claude-code-tool-kit --skill convert-codegit clone --depth 1 https://github.com/viknesh20-20/claude-code-tool-kitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00030 | $0.00736 |
| Opus 5 | $0.00015 | $0.00368 |
| Sonnet 5 | $0.00006 | $0.00147 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
convert-code scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Conversion
Instructions
Step 1: Analyze Source Code
- Read the source file completely
- Identify:
- All functions, classes, and types
- External dependencies and their equivalents in the target language
- Error handling patterns
- Async/concurrent patterns
- Data structures and their representations
- Tests (if present)
Step 2: Map Language Idioms
Don't transliterate — convert to idiomatic target code:
| Source Pattern | Target Equivalent |
|---|---|
| Python list comprehension | Java streams / Go slices / Rust iterators |
| JS Promise.all | Python asyncio.gather / Go goroutines+WaitGroup / Rust tokio::join |
| Python dict | Go map / Rust HashMap / Java Map |
| JS destructuring | Go multiple returns / Python tuple unpacking |
| Python decorator | Java annotation / Go middleware / Rust macro |
| C# LINQ | Python generators / JS array methods / Rust iterators |
| Ruby blocks | Python context managers / Go defer / Rust closures |
Step 3: Handle Dependencies
For each import/dependency in the source:
- Find the equivalent package/library in the target ecosystem
- If no direct equivalent exists, note it and implement the functionality
- Map API calls to the target library's conventions
Step 4: Convert Error Handling
Different languages handle errors differently:
| Language | Pattern |
|---|---|
| Python | try/except, raise |
| JavaScript/TypeScript | try/catch, throw, Promise rejection |
| Go | Multiple return values (value, error) |
| Rust | Result<T, E>, Option, ? operator |
| Java/C# | try/catch, checked/unchecked exceptions |
| Ruby | begin/rescue/ensure |
| Elixir | {:ok, value} / {:error, reason} tuples |
Convert error handling to the idiomatic pattern of the target language.
Step 5: Convert Types
Map type systems:
- Dynamic → Static: Infer and add explicit types
- Static → Dynamic: Simplify but add runtime checks where safety-critical
- Generics: Map to target language's generic system
- Null safety: Map Optional/Maybe/Option patterns appropriately
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 86 lines · 30 tokens per session scan A b7e7a6bea7aa
convert-code is a skill published in the GitHub repository viknesh20-20/claude-code-tool-kit (7 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 736 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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