Borrowing it
Nothing to install: this file belongs to SolvingLab/OmniWeave. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SolvingLab/OmniWeave/main/.agents/skills/add-lang/SKILL.mdgit clone --depth 1 https://github.com/SolvingLab/OmniWeaveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/solvinglab/omniweave/add-lang)<a href="https://agentmods.dev/skills/solvinglab/omniweave/add-lang"><img src="https://agentmods.dev/badge/skills/solvinglab/omniweave/add-lang/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/solvinglab/omniweave/add-lang"><img src="https://agentmods.dev/badge/skills/solvinglab/omniweave/add-lang.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00079 | $0.03030 |
| Opus 5.5 | $0.00032 | $0.01212 |
| Sonnet 5.5 | $0.00016 | $0.00606 |
| Haiku 4.5 | $0.00008 | $0.00303 |
Grade A, and why
add-lang scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
classes/structs, imports, enums; or `curl` a raw file from a known repo), then: This is a copy
97% identical to add-lang — 41 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add a language to OmniWeave
Wire a new tree-sitter language into omniweave's extraction pipeline, prove it extracts real symbols on popular repos, and prove it beats no-omniweave for an agent. Runs fully autonomously — pick repos, benchmark, update docs, then report. Never commit, push, publish, or tag (house rule); leave all changes for the user to review.
The argument is the language token used throughout the Language union, e.g.
lua, elixir, zig. If none was given, ask which language. Use the lowercase
single-token form everywhere (csharp, not c#).
Prerequisites
- Run from the omniweave repo root.
node,git,gh, and a logged-inCodexCLI (the benchmark spawns realCodex -pruns). - The benchmark uses the local dev build — Step 8 builds + links it on PATH.
Workflow
Copy this checklist and work through it in order:
- [ ] 1. Resolve language; bail early if already supported (just benchmark)
- [ ] 2. Find a grammar + health-check it (ABI / heap corruption)
- [ ] 3. Discover the grammar's AST node types (dump-ast.mjs)
- [ ] 4. Wire the language (4 files; sometimes a 5th core touch)
- [ ] 5. Build + verify-extraction loop until PASS
- [ ] 6. Add extraction tests; make them green
- [ ] 7. Auto-pick 3 popular repos by size tier; add to corpus.json
- [ ] 8. Benchmark all 3: extraction + with/without A/B
- [ ] 9. Update README + CHANGELOG
- [ ] 10. Report; do NOT commit
Step 1 — Resolve + short-circuit
Check whether the language is already wired: look for the token in the
LANGUAGES const (src/types.ts) and the EXTRACTORS map
(src/extraction/languages/index.ts). If it is already supported (e.g.
typescript, rust), skip Steps 2–6 and go straight to benchmarking
(Steps 7–8) to validate/measure it — note in the report that no code changed.
Step 2 — Find a grammar, then health-check it
ls node_modules/tree-sitter-wasms/out/ | grep -i <lang> # csharp -> c_sharp
- Present → likely off-the-shelf;
grammars.tsresolves it fromtree-sitter-wasmsautomatically. (Many languages: elixir, zig, ocaml, solidity, toml, yaml, …) - Absent → vendor a
.wasmintosrc/extraction/wasm/(likepascal/scala/lua) and add the token to the vendored branch in Step 4.
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.
- 1mo ago First seen · 220 lines · 79 tokens per session scan A d6127863387e
add-lang is a skill published in the GitHub repository SolvingLab/OmniWeave (0 stars, last pushed 4mo ago), licensed MIT. It adds 79 tokens to every session and 3,030 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 97% identical to add-lang, differing in 41 lines, and is treated as a copy.
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