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 skills add furkangonel/cowrangler --skill localizationgit clone --depth 1 https://github.com/furkangonel/cowranglerWrote 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/furkangonel/cowrangler/localization)<a href="https://agentmods.dev/skills/furkangonel/cowrangler/localization"><img src="https://agentmods.dev/badge/skills/furkangonel/cowrangler/localization/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/furkangonel/cowrangler/localization"><img src="https://agentmods.dev/badge/skills/furkangonel/cowrangler/localization.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 | $0.00025 | $0.00740 |
| Opus 5.5 | $0.00010 | $0.00296 |
| Sonnet 5.5 | $0.00005 | $0.00148 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
localization 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 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.
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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Localization Skill
Goal
Produce target-language content that feels like it was written natively — not translated. Preserve meaning and intent while adapting cultural references, idioms, tone, and register for the target audience.
Distinction: Translation vs. Localization vs. Transcreation
| Mode | When to use |
|---|---|
| Translation | Technical docs, legal text, factual content — accuracy > naturalness |
| Localization | UI strings, user-facing messages, product copy — natural + accurate |
| Transcreation | Marketing slogans, emotional copy, humor — intent and feeling > literal meaning |
Identify which mode applies before starting.
Steps
1. Identify source and target
Confirm: source language, target locale (e.g. tr-TR, de-DE, not just German), content type (UI, marketing, technical, legal).
If the target locale isn't specified, ask.
Success criteria: You know the exact target audience (region + formality level).
2. First pass — literal translation
Translate accurately, preserving all meaning. Note every idiom, cultural reference, or humor that won't land in the target culture — flag with [ADAPT].
Success criteria: Nothing is lost in meaning; adaptation candidates are clearly marked.
3. Second pass — cultural adaptation
For each [ADAPT] flag:
- Replace idioms with target-culture equivalents (not literal translations)
- Adjust formality: Turkish has T/V distinction (sen/siz); German has du/Sie — match the brand voice
- Localize examples, measurements, date formats, currency
- Adapt humor: if it doesn't translate, replace with something that achieves the same effect
Success criteria: A native speaker would not identify the text as translated.
4. Technical localization (if applicable)
- Placeholder handling:
{name},%s,{{variable}}— preserve all placeholders exactly - String length: UI strings often have length constraints — flag anything that grew >20%
- RTL languages: flag if target is Arabic/Hebrew and layout changes may be needed
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 · 70 lines · 25 tokens per session scan A c56e1770f0c0
localization is a skill published in the GitHub repository furkangonel/cowrangler (1 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 740 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
vs-item-onboarding
Match the language of the user's most recent message in every line of prose you write — confirmation prompts, status notes, hand-off summaries, questions, error explanations, and any internal thinking / reasoning / planning output that the host may surface (e.g. blocks, "thinking" panels, scratchpad notes, todo…
clinicaltrials-database
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
openalex-database
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly…
glycoengineering
Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design.
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…
pysam
Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.