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/microsoft/data-formulator/language-injectionnpx skills add microsoft/data-formulator --skill language-injectiongit clone --depth 1 https://github.com/microsoft/data-formulatorWhat 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 | $0.00041 | $0.01074 |
| Opus 5 | $0.00020 | $0.00537 |
| Sonnet 5 | $0.00008 | $0.00215 |
| Haiku 4.5 | $0.00004 | $0.00107 |
Grade A, and why
language-injection 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 yesterday.
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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Language Injection for Agent Prompts
Authoritative developer guide: docs/dev-guides/6-i18n-language-injection.md.
Prerequisites: Read
docs/dev-guides/6-i18n-language-injection.mdbefore changing Agent prompts, Agent routes, backend user-visible messages, or frontend i18n strings. If your work introduces new language injection patterns or conventions, update this file and related dev-guides accordingly.
Architecture
Frontend i18n.language → Accept-Language header → get_language_instruction()
│
build_language_instruction()
(agents/agent_language.py)
│
┌────────────┴────────────┐
▼ ▼
mode="full" mode="compact"
(text-heavy agents) (code-gen agents)
Core Modules
| Module | Role |
|---|---|
agents/agent_language.py |
build_language_instruction(lang, mode) — generates prompt fragments; inject_language_instruction() — injects into system prompts; supports 20 languages; returns "" for English |
routes/agents.py → get_language_instruction() |
Reads Accept-Language header, delegates to build_language_instruction |
routes/agents.py → _get_ui_lang() |
Extracts primary language code from Accept-Language header |
src/app/utils.tsx → fetchWithIdentity() |
Sets Accept-Language header on every API request from i18n.language |
src/app/utils.tsx → translateBackend() |
Translates backend message_code / content_code using frontend i18n |
Code Examples
Route handler — inject language
# In a Flask route handler:
lang_instruction = get_language_instruction(mode="compact")
lang_suffix = f"\n\n{lang_instruction}" if lang_instruction else ""
messages = [
{"role": "system", "content": "You are a helpful assistant." + lang_suffix},
{"role": "user", "content": user_input},
]
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.
- yesterday First seen · 114 lines · 41 tokens per session scan A 950573cf66ef
language-injection is a skill published in the GitHub repository microsoft/data-formulator (17,048 stars, last pushed 3d ago), licensed MIT. It adds 41 tokens to every session and 1,074 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 2026-08-30.
Other skills, from other repositories
planning-with-files-ar
تخطيط مستمر قائم على الملفات لعمل وكلاء الذكاء الاصطناعي متعدد الخطوات. يحتفظ بملفات taskplan.md و findings.md و progress.md على القرص، وتحقن خطافات دورة الحياة سياق التخطيط المحدد للمشروع. تقرأ الاستعادة التلقائية ملفات تخطيط المشروع فقط. يمكن للأمر الصريح session-catchup.py --metadata فحص بيانات وصفية لجلسات الوكيل…
kl-consistency-test
Write, calibrate, and debug the prefill-vs-decode logprob (KL) consistency tests in sglang -- the two independent conditions a zero requires (every operator batch-invariant, and the two paths computing the same function), which helper separates them, how to pick a threshold once they hold, and how to localize a…
i18n-localization
Internationalization and localization patterns. Detecting hardcoded strings, managing translations, locale files, RTL support.
dsh-web-documentation
Use when adding or editing dsh-web README files, docs, AGENTS.md instructions, user-facing configuration text, or bilingual documentation pairs.
baoyu-youtube-transcript
Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面"…
indication-dossier
Build a source-backed biomedical indication dossier. Use when a research task asks for disease biology, target rationale, patient segmentation, biomarkers, trials, drugs, competitive landscape, or translational evidence.