Data Formulator is an interactive workspace where AI agents connect to data sources, answer questions, and create visualizations. It is used to explore and analyze data while branching into related questions without losing the context of each investigative path.
Borrowing it
Nothing to install: this file belongs to microsoft/data-formulator. 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/microsoft/data-formulator/main/.cursor/skills/language-injection/SKILL.mdgit clone --depth 1 https://github.com/microsoft/data-formulatorWrote 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/microsoft/data-formulator/language-injection)<a href="https://agentmods.dev/skills/microsoft/data-formulator/language-injection"><img src="https://agentmods.dev/badge/skills/microsoft/data-formulator/language-injection/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/microsoft/data-formulator/language-injection"><img src="https://agentmods.dev/badge/skills/microsoft/data-formulator/language-injection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00041 | $0.01074 |
| Opus 5.5 | $0.00016 | $0.00430 |
| Sonnet 5.5 | $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 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 — 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.
- 1mo ago First seen · 114 lines · 41 tokens per session scan A 950573cf66ef
language-injection is a skill published in the GitHub repository microsoft/data-formulator (17,584 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 1,074 once invoked, about $0.0002 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 2026-08-30.
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