Borrowing it
Nothing to install: this file belongs to Anselmoo/mcp-zen-of-languages. 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/Anselmoo/mcp-zen-of-languages/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/Anselmoo/mcp-zen-of-languagesWrote 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/instructions/anselmoo/mcp-zen-of-languages/copilot-instructions)<a href="https://agentmods.dev/instructions/anselmoo/mcp-zen-of-languages/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/anselmoo/mcp-zen-of-languages/copilot-instructions/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/instructions/anselmoo/mcp-zen-of-languages/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/anselmoo/mcp-zen-of-languages/copilot-instructions.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 | $0.01855 | $0.01855 |
| Opus 5 | $0.00928 | $0.00928 |
| Sonnet 5 | $0.00371 | $0.00371 |
| Haiku 4.5 | $0.00186 | $0.00186 |
Grade A, and why
mcp-zen-of-languages copilot-instructions.md 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 6d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Zen of Languages - AI Agent Instructions
Use regularly
Serenain the MCP Zen of Languages repository for code analysis, following the architecture and patterns established in the codebase.Serenaallows saving tokens by writing, fetching, deleting, and reading memories which should be synchronized with plans. Always ensure that any code changes pass the pre-commit checks before claiming completion.
Architecture Overview
This is an MCP server for multi-language code analysis against "zen principles" (idiomatic best practices). Core components:
- server.py: FastMCP server exposing analysis tools via
@mcp.tooldecorators - main.py: Entry point for
python -m mcp_zen_of_languagesorzen-mcp-server - config.py: Auto-loads
zen-config.yaml(CWD or parent untilpyproject.toml) - languages/*/rules.py: Canonical Pydantic models defining zen principles per language
- analyzers/: Language-specific analyzers using Template Method pattern (see
base.py) - analyzers/pipeline.py: Builds detector configs from rules and merges overrides
- analyzers/detectors/: Individual violation detectors (Strategy pattern)
- models.py: Analysis results with dict-like access for legacy compatibility
- cli.py: Local analysis wrapper (
zen check file.py)
Data flow: server/cli -> create_analyzer() -> BaseAnalyzer.analyze() (parse/metrics) -> DetectionPipeline detectors -> AnalysisResult.
Running the Server
# Via module
python -m mcp_zen_of_languages
# Via entry point (after uv sync)
zen-mcp-server
# For local CLI analysis
zen check myfile.py
Build, Test, Lint
- Setup:
uv sync --all-groups --all-extras - Build:
uv build - Tests:
uv run pytest - Single test:
uv run pytest tests/test_server_routing.py::test_analyze_zen_violations_python - Type check:
uv run ty check(preferred over mypy) - Lint:
uv run ruff check - Pre-commit (all, mandatory before completion):
uvx pre-commit run --all-files - Pre-push docs gate:
uvx pre-commit run --hook-stage pre-push --all-files
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.
- 6d ago Changed · -3 tokens per session a125668cb3c8
- 11d ago First seen · 157 lines · 1,858 tokens per session scan A 0f77de81d617
mcp-zen-of-languages copilot-instructions.md is an instructions file published in the GitHub repository Anselmoo/mcp-zen-of-languages (2 stars, last pushed 2d ago), licensed MIT. It adds 1,855 tokens to every session, about $0.0093 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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