gitlab-mcp-server context-engineering.instructions.md

Guidelines for organizing code and project files so GitHub Copilot, an AI coding assistant, can better understand their purpose and relationships.

In plain words
What is it for?
Use it when structuring projects, naming files and variables, defining public interfaces, choosing types, and preparing relevant files for Copilot.
Why use it?
Clear names, types, file locations, and project context give Copilot more useful information when it suggests code or answers questions.

Instructions file for GitHub Copilot

Install

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.

agentmods
npx agentmods add instructions/jmrplens/gitlab-mcp-server/context-engineering
Clone the repo
git clone --depth 1 https://github.com/jmrplens/gitlab-mcp-server

Made for: GitHub Copilot.

Per session 545 This file is loaded in full into every session.
When invoked 545 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00545 $0.00545
Opus 5 $0.00272 $0.00272
Sonnet 5 $0.00109 $0.00109
Haiku 4.5 $0.00055 $0.00055

Measured 2d ago against content hash 303780cee09b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

gitlab-mcp-server context-engineering.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 2d 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.

.github/instructions/context-engineering.instructions.md · 45 lines

How it starts

The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Context Engineering

Principles for helping GitHub Copilot understand your codebase and provide better suggestions.

Project Structure

  • Use descriptive file paths: src/auth/middleware.ts > src/utils/m.ts. Copilot uses paths to infer intent.
  • Colocate related code: Keep components, tests, types, and hooks together. One search pattern should find everything related.
  • Export public APIs from index files: What's exported is the contract; what's not is internal. This helps Copilot understand boundaries.

Code Patterns

  • Prefer explicit types over inference: Type annotations are context. function getUser(id: string): Promise<User> tells Copilot more than function getUser(id).
  • Use semantic names: activeAdultUsers > x. Self-documenting code is AI-readable code.
  • Define constants: MAX_RETRY_ATTEMPTS = 3 > magic number 3. Named values carry meaning.

Working with Copilot

  • Keep relevant files open in tabs: Copilot uses open tabs as context signals. Working on auth? Open auth-related files.
  • Position cursor intentionally: Copilot prioritizes code near your cursor. Put cursor where context matters.
  • Use Copilot Chat for complex tasks: Inline completions have minimal context. Chat mode sees more files.

Context Hints

  • Add a COPILOT.md file: Document architecture decisions, patterns, and conventions Copilot should follow.
  • Use strategic comments: At the top of complex modules, briefly describe the flow or purpose.
  • Reference patterns explicitly: "Follow the same pattern as src/api/users.ts" gives Copilot a concrete example.

Multi-File Changes

  • Describe scope first: Tell Copilot all files involved before asking for changes. "I need to update the User model, API endpoint, and tests."
  • Work incrementally: One file at a time, verifying each change. Don't ask for everything at once.
  • Check understanding: Ask "What files would you need to see?" before complex refactors.

Read the full file on GitHub · 45 lines

Changes

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.

  1. 2d ago First seen · 45 lines · 545 tokens per session scan A 303780cee09b

Subscribe to this mod's changes

gitlab-mcp-server context-engineering.instructions.md is an instructions file published in the GitHub repository jmrplens/gitlab-mcp-server (31 stars, last pushed 2d ago), licensed MIT. It adds 545 tokens to every session, about $0.0027 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.