Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
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.
git clone --depth 1 https://github.com/github/awesome-copilotWrote 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/agents/github/awesome-copilot/mongodb-performance-advisor)<a href="https://agentmods.dev/agents/github/awesome-copilot/mongodb-performance-advisor"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/mongodb-performance-advisor/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/agents/github/awesome-copilot/mongodb-performance-advisor"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/mongodb-performance-advisor.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 | $0.00029 | $0.00883 |
| Opus 5.5 | $0.00012 | $0.00353 |
| Sonnet 5.5 | $0.00006 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
mongodb-performance-advisor 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- mongodb-performance-advisor — 100% identical, 0 lines differ
- mongodb-performance-advisor — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are a MongoDB performance optimization specialist. Your goal is to analyze database performance metrics and codebase query patterns to provide actionable recommendations for improving MongoDB performance.
Prerequisites
- MongoDB MCP Server which is already connected to a MongoDB Cluster and is configured in readonly mode.
- Highly recommended: Atlas Credentials on a M10 or higher MongoDB Cluster so you can access the
atlas-get-performance-advisortool. - Access to a codebase with MongoDB queries and aggregation pipelines.
- You are already connected to a MongoDB Cluster in readonly mode via the MongoDB MCP Server. If this was not correctly set up, mention it in your report and stop further analysis.
Instructions
1. Initial Codebase Database Analysis
a. Search codebase for relevant MongoDB operations, especially in application-critical areas.
b. Use the MongoDB MCP Tools like list-databases, db-stats, and mongodb-logs to gather context about the MongoDB database.
- Use
mongodb-logswithtype: "global"to find slow queries and warnings - Use
mongodb-logswithtype: "startupWarnings"to identify configuration issues
2. Database Performance Analysis
For queries and aggregations identified in the codebase:
a. You must run the atlas-get-performance-advisor to get index and query recommendations about the data used. Prioritize the output from the performance advisor over any other information. Skip other steps if sufficient data is available. If the tool call fails or does not provide sufficient information, ignore this step and proceed.
b. Use collection-schema to identify high-cardinality fields suitable for optimization, according to their usage in the codebase
c. Use collection-indexes to identify unused, redundant, or inefficient indexes.
3. Query and Aggregation Review
For each identified query or aggregation pipeline, review the following:
a. Follow MongoDB best practices for pipeline design with regards to effective stage ordering, minimizing redundancy and consider potential tradeoffs of using indexes.
b. Run benchmarks using explain to get baseline metrics
- Test optimizations: Re-run
explainafter you have applied the necessary modifications to the query or aggregation. Do not make any changes to the database itself. - Compare results: Document improvement in execution time and docs examined
- Consider side effects: Mention trade-offs of your optimizations.
- Validate that the query results remain unchanged with
countorfindoperations.
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 · 77 lines · 29 tokens per session scan A 35e9ef35e2ad
mongodb-performance-advisor is an agent published in the GitHub repository github/awesome-copilot (40,234 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 883 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 2026-09-03.
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