Borrowing it
Nothing to install: this file belongs to Hack23/European-Parliament-MCP-Server. 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/Hack23/European-Parliament-MCP-Server/main/.github/skills/performance-optimization/SKILL.mdgit clone --depth 1 https://github.com/Hack23/European-Parliament-MCP-ServerWrote 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/hack23/european-parliament-mcp-server/performance-optimization)<a href="https://agentmods.dev/skills/hack23/european-parliament-mcp-server/performance-optimization"><img src="https://agentmods.dev/badge/skills/hack23/european-parliament-mcp-server/performance-optimization.svg" alt="Measured on agentmods" height="20"></a>- 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 | $0.00027 | $0.05336 |
| Opus 5 | $0.00014 | $0.02668 |
| Sonnet 5 | $0.00005 | $0.01067 |
| Haiku 4.5 | $0.00003 | $0.00534 |
Grade A, and why
performance-optimization 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 8d 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 — 726 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization Skill
Context
This skill applies when:
- Implementing MCP protocol handlers and tool endpoints
- Fetching data from European Parliament APIs
- Processing large datasets or document collections
- Designing caching strategies for API responses
- Optimizing database queries or data transformations
- Profiling and measuring performance bottlenecks
- Writing async/await code or Promise chains
- Handling concurrent requests or batch operations
- Reducing memory allocations and garbage collection pressure
- Optimizing startup time and initialization
Performance is critical for MCP servers as they power real-time AI interactions. Response times should be < 500ms, memory usage should be stable, and throughput should handle 100+ requests per 15-minute window.
Rules
- Measure Before Optimizing: Always profile and measure performance before making changes - no premature optimization
- Set Performance Budgets: API responses < 500ms, memory growth < 10MB per hour, startup time < 2s
- Cache Aggressively: Cache European Parliament API responses with appropriate TTL (5-60 minutes)
- Use Async/Await Properly: Avoid blocking the event loop - use async for I/O, sync for CPU-bound work
- Minimize Allocations: Reuse objects, use object pools, avoid creating unnecessary intermediate arrays
- Batch Operations: Group multiple API calls or database queries when possible
- Lazy Load: Defer loading of non-critical resources until needed
- Stream Large Data: Use Node.js streams for processing large documents or datasets
- Optimize Serialization: Minimize JSON parsing overhead, consider caching parsed results
- Index Data Structures: Use Maps/Sets for O(1) lookups instead of O(n) array searches
- Avoid Memory Leaks: Clear timers, remove event listeners, close connections properly
- Monitor Performance: Implement performance logging and alerting for regression detection
- Compress Responses: Use gzip/brotli for MCP responses when size exceeds 1KB
- Debounce/Throttle: Rate limit expensive operations like API calls or searches
- Use Worker Threads: Offload CPU-intensive work (parsing, transformation) to worker threads
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.
- 8d ago First seen · 726 lines · 27 tokens per session scan A bca551331044
performance-optimization is a skill published in the GitHub repository Hack23/European-Parliament-MCP-Server (28 stars, last pushed yesterday), licensed Apache-2.0. It adds 27 tokens to every session and 5,336 once invoked, about $0.0001 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.
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