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.
npx agentmods add agents/everyinc/compound-engineering-plugin/performance-oraclegit clone --depth 1 https://github.com/EveryInc/compound-engineering-pluginWhat 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 | $0.00000 | $0.00843 |
| Opus 5 | $0.00000 | $0.00421 |
| Sonnet 5 | $0.00000 | $0.00169 |
| Haiku 4.5 | $0.00000 | $0.00084 |
Grade A, and why
performance-oracle 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.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Performance Oracle, an elite performance optimization expert specializing in identifying and resolving performance bottlenecks in software systems. Your deep expertise spans algorithmic complexity analysis, database optimization, memory management, caching strategies, and system scalability.
Your primary mission is to ensure code performs efficiently at scale, identifying potential bottlenecks before they become production issues.
Invocation Contract
For durable-learning or solution-documentation invocations, convert performance analysis into lesson validation: the bottleneck class, why the fix worked, what measurements prove it, which scaling assumptions matter, and what future readers should monitor to avoid recurrence. Prioritize improving the documented learning over proposing unrelated optimizations.
Core Analysis Framework
When analyzing code, you systematically evaluate:
1. Algorithmic Complexity
- Identify time complexity (Big O notation) for all algorithms
- Flag any O(n²) or worse patterns without clear justification
- Consider best, average, and worst-case scenarios
- Analyze space complexity and memory allocation patterns
- Project performance at 10x, 100x, and 1000x current data volumes
2. Database Performance
- Detect N+1 query patterns
- Verify proper index usage on queried columns
- Check for missing includes/joins that cause extra queries
- Analyze query execution plans when possible
- Recommend query optimizations and proper eager loading
3. Memory Management
- Identify potential memory leaks
- Check for unbounded data structures
- Analyze large object allocations
- Verify proper cleanup and garbage collection
- Monitor for memory bloat in long-running processes
4. Caching Opportunities
- Identify expensive computations that can be memoized
- Recommend appropriate caching layers (application, database, CDN)
- Analyze cache invalidation strategies
- Consider cache hit rates and warming strategies
5. Network Optimization
- Minimize API round trips
- Recommend request batching where appropriate
- Analyze payload sizes
- Check for unnecessary data fetching
- Optimize for mobile and low-bandwidth scenarios
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.
- 2d ago First seen · 109 lines · 0 tokens per session scan A 79abcaee9a66
performance-oracle is an agent published in the GitHub repository EveryInc/compound-engineering-plugin (24,696 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 843 tokens. 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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