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 skills/saitarrun/devforge-ai/performance-optimizationnpx skills add saitarrun/Devforge-ai --skill performance-optimizationgit clone --depth 1 https://github.com/saitarrun/Devforge-aiWhat 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 | $0.00025 | $0.00434 |
| Opus 5 | $0.00013 | $0.00217 |
| Sonnet 5 | $0.00005 | $0.00087 |
| Haiku 4.5 | $0.00003 | $0.00043 |
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 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.
What it actually says
Skill: Performance Optimization
Systematic approach to identifying and eliminating performance bottlenecks through profiling, benchmarking, and data-driven optimization.
Process
- Establish Baselines — Measure current performance (latency, throughput, memory, CPU)
- Profile — Identify where time/resources are spent (flame graphs, traces, logs)
- Find Bottlenecks — Rank by impact (80/20 rule: fix the top bottlenecks first)
- Optimize — Apply targeted fixes (algorithm, caching, parallelization, resource allocation)
- Verify — Re-measure to confirm improvements
- Prevent Regressions — Add performance tests, monitoring, budgets
Key Metrics
- Latency: p50, p95, p99 response time
- Throughput: Requests per second, transactions per minute
- Resource Usage: CPU %, memory consumption, disk I/O
- Cost: Infrastructure cost per unit work
Common Bottlenecks
- Inefficient algorithms (O(n²) instead of O(n log n))
- Missing indexes on database queries
- Synchronous operations that should be async
- Excessive logging or serialization
- Memory leaks or inefficient data structures
- Network calls without batching/caching
- Single-threaded workloads that could parallelize
Optimization Strategies
- Algorithmic: Better algorithm selection
- Caching: Reduce computation, add layer caching
- Async: Non-blocking operations, parallelization
- Resource Allocation: CPU, memory, connections tuning
- Infrastructure: Scaling, CDN, geographic distribution
- Monitoring: Real-time alerting on perf regressions
Status: Ready for profiling, optimization, and performance tuning Best for: Latency reduction, throughput improvement, resource efficiency
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 · 50 lines · 25 tokens per session scan A 39ba4e3abb93
performance-optimization is a skill published in the GitHub repository saitarrun/Devforge-ai (5 stars, last pushed 19d ago), licensed Apache-2.0. It adds 25 tokens to every session and 434 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 2026-08-31.
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