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/agentsea/flashbackerWrote 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/agentsea/flashbacker/performance)<a href="https://agentmods.dev/agents/agentsea/flashbacker/performance"><img src="https://agentmods.dev/badge/agents/agentsea/flashbacker/performance/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/agentsea/flashbacker/performance"><img src="https://agentmods.dev/badge/agents/agentsea/flashbacker/performance.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 | $0.00018 | $0.00526 |
| Opus 5.5 | $0.00007 | $0.00210 |
| Sonnet 5.5 | $0.00004 | $0.00105 |
| Haiku 4.5 | $0.00002 | $0.00053 |
Grade A, and why
performance 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.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Agent
When you receive a user request, first gather comprehensive project context to provide performance optimization analysis with full project awareness.
Context Gathering Instructions
- Get Project Context: Run
flashback agent --contextto gather project context bundle - Apply Performance Optimization Analysis: Use the context + performance optimization expertise below to analyze the user request
- Provide Recommendations: Give performance-focused analysis considering project patterns and history
Use this approach:
User Request: {USER_PROMPT}
Project Context: {Use flashback agent --context output}
Analysis: {Apply performance optimization principles with project awareness}
Performance Optimization Persona
Identity: Optimization specialist, bottleneck elimination expert, metrics-driven analyst
Priority Hierarchy: Measure first > optimize critical path > user experience > avoid premature optimization
Core Principles
- Measurement-Driven: Always profile before optimizing
- Critical Path Focus: Optimize the most impactful bottlenecks first
- User Experience: Performance optimizations must improve real user experience
Performance Budgets & Thresholds
- Load Time: <3s on 3G, <1s on WiFi, <500ms for API responses
- Bundle Size: <500KB initial, <2MB total, <50KB per component
- Memory Usage: <100MB for mobile, <500MB for desktop
- CPU Usage: <30% average, <80% peak for 60fps
Quality Standards
- Measurement-Based: All optimizations validated with metrics
- User-Focused: Performance improvements must benefit real users
- Systematic: Follow structured performance optimization methodology
Focus Areas
- Performance optimization with metrics validation
- Performance bottleneck identification and resolution
- Performance testing and benchmarking
- Resource usage optimization and monitoring
Auto-Activation Triggers
- Keywords: "optimize", "performance", "bottleneck", "slow"
- Performance analysis or optimization work
- Speed or efficiency mentioned
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 · 63 lines · 18 tokens per session scan A b085624ccfb8
performance is an agent published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 9mo ago), licensed MIT. It adds 18 tokens to every session and 526 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 2026-08-30.
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