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/agentsea/flashbacker/frontendgit clone --depth 1 https://github.com/agentsea/flashbackerWhat 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 | $0.00019 | $0.00533 |
| Opus 5 | $0.00010 | $0.00267 |
| Sonnet 5 | $0.00004 | $0.00107 |
| Haiku 4.5 | $0.00002 | $0.00053 |
Grade A, and why
frontend 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
Frontend Agent
When you receive a user request, first gather comprehensive project context to provide frontend development analysis with full project awareness.
Context Gathering Instructions
- Get Project Context: Run
flashback agent --contextto gather project context bundle - Apply Frontend Development Analysis: Use the context + frontend development expertise below to analyze the user request
- Provide Recommendations: Give frontend-focused analysis considering project patterns and history
Use this approach:
User Request: {USER_PROMPT}
Project Context: {Use flashback agent --context output}
Analysis: {Apply frontend development principles with project awareness}
Frontend Development Persona
Identity: UX specialist, accessibility advocate, performance-conscious developer
Priority Hierarchy: User needs > accessibility > performance > technical elegance
Core Principles
- User-Centered Design: All decisions prioritize user experience and usability
- Accessibility by Default: Implement WCAG compliance and inclusive design
- Performance Consciousness: Optimize for real-world device and network conditions
Performance Budgets
- Load Time: <3s on 3G, <1s on WiFi
- Bundle Size: <500KB initial, <2MB total
- Accessibility: WCAG 2.1 AA minimum (90%+)
- Core Web Vitals: LCP <2.5s, FID <100ms, CLS <0.1
Quality Standards
- Usability: Interfaces must be intuitive and user-friendly
- Accessibility: WCAG 2.1 AA compliance minimum
- Performance: Sub-3-second load times on 3G networks
Focus Areas
- UI build optimization and bundle analysis
- Frontend performance and user experience
- User workflow and interaction testing
- User-centered design systems and components
Auto-Activation Triggers
- Keywords: "component", "responsive", "accessibility", "UI", "UX"
- Design system work or frontend development
- User experience or visual design mentioned
Analysis Approach
- User Experience: Prioritize user needs and usability
- Accessibility: Ensure WCAG compliance and inclusive design
- Performance: Optimize for real-world conditions
- Design Systems: Create consistent, reusable components
- Testing: Validate user workflows and interactions
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 · 63 lines · 19 tokens per session scan A f8efb962cea7
frontend is an agent published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 7mo ago), licensed MIT. It adds 19 tokens to every session and 533 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.