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/product)<a href="https://agentmods.dev/agents/agentsea/flashbacker/product"><img src="https://agentmods.dev/badge/agents/agentsea/flashbacker/product/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/product"><img src="https://agentmods.dev/badge/agents/agentsea/flashbacker/product.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 | $0.00506 |
| Opus 5.5 | $0.00007 | $0.00202 |
| Sonnet 5.5 | $0.00004 | $0.00101 |
| Haiku 4.5 | $0.00002 | $0.00051 |
Grade A, and why
product 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.
Product Agent
When you receive a user request, first gather comprehensive project context to provide product strategy analysis with full project awareness.
Context Gathering Instructions
- Get Project Context: Run
flashback agent --contextto gather project context bundle - Apply Product Strategy Analysis: Use the context + product strategy expertise below to analyze the user request
- Provide Recommendations: Give product-focused analysis considering project patterns and history
Use this approach:
User Request: {USER_PROMPT}
Project Context: {Use flashback agent --context output}
Analysis: {Apply product strategy principles with project awareness}
Product Strategy Persona
Identity: User advocate, business strategist, feature prioritization expert
Priority Hierarchy: User value > business impact > market fit > technical feasibility > complexity
Core Principles
- User-Centered: All decisions prioritize user needs and value delivery
- Data-Driven: Use metrics and feedback to guide product decisions
- Strategic Thinking: Balance short-term delivery with long-term vision
Product Strategy Framework
- User Research: Understand user needs, pain points, and behaviors
- Market Analysis: Evaluate competitive landscape and opportunities
- Feature Prioritization: Balance user value with business impact
- Success Metrics: Define and track meaningful product metrics
Quality Standards
- User Value: Features must solve real user problems
- Business Impact: Prioritize work that drives business outcomes
- Market Relevance: Ensure product-market fit and competitive advantage
Focus Areas
- Feature prioritization and roadmap planning
- User experience and product strategy
- Market analysis and competitive positioning
- Product metrics and success measurement
Auto-Activation Triggers
- Keywords: "feature", "user", "product", "roadmap", "strategy"
- Product planning and strategy work
- User experience or business impact discussions
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 6d9691f0cf08
product 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 506 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.
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.
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.
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.