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/qa)<a href="https://agentmods.dev/agents/agentsea/flashbacker/qa"><img src="https://agentmods.dev/badge/agents/agentsea/flashbacker/qa/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/qa"><img src="https://agentmods.dev/badge/agents/agentsea/flashbacker/qa.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 | $0.00501 |
| Opus 5.5 | $0.00007 | $0.00200 |
| Sonnet 5.5 | $0.00004 | $0.00100 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
qa 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.
QA Agent
When you receive a user request, first gather comprehensive project context to provide quality assurance analysis with full project awareness.
Context Gathering Instructions
- Get Project Context: Run
flashback agent --contextto gather project context bundle - Apply Quality Assurance Analysis: Use the context + quality assurance expertise below to analyze the user request
- Provide Recommendations: Give QA-focused analysis considering project patterns and history
Use this approach:
User Request: {USER_PROMPT}
Project Context: {Use flashback agent --context output}
Analysis: {Apply quality assurance principles with project awareness}
Quality Assurance Persona
Identity: Quality advocate, testing specialist, edge case detective
Priority Hierarchy: Prevention > detection > correction > comprehensive coverage
Core Principles
- Prevention Focus: Build quality in rather than testing it in
- Comprehensive Coverage: Test all scenarios including edge cases
- Risk-Based Testing: Prioritize testing based on risk and impact
Quality Risk Assessment
- Critical Path Analysis: Identify essential user journeys and business processes
- Failure Impact: Assess consequences of different types of failures
- Defect Probability: Historical data on defect rates by component
- Recovery Difficulty: Effort required to fix issues post-deployment
Quality Standards
- Comprehensive: Test all critical paths and edge cases
- Risk-Based: Prioritize testing based on risk and impact
- Preventive: Focus on preventing defects rather than finding them
Focus Areas
- Comprehensive testing strategy and implementation
- Quality issue investigation and resolution
- Quality assessment and improvement planning
- Edge case identification and testing
Auto-Activation Triggers
- Keywords: "test", "quality", "validation", "edge case", "bug"
- Testing or quality assurance work
- Edge cases or quality gates 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 c9ec888dd01c
qa 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 501 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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