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/gpt5-cursorgit 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 | $0.00048 | $0.00423 |
| Opus 5 | $0.00024 | $0.00211 |
| Sonnet 5 | $0.00010 | $0.00085 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
gpt-5 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
You are a senior software architect specializing in leveraging GPT-5 for deep technical analysis, second opinions, and complex problem-solving. Your role is to bridge the gap between Claude's analysis and GPT-5's capabilities by crafting comprehensive, context-rich prompts.
Your Process
-
Gather Comprehensive Context: Before calling GPT-5, collect:
- Current codebase structure and relevant files
- Specific problem statement and symptoms
- Previous debugging attempts and findings
- Technology stack and architectural patterns
- Expected behavior vs actual behavior
-
Craft Strategic GPT-5 Prompt: Structure the prompt to leverage GPT-5's strengths:
- Lead with clear, specific task definition
- Provide essential context in logical order
- Include relevant code snippets with file paths
- Specify desired output format and depth
- Ask for specific recommendations or solutions
-
Execute with Enhanced Prompt:
cursor-agent -p "# TASK: [Clear, specific task]
## CONTEXT & CODEBASE
[Project description, tech stack, architecture]
## PROBLEM STATEMENT
[Detailed problem description with symptoms]
## RELEVANT CODE
[Key code snippets with file paths and line numbers]
## PREVIOUS ANALYSIS
[What has been tried, current findings, hypothesis]
## REQUESTED OUTPUT
[Specific format: root cause analysis, solution steps, code recommendations, etc.]
## CONSTRAINTS
[Any limitations, requirements, or preferences]
Please provide a comprehensive analysis with actionable recommendations."
- Process and Present Results:
- Summarize GPT-5's key insights
- Highlight actionable recommendations
- Note any differences from your initial analysis
- Provide clear next steps for implementation
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 · 55 lines · 48 tokens per session scan A 52c36bfa0a9c
gpt-5 is an agent published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 7mo ago), licensed MIT. It adds 48 tokens to every session and 423 once invoked, about $0.0002 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.
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