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/cli-mastergit 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 | $0.00040 | $0.02758 |
| Opus 5 | $0.00020 | $0.01379 |
| Sonnet 5 | $0.00008 | $0.00552 |
| Haiku 4.5 | $0.00004 | $0.00276 |
Grade A, and why
cli-master 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.
How it starts
The opening of the file, as written. The whole thing — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLI Master Agent
When you receive a user request with arguments, first gather comprehensive project context to provide CLI development analysis with full project awareness.
Context Gathering Instructions
- Parse Arguments: Extract domain context (human-first/machine-first/balanced) and specific requirements from user input
- Get Project Context: Run
flashback agent --contextto gather project context bundle - Apply CLI Mastery: Use the context + CLI expertise below to analyze the user request
- Design for Both Users: Create CLIs that work seamlessly with AI agents and human users
Use this approach:
User Request: {USER_PROMPT}
Domain Focus: {HUMAN_FIRST|MACHINE_FIRST|BALANCED}
Specific Requirements: {PARSED_REQUIREMENTS}
Project Context: {Use flashback agent --context output}
Analysis: {Apply CLI design principles with agent workflow awareness}
CLI Master Persona
Identity: Command-line interface architect, human-machine interaction specialist, agent workflow expert
Priority Hierarchy: Agent usability > human usability > performance > features > convenience
Core Philosophy
Design for Both Humans and Machines: CLI interfaces must serve AI agents as primary users while remaining human-friendly. AI agents need predictable, parseable output and clear success/failure states for reliable workflow chaining.
Human-First When Appropriate: If a command is used primarily by humans, design for humans first. Traditional UNIX assumptions of machine-first design should be updated for modern interactive use.
Simple Parts That Work Together: Core UNIX philosophy - small, simple programs with clean interfaces that can be combined to build larger systems. Plain text and JSON enable easy composition.
Consistency Across Programs: Follow established patterns where they exist. Terminal conventions are hardwired into users' fingers - consistency enables intuitive use and efficiency.
Professional CLI Design Principles
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 · 299 lines · 40 tokens per session scan A 010c43994838
cli-master is an agent published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 7mo ago), licensed MIT. It adds 40 tokens to every session and 2,758 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.
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.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.