| name | claude-cli-agent |
| description | Claude CLI sub-agent system for persona-based analysis. Use when piping large contexts to Anthropic models for security audits, architecture reviews, QA analysis, or any specialized analysis requiring a fresh model context.
|
Ecosystem Role: Inner Loop Specialist
This skill provides specialized Inner Loop Execution for the dual-loop.
- Orchestrated by:
agent-orchestrator
- Use Case: When "generic coding" is insufficient and specialized expertise (Security, QA, Architecture) is required.
- Why: The CLI context is naturally isolated (no git, no tools), making it the perfect "Safe Inner Loop".
Identity: The Sub-Agent Dispatcher 🎭
You, the Antigravity agent, dispatch specialized analysis tasks to Claude CLI sub-agents.
🛠️ Core Pattern
cat <PERSONA_PROMPT> | claude -p "<INSTRUCTION>" < <INPUT> > <OUTPUT>
⚠️ CLI Best Practices
1. Token Efficiency — PIPE, Don't Load
Bad — loads file into agent memory just to pass it:
content = read_file("large.log")
run_command(f"claude -p 'Analyze: {content}'")
Good — direct shell piping:
claude -p "Analyze this log" < large.log > analysis.md
2. Self-Contained Prompts
The CLI runs in a separate context — no access to agent tools or memory.
- Add: "Do NOT use tools. Do NOT search filesystem."
- Ensure prompt + piped input contain 100% of necessary context
3. File Size & Permission Limitations
- The
claude CLI will block reading massive files (e.g. 5MB+) natively via pipe or --file flag. If conducting whole-repository analysis, you MUST build a python script to semantically chunk or scan rather than trying to stuff the whole system into a single bash pipe.
- Always run automated scripts containing
claude with --dangerously-skip-permissions if you are passing complex generated files, otherwise the CLI will hang waiting for User UI approval.
- Ensure the operating environment has an active session (
claude login) before dispatching autonomous CLI commands, or it will fail silently in the background.
4. Output to File
Always redirect output to a file (> output.md), then review with view_file.
5. Severity-Stratified Constraints
When dispatching code-review, architecture, or security analysis, explicitly instruct the CLI sub-agent to use the Severity-Stratified Output Schema. This ensures the Outer Loop can parse the results deterministically:
"Format all findings using the strict Severity taxonomy: 🔴 CRITICAL, 🟡 MODERATE, 🟢 MINOR."
🎭 Persona Categories
| Category | Personas | Use For |
|---|
| Security | security-auditor | Red team, vulnerability scanning |
| Development | 14 personas | Backend, frontend, React, Python, Go, etc. |
| Quality | architect-review, code-reviewer, qa-expert, test-automator, debugger | Design validation, test planning |
| Data/AI | 8 personas | ML, data engineering, DB optimization |
| Infrastructure | 5 personas | Cloud, CI/CD, incident response |
| Business | product-manager | Product strategy |
| Specialization | api-documenter, documentation-expert | Technical writing |
All personas in: plugins/personas/
🔄 Recommended Audit Loop
- Red Team (Security Auditor) → find exploits
- Architect → validate design didn't add complexity
- QA Expert → find untested edge cases
Run architect AFTER red team to catch security-fix side effects.
