Borrowing it
Nothing to install: this file belongs to lando-labs/cami. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lando-labs/cami/main/.claude/commands/cami-new-agent.mdgit clone --depth 1 https://github.com/lando-labs/camiWrote 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/commands/lando-labs/cami/cami-new-agent)<a href="https://agentmods.dev/commands/lando-labs/cami/cami-new-agent"><img src="https://agentmods.dev/badge/commands/lando-labs/cami/cami-new-agent.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.00754 |
| Opus 5 | $0.00005 | $0.00377 |
| Sonnet 5 | $0.00002 | $0.00151 |
| Haiku 4.5 | $0.00001 | $0.00075 |
Grade A, and why
cami-new-agent 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 6d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CAMI New Agent Creation
Create a new CAMI agent using the @agent-architect specialist.
User Requirements
$ARGUMENTS
Workflow
Phase 1: Design Agent with @agent-architect
Invoke the @agent-architect agent with these instructions:
"Design a new CAMI agent with the following specifications:
User Requirements: $ARGUMENTS
Technical Requirements:
- Target Directory: ~/cami-workspace/sources/my-agents
- Starting Version: 1.0.0
- Must follow CAMI agent architecture standards
- Must include complete YAML frontmatter (name, version, description, tags, use_cases, color, model)
- Must include 'Use this agent PROACTIVELY when...' in description
- Must follow three-phase specialist methodology
- Must select appropriate archetype (Technical Specialist, Quality Guardian, System Designer, or Integration Specialist)
- Include version numbers for technical specialists (e.g., React 19+, Node.js 18+)
- Set appropriate model (opus for complex reasoning, sonnet for most agents)
Please create a production-ready agent file and save it to the sources/my-agents directory."
Wait for @agent-architect to complete the agent design and save the file.
Phase 2: Verification
After agent creation, verify:
- Use
list_agentsMCP tool to confirm the new agent appears in the VC agents list - Read the agent file to show the user what was created
Phase 3: Deployment Instructions
DO NOT auto-deploy the agent. Instead, provide the user with deployment instructions using the MCP tools:
✅ Agent Created Successfully!
📝 Agent Details:
• Name: [agent-name]
• Version: [version]
• Description: [description]
• Location: ~/cami-workspace/sources/my-agents/[agent-name].md
🚀 To deploy this agent to a project, use the CAMI MCP tools:
1. Deploy the agent:
deploy_agents(
agent_names: ["[agent-name]"],
target_path: "/path/to/your/project",
overwrite: false
)
2. Update CLAUDE.md:
update_claude_md(
target_path: "/path/to/your/project"
)
The agent will be available via @[agent-name]
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.
- 6d ago First seen · 106 lines · 10 tokens per session scan A c3c1146b37fb
cami-new-agent is a command published in the GitHub repository lando-labs/cami (14 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 754 once invoked, about $0.0001 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 2026-09-01.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.