Prime — Session Startup

Complete startup checklist for new agent sessions. Direct execution (no subagents).

Design rationale: This skill executes directly rather than spawning subagents because each step is a simple command sequence (~200 tokens each), not substantial analytical work. Per Lita research: "Simple agents achieve 97% of complex system performance with 15x less code." Subagent overhead would exceed actual work.

When This Applies

Signal	Action
New session starting	Run full checklist
User says "/prime"	Run full checklist
User says "startup" or "initialize"	Run full checklist
Multi-agent coordination needed	Run full checklist

Tool Reference

Agent Mail (MCP)

Tool	Purpose
`macro_start_session(human_key, program, model)`	PREFERRED: All-in-one startup
`ensure_project(human_key)`	Initialize project in Agent Mail
`register_agent(project_key, program, model)`	Register and get agent name
`set_contact_policy(agent_name, policy)`	Set to "open" for coordination
`fetch_inbox(agent_name, limit)`	Check for messages
`send_message(to, subject, body_md)`	Send greeting/announcements

Bash Commands

Command	Purpose
`cm context "task description" --json`	Get patterns + anti-patterns before work
`cass search "query" --robot --days 7`	Search session history
`bd ready --json`	List available tasks
`bv --robot-triage`	Get task recommendations
`git status`	Check repo state
`git log --oneline -5`	Recent commits

Execution Flow

Execute these steps directly. No subagents needed.

Step 1: Register Identity

PREFERRED — Use macro for simplicity:

python

result = macro_start_session(
    human_key="PROJECT_PATH",  # Absolute path to project
    program="claude-code",
    model="opus-4.5",
    inbox_limit=10
)
agent_name = result["agent"]["name"]  # e.g., "BlueLake"

ALTERNATIVE — Manual steps:

python

# 1. Ensure project exists
ensure_project(human_key="PROJECT_PATH")

# 2. Register agent (name is auto-assigned)
result = register_agent(
    project_key="PROJECT_PATH",
    program="claude-code",
    model="opus-4.5",
    task_description="Agent initializing..."
)
agent_name = result["name"]  # SAVE THIS

# 3. Set contact policy for coordination
set_contact_policy(
    project_key="PROJECT_PATH",
    agent_name=agent_name,
    policy="open"
)

Update terminal title:

bash

echo -ne "\033]0;[{agent_name}] | PROJECT_NAME\007"

Step 2: Orient

Read project documentation:

code

Read("AGENTS.md")     # Agent workflow instructions
Read("CLAUDE.md")     # Project overview

Check recent session history:

bash

cass search "PROJECT_NAME" --workspace "PROJECT_PATH" --robot --days 7 --limit 5

Get learned patterns:

bash

cm context "starting work on PROJECT_NAME" --json

Check git state:

bash

git status
git log --oneline -5
git branch

Discover active agents:

python

ReadMcpResourceTool(
    server="mcp-agent-mail",
    uri="resource://agents/PROJECT_PATH"
)

Step 3: Coordinate

Check inbox:

python

messages = fetch_inbox(
    project_key="PROJECT_PATH",
    agent_name=agent_name,
    include_bodies=True,
    limit=20
)

Categorize messages:

•Urgent — Requires response before starting work
•Informational — [CLAIMED], [CLOSED] announcements
•Acknowledgement required — ack_required=True

Note file reservations from other agents (these are off-limits).

Send greeting (if other agents active):

python

send_message(
    project_key="PROJECT_PATH",
    sender_name=agent_name,
    to=[active_agents],
    subject="Agent Online",
    body_md="I'm online. Focusing on: [task]. What needs attention?",
    importance="normal"
)

Step 4: Discover Work

Get recommendations:

bash

bv --robot-triage      # Priority analysis
bv --robot-next        # Single best task

Get ready tasks:

bash

bd ready --json

Check already claimed:

bash

bd list --status in_progress --json

Filter by file availability:

•Check each ready task's required files
•Exclude tasks where files are reserved by others
•Recommend highest-priority available task

Step 5: Output Summary

Present to user:

markdown

## Agent Initialized

**Name:** {agent_name}
**Project:** PROJECT_NAME
**Branch:** {git_branch}

### Context
- Recent sessions: {summary from CASS}
- Active agents: {list or "none"}
- Inbox: {count} messages ({urgent} urgent)
- Blocked files: {list or "none"}

### Learned Patterns
{relevant patterns from cm}

### Recommended Task
**ID:** {task_id}
**Title:** {task_title}
**Priority:** {priority}
**Files:** {file_scope}

Ready to claim? Use `/advance` to claim and begin work.

Mandatory Gates

After priming, these gates apply to all work:

Gate	Requirement	Evidence
TDD-first	Write tests before implementation	45.97% higher success rate
ubs --staged	Security scan before every commit	~40% of LLM code has vulnerabilities
Max 3 iterations	Stop after 3 repair attempts	Security degrades with more
Escalate on failure	Create spike, notify operator	Don't waste time debugging

Quick Reference

bash

# Register (preferred)
macro_start_session(human_key=PROJECT_PATH, program="claude-code", model="opus-4.5")

# Orient
cass search "PROJECT" --robot --days 7
cm context "task" --json
git status

# Coordinate
fetch_inbox(agent_name=YOUR_NAME, limit=20)
send_message(to=[...], subject="Agent Online", ...)

# Discover
bv --robot-triage
bd ready --json

Anti-Patterns

Don't	Why	Do Instead
Skip registration	Can't coordinate	Always register first
Ignore inbox	Miss urgent messages	Check before claiming
Ignore reservations	File conflicts	Respect all reservations
Skip cm context	Miss learned patterns	Always check patterns
Claim without checking	Duplicate work	Verify availability first

Prime — Session Startup

When This Applies

Tool Reference

Agent Mail (MCP)

Bash Commands

Execution Flow

Step 1: Register Identity

Step 2: Orient

Step 3: Coordinate

Step 4: Discover Work

Step 5: Output Summary

Mandatory Gates

Quick Reference

Anti-Patterns

See Also