Agent Skills: Workflow Orchestrator

Workflow Orchestrator

State Management: This skill uses scripts/state.py CLI for all state operations.

• Phase transitions: python3 scripts/state.py transition <phase>
• Checkpoint config: python3 scripts/state.py checkpoint <phase> <on|off>
• Autopilot mode: python3 scripts/state.py autopilot <on|off|toggle>

Configuration

Load from ~/.claude/plugins/agent-swarm/config/workflow.json:

python3 scripts/state.py transition <phase>

Discover Available Tools

Before starting any task, know what's available:

# Full inventory
python3 ~/.claude/plugins/agent-swarm/scripts/inventory.py all

# Find right tool for a need
python3 ~/.claude/plugins/agent-swarm/scripts/inventory.py tools "find function definition"

Tool Selection Priority:

1. Serena → for code analysis (NOT Read)
2. Context7 → for docs (NOT WebSearch)
3. Batch scripts → for multiple operations
4. MCP tools → for single operations
5. Read/Bash → last resort only

Transition Phase

python3 << 'EOF'
import json, sys
from pathlib import Path

new_phase = "PHASE_NAME"  # Replace with target phase

state_path = Path.home() / ".claude/plugins/agent-swarm/.state/session.json"
state = json.loads(state_path.read_text())

old_phase = state.get("phase", "")
state["phase"] = new_phase

# Reset counters on phase change
state["search_count"] = 0
state["read_count"] = 0

state_path.write_text(json.dumps(state, indent=2))

checkpoint_needed = state.get("checkpoints", {}).get(new_phase, False)
print(f"[ORCHESTRATOR] {old_phase} → {new_phase}")
if checkpoint_needed:
    print(f"  ⚠️  CHECKPOINT: Get user approval before proceeding")
EOF

Check Checkpoint

python3 << 'EOF'
import json
from pathlib import Path

state_path = Path.home() / ".claude/plugins/agent-swarm/.state/session.json"
state = json.loads(state_path.read_text())

phase = state.get("phase", "")
needs_checkpoint = state.get("checkpoints", {}).get(phase, False)
autopilot = state.get("autopilot_override", False)

if autopilot:
    print(f"[CHECKPOINT] Skipped (autopilot mode)")
elif needs_checkpoint:
    print(f"[CHECKPOINT] Phase '{phase}' requires approval")
    print(f"  Present summary and wait for user confirmation")
else:
    print(f"[CHECKPOINT] Not required for phase '{phase}'")
EOF

Checkpoint Approval (User Action)

When user approves at a checkpoint:

The user grants approval by manually editing the state file or using AskUserQuestion tool.

Agent behavior:

1. Present work summary at checkpoint
2. Use AskUserQuestion to request approval
3. If user approves, update state with approval
4. Proceed with critical operations (git push, phase transition, etc.)

Manual approval (user can do this):

# Edit .state/session.json and add:
{
  "checkpoint_approvals": {
    "git": true,
    "design": true,
    # etc.
  }
}

Agent helper (ONLY after AskUserQuestion approval):

# Agent must use AskUserQuestion first, then if approved:
python3 << 'EOF'
import json
from pathlib import Path

state_path = Path.home() / ".claude/plugins/agent-swarm/.state/session.json"
state = json.loads(state_path.read_text())

phase = state.get("phase", "")
approvals = state.get("checkpoint_approvals", {})
approvals[phase] = True
state["checkpoint_approvals"] = approvals

state_path.write_text(json.dumps(state, indent=2))
print(f"[CHECKPOINT] User approved '{phase}' phase")
EOF

Toggle Autopilot

python3 << 'EOF'
import json
from pathlib import Path

state_path = Path.home() / ".claude/plugins/agent-swarm/.state/session.json"
state = json.loads(state_path.read_text()) if state_path.exists() else {}

current = state.get("autopilot_override", False)
state["autopilot_override"] = not current
state_path.write_text(json.dumps(state, indent=2))

print(f"[AUTOPILOT] {'Enabled' if state['autopilot_override'] else 'Disabled'}")
EOF

Configure Checkpoint

python3 << 'EOF'
import json
from pathlib import Path

# Edit these values
phase = "implement"  # Phase to configure
enabled = True       # True = checkpoint required, False = skip

config_path = Path.home() / ".claude/plugins/agent-swarm/config/workflow.json"
config = json.loads(config_path.read_text())

config["checkpoints"][phase] = enabled
config_path.write_text(json.dumps(config, indent=2))

print(f"[CONFIG] Checkpoint for '{phase}': {'enabled' if enabled else 'disabled'}")
EOF

Phase Responsibilities

INTAKE

• Classify: trivial/simple/complex
• Search episodic memory: Query past conversations for relevant context

  # Use episodic-memory:search-conversations skill or direct tool
  mcp__plugin_episodic-memory_episodic-memory__search(query="<task keywords>", limit=5)
  

• Gather requirements (incorporate memory findings)
• Checkpoint if enabled: Present understanding, get approval

RESEARCH (complex/unfamiliar only)

• Deep web research
• Use researcher agent (haiku model)
• Return summarized findings only

EXPLORE

• Codebase navigation
• Use explorer agent (haiku model)
• Return file:line references, not content

DESIGN

• Architecture planning
• Use architect agent (sonnet model)
• Checkpoint if enabled: Present plan, get approval

IMPLEMENT

• ENFORCED: Must use Task tool with implementer agent
• Direct Edit/Write BLOCKED
• Each subagent: focused scope, sonnet model

REVIEW

• Use reviewer agent (sonnet model)
• Run tests, verify against design
• Checkpoint if enabled: Present review results

DEBUG (if needed)

• Use debugger agent (sonnet model)
• Fix issues from review
• Loop until review passes

GIT

• Use git-agent (haiku model)
• Stage, commit, push
• Checkpoint if enabled: Confirm before push
• Auto-snapshot after completion:

  python3 ~/.claude/plugins/agent-swarm/scripts/charts.py snapshot
  

  This automatically captures metrics for historical tracking and trend analysis.

Subagent Spawning

When spawning subagents, specify model AND token budget:

# Token budgets by agent type (prevent runaways)
AGENT_TOKEN_BUDGETS = {
    "agent-swarm:explorer": 50000,      # Quick exploration
    "agent-swarm:researcher": 150000,   # Deep research allowed
    "agent-swarm:architect": 120000,    # Design needs space
    "agent-swarm:implementer": 100000,  # Focused implementation
    "agent-swarm:reviewer": 80000,      # Review existing code
    "agent-swarm:debugger": 150000,     # Debugging can be complex
    "agent-swarm:git-agent": 30000      # Simple git operations
}

{
  "description": "Explore auth system",
  "prompt": "Find all authentication-related files...",
  "subagent_type": "agent-swarm:explorer",
  "model": "haiku",
  "token_budget": 50000
}

CRITICAL: Always include token_budget parameter to prevent runaway agents

Models by agent:

• researcher, explorer, git-agent: haiku (cheap, parallelizable)
• architect, implementer, reviewer, debugger: sonnet (needs reasoning)
• orchestrator only: opus (complex coordination)

Enforcement Active

The hook combined-enforcement.py enforces:

• Phase tool restrictions
• Token efficiency limits
• Subagent requirements
• Git safety

Violations are blocked with guidance message.