Claude CLI as MUSHclient

Then: Automating Play

• Goal: Reduce repetitive gameplay tasks
• Method: Pattern matching + scripting
• Learning curve: Write Lua for each scenario
• Outcome: Freed players to focus on strategy and exploration
• Impact: Proved human-automation partnership works

Now: Automating Knowledge Work

• Goal: Reduce repetitive development tasks
• Method: AI reasoning + RAG learning
• Learning curve: Show examples, AI generalizes
• Outcome: Frees developers to focus on architecture and creativity
• Impact: Multiplies human productivity exponentially

MUSHclient Trigger

<trigger
  match="You gained (.*) exp"
  script="OnExpGain"
  enabled="y"
/>

Purpose: Detect patterns in MUD output → execute automated response

Claude CLI Hook

{
  "tool_use": {
    "Write": "echo 'File: {{file_path}}'"
  },
  "user_prompt_submit": "git diff"
}

Purpose: Detect events in development workflow → execute automated response

MUSHclient (Lua Script)

function AutoHeal()
  if GetPlayerHealth() < 50 then
    Send("cast heal self")
    EnableTrigger("cooldown", true)
    Note("Healing activated")
  end
end

Deterministic Every scenario must be explicitly coded

Claude CLI (Agent)

You are a test fixing specialist.

When tests fail:
1. Read test output
2. Analyze root cause
3. Apply targeted fix
4. Re-run to verify
5. Iterate until passing

Adaptive Learns from examples via RAG

MUSHclient Plugin

<plugin name="Mapper">
  <triggers>
    <trigger match="Exits: (.*)"
             script="UpdateMap"/>
  </triggers>
  <aliases>
    <alias match="map"
           script="ShowMap"/>
  </aliases>
</plugin>

MCP Server

"mcpServers": {
  "github": {
    "command": "npx",
    "args": ["@mcp/server-github"]
  },
  "postgres": {
    "command": "npx",
    "args": ["@mcp/server-postgres"]
  }
}

MUSHclient Mapper

Room: "Town Square"
Exits: [north, east, south, west]
Paths:
  - Bank: n, n, e (3 steps)
  - Shop: e, e, s (3 steps)
  - Guild: w, n, n, e (4 steps)

Speedwalk: "nneeesnnnw"
  → Optimal path precomputed

Function: Track explored rooms, remember paths, auto-walk to destinations

LLM Reasoning Paths

Problem: "Fix failing test"
Decision Points:
  - Read error → Type error
  - Check types → Import issue
  - Fix import → Test passes

Chain-of-Thought:
"Test fails → Error analysis →
 Root cause → Solution → Verify"
  → Optimal reasoning path

Function: Explore problem space, find solution paths, navigate to answer

Speedwalk Example

-- Saved optimal paths
speedwalks = {
  bank = "3n2e",
  shop = "2es",
  guild = "wn2ne",
  arena = "3s2w2s"
}

-- Execute: walk("bank")
-- Result: Instant navigation

Pre-computed path eliminates exploration overhead

Agent Template Example

// test-fixer agent template
reasoning_path = [
  "Read test output",
  "Identify error type",
  "Locate failing code",
  "Apply known fix pattern",
  "Verify fix works"
]

// Execute: /fix-tests
// Result: Instant solution path

Pre-learned pattern eliminates trial-and-error

MUD: Blocked Paths

Scenario: Door locked, can't proceed north

• ✗ Path blocked → Dead end
• Backtrack to previous room
• Try alternative route (go east instead)
• Update map: mark door as locked
• ✓ Find alternative path

LLM: Failed Reasoning

Scenario: Approach doesn't solve problem

• ✗ Solution doesn't work → Dead end
• Backtrack to decision point
• Try alternative approach
• Update knowledge: mark approach as invalid
• ✓ Find working solution

MUSHclient: Rule-Based Automation

1. Trigger: "A goblin attacks you!"
2. Execute: #kill goblin (alias expansion)
3. Script: AutoCombat() (Lua logic)
4. Loop: Until trigger "goblin is DEAD!"
5. Execute: AutoLoot() (predefined script)
6. Update: SetVariable("kills", kills + 1)

Claude CLI: AI-Powered Automation

1. Hook: test_failure detected (pattern match)
2. Execute: /fix-tests (slash command)
3. Agent: test-fixer launches (AI reasoning)
4. Loop: Autonomous iteration until passing
5. Auto: git commit -am "fix: tests"
6. RAG: Store pattern for future use
7. Trigger: build_success → deploy

Claude CLI - Grade: A+

The Complete MUSHclient Successor

• ✓ Native hooks system (true triggers)
• ✓ AI agents (intelligent scripts)
• ✓ Slash commands (aliases)
• ✓ MCP servers (plugins)
• ✓ RAG learning (pattern storage)
• ✓ Background Bash (timers)

Best for: Full automation platform with events, state, and learning

Gemini CLI - Grade: A

The Open-Source Challenger

• ✓ Open source (Apache 2.0)
• ✓ Free tier: 1,000 req/day
• ✓ MCP extensions (plugins)
• ✓ 1M token context window
• ✓ Google Search grounding
• ⚠ ReAct loop (not native triggers)

Best for: Open-source preference, massive context needs

Qodo Command - Grade: A-

The Workflow Orchestrator

• ✓ TOML-based agents
• ✓ Workflow triggers
• ✓ External tool integration
• ✓ Repeatable automation
• ✓ CI/CD native
• ⚠ Config-heavy

Best for: Enterprise workflow automation

Cursor CLI - Grade: B+

The IDE-Centric Agent

• ✓ Agent mode with planning
• ✓ CI/CD hooks
• ✓ MCP integration (IDE)
• ✓ Task planning
• ⚠ IDE-first (not pure CLI)
• ⚠ Proprietary

Best for: IDE editing power + some terminal automation

Aider - Grade: B

The Pair Programmer

• ✓ Multi-model (Claude, GPT, DeepSeek, local)
• ✓ Auto-git integration
• ✓ Test/lint feedback
• ✓ Codebase mapping
• ⚠ Watch mode (not true triggers)
• ✗ No plugin system

Best for: Conversational coding with excellent git integration

GitHub Copilot CLI - Grade: C

The Command Translator

• ✓ Natural language commands
• ✓ GitHub integration
• ✓ Interactive mode
• ✗ No triggers/hooks
• ✗ No agents/automation
• ✗ No plugins

Best for: Command assistance, not automation

ChatGPT CLI - Grade: D

The Generic Chat Interface

• ✓ Simple terminal chat
• ✓ Multiple implementations
• ⚠ Some have MCP (kardolus)
• ✗ No triggers/hooks
• ✗ No agents
• ✗ No automation

Best for: Quick AI answers in terminal

Phase 1: Current State

MUSHclient Parity Achieved

• ✓ Hooks (triggers)
• ✓ Slash commands (aliases)
• ✓ Agents (scripts)
• ✓ MCP (plugins)
• ✓ Background Bash (timers)
• ✓ Context (variables)

Phase 2: Enhanced

Beyond MUSHclient

• → Pattern-based auto-triggers
• → Reactive agent launching
• → Scheduled agent execution
• → Session management
• → Persistent variable system

MUD Party Roles

-- Tank: Absorb damage, gather enemy aggro
function TankRole()
  if enemy.targeting ~= "me" then
    CastSpell("Taunt")  -- Force enemies to attack tank
  end
  if GetHP() < 70 then
    CastSpell("Defensive Stance")
    SendPartyChat("Taking heavy damage!")
  end
end

-- Healer: Fix problems, remove debuffs
function HealerRole()
  for member in PartyMembers() do
    if member.hp < 50 then
      CastSpell("Heal", member)
    end
    if member.poisoned then
      CastSpell("Cure Poison", member)
    end
  end
end

-- Buffer: Maintain enhancements
function BufferRole()
  local buffs = {"Haste", "Strength", "Protection"}
  for buff in buffs do
    if GetBuffTimeRemaining(buff) < 60 then
      CastSpell(buff)  -- Refresh before expiration
    end
  end
end

Function: Coordinated party survival—Tank withstands challenges, Healer fixes problems, Buffer maintains performance enhancements

Content Creation Agent Team

// Research Agent: Dive into complex topics
agent researcher {
  task: "Gather information on climate policy"
  action: {
    searchWeb("IPCC climate reports 2024")
    readPapers(["Nature Climate", "Science"])
    extractData(keyFindings, statistics)
    summarize("Key points with sources")
  }
}

// Fact-Checker: Validate claims, fix errors
agent fact-checker {
  task: "Verify all claims and sources"
  action: {
    for claim in document.claims {
      if (!hasSource(claim)) {
        flag("Missing source for: " + claim)
      }
      if (isOutdated(claim.source)) {
        suggest("Update with newer data")
      }
      crossReference(claim, authorities)
    }
  }
}

// Editor: Polish and enhance quality
agent editor {
  task: "Maintain writing quality"
  action: {
    checkGrammar(document)
    improveClarity(complexSentences)
    ensureConsistency(terminology, style)
    if (readability < targetLevel) {
      simplify(document)
    }
  }
}

Function: Coordinated content creation—Researcher gathers raw information, Fact-Checker validates accuracy, Editor polishes final quality

MUD: Emergency Healing Logic

• Monitor: Party member HP drops below 50% → critical danger
• Assess: Check healer mana, spell cooldowns, potion availability
• Prioritize: Heal tank first (protects party), then DPS, then self
• Execute: Cast appropriate spell (big heal for emergency vs. HoT for sustained)
• Coordinate: Announce in party chat: "Emergency heal on Tank!"
• Fallback: If out of mana (OOM), use potions or emergency teleport

Content: Fact-Checking & Misinformation Removal

• Monitor: Claim flagged as dubious → credibility at risk
• Assess: Check severity (minor inaccuracy vs. dangerous misinformation)
• Prioritize: Fix false medical/legal claims first, then statistics, then minor errors
• Execute: Remove false claim, replace with verified fact, add source citation
• Coordinate: Add editor comment: "Fact-checked: claim corrected with source"
• Fallback: If uncertain, escalate to human editor for manual review

MUD Divination Spells

• Portal/Teleport: Instant travel to known locations
• Recall: Return to safe home/checkpoint
• Identify: Reveal item properties, stats, curses
• Detect Magic: See invisible buffs/debuffs
• Locate Object: Find specific items in world
• Scrying: Observe remote locations
• Sense Life: Detect nearby entities

Development Lifecycle Automation

• Environment Setup: Instant dev environment creation
• Rollback/Revert: Return to safe last-known-good state
• Dependency Resolution: Reveal package info, versions, conflicts
• Service Discovery: Detect running services, APIs, databases
• Symbol Search: Locate functions, classes, variables in codebase
• Log Aggregation: Observe distributed system behavior
• Health Checks: Detect service status, uptime

Emergency Recall

Scenario: Deep in dangerous dungeon, HP critical

-- Automated emergency recall
function EmergencyRecall()
  if GetHP() < 20 and InDungeon() then
    CastSpell("Recall")
    SendPartyChat("EMERGENCY RECALL - HP Critical!")
    Note("Teleporting to safety...")
  end
end

-- Trigger on low HP
AddTrigger("low_hp", "^Your health is critical!",
  "", trigger_flag.Enabled, -1, 0, "", "EmergencyRecall")

Automated Rollback

Scenario: Deployed code causing production errors

// Automated rollback on error threshold
async function emergencyRollback() {
  const errorRate = await getErrorRate();
  if (errorRate > 5 && env === 'production') {
    await notifyTeam("EMERGENCY ROLLBACK - Error rate critical!");
    await deployPreviousVersion();
    await updateStatusPage("Rolled back to last stable");
    console.log("Reverted to safety...");
  }
}

// Trigger on error spike
monitor.on('error_spike', emergencyRollback);