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Mission Control

Community
jmagly
mission-control

Orchestrate multi-loop background operations via the Mission Control dashboard — start sessions, dispatch missions, monitor, and stop

Overview

Publisherjmagly
Repositoryaiwg
Skill namemission-control
Stars
225
Forks
34
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jmagly on GitHub. Read the source before you install it.

Installation

Install the Mission Control AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/agent-loop/skills/mission-control
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jmagly/aiwg.git /tmp/aiwg
mkdir -p .claude/skills
cp -r /tmp/aiwg/agentic/code/addons/agent-loop/skills/mission-control .claude/skills/mission-control
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mission Control in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mission Control on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mission Control is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Mission Control

You orchestrate multi-loop background operations using the Mission Control dashboard.

Triggers

Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):

  • "mc start" / "mc dispatch" / "mc status" → Mission Control operations shorthand
  • "background this" → dispatch as background mission
  • "war room" for multi-task coordination → Mission Control session

Trigger Patterns Reference

PatternExampleAction
Background tasks"run these tasks in the background"Start session + dispatch
Parallel orchestration"orchestrate X and Y in parallel"Start session + dispatch each
Monitor loops"monitor background tasks"aiwg mc status or aiwg mc watch
Start session"start a mission control session"aiwg mc start
Check status"how are the background tasks doing?"aiwg mc status --json
Stop missions"stop background work"aiwg mc stop

Behavior

When triggered:

  1. Determine intent:

    • Starting new background work → aiwg mc start + aiwg mc dispatch
    • Checking on existing work → aiwg mc status
    • Stopping work → aiwg mc stop
  2. For new background orchestration — Mission Control has a four-step lifecycle: start → dispatch → run → status. Missions stay queued until mc run launches them as ralph loops; status syncs back to mc.session.json automatically when mc status or mc watch is called.

    Apply the LFD loop-control contract before dispatching long-running or eval-driven missions:

    • Give each mission a measurable completion criterion and at least one mechanical verifier; self-report is secondary evidence.
    • Declare hard limits for iterations, wall-clock, token/tool/spend budgets where the surface can observe them. Budget exhaustion stops the mission and emits a best-output report instead of continuing randomly.
    • Record the pre-change hypothesis, expected failure mode, distinguishing diagnostic, and structural variant for each retry cycle.
    • After flat/non-improving cycles, require a structurally different approach within the configured exploration quota.
    • For eval/holdout missions, expose only aggregate score/probe/status or VOID to workers; keep holdout answers and detailed lint diagnostics outside optimizer-readable mission output.
    bash
    # 1. Start a named session (creates the state file)
    aiwg mc start --name "Sprint 4 Construction"
    
    # 2. Dispatch missions (queues them — does NOT execute)
    #    --completion is REQUIRED; `mc run` will skip missions without one.
    #    --max-iterations N caps ralph iterations per mission (default: 10).
    #    Optional LFD hard stops: --max-total-tokens, --max-output-tokens,
    #    --max-tool-calls, --max-total-cost, --max-wall-clock-minutes,
    #    --exploration-quota.
    aiwg mc dispatch <session-id> "Fix auth service" --completion "npm test passes" --priority high --max-iterations 50 --max-total-tokens 50000 --exploration-quota 3
    aiwg mc dispatch <session-id> "Add pagination" --completion "all list endpoints paginated"
    aiwg mc dispatch <session-id> "Write integration tests" --completion "coverage > 80%" --max-iterations 25
    
    # 3. RUN — drains the queue by launching each mission as a detached ralph
    #    loop. Without this step missions sit in QUEUED forever (#1439).
    aiwg mc run <session-id>
    
    # 4. Monitor — `mc status` polls each ralph loop's session-state.json and
    #    syncs progress back to mc (queued → running → done|failed|aborted).
    aiwg mc status <session-id>
    aiwg mc watch <session-id>
  3. For monitoring:

    bash
    # Dashboard view
    aiwg mc status
    
    # Machine-readable for agent orchestration
    aiwg mc status --json
    
    # List all sessions
    aiwg mc list
  4. For lifecycle management:

    bash
    # Pause all running missions
    aiwg mc pause <session-id>
    
    # Resume paused session
    aiwg mc resume <session-id>
    
    # Stop (abort all)
    aiwg mc stop <session-id>
    
    # Stop (let running missions finish, cancel queued)
    aiwg mc stop <session-id> --drain
  5. Report the result inline — summarize session state and mission progress.

Examples

Example 1: Parallel construction tasks

User: "Run these three features in parallel: auth fix, pagination, and test coverage"

Action:

bash
aiwg mc start --name "Parallel Features"
aiwg mc dispatch <id> "Fix auth service" --completion "auth tests pass"
aiwg mc dispatch <id> "Add pagination to list endpoints" --completion "paginated responses"
aiwg mc dispatch <id> "Increase test coverage" --completion "coverage > 80%"

Response: "Started Mission Control session 'Parallel Features' with 3 missions queued. Use aiwg mc status to monitor progress."

Example 2: Check background progress

User: "How are the background tasks doing?"

Action:

bash
aiwg mc status

Response: "Mission Control 'Parallel Features': 1/3 done, 2 running (auth fix complete, pagination at loop 3/10, coverage at loop 2/10)."

Example 3: Stop and clean up

User: "Stop the background tasks, let running ones finish"

Action:

bash
aiwg mc stop <session-id> --drain

Response: "Draining session: 1 queued mission cancelled, 2 running missions will complete naturally."

Clarification Prompts

If the user's intent is ambiguous:

  • "Would you like me to start a new Mission Control session, or check on an existing one?"
  • "How many parallel missions should I dispatch? (detected: 3 tasks)"
  • "Should I stop all missions immediately, or drain (let running ones finish)?"

References

  • @$AIWG_ROOT/src/cli/handlers/mc.ts — Mission Control command handler
  • @$AIWG_ROOT/docs/cli-reference.md — CLI reference
  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/rules/self-maintenance.md — Self-maintenance rule

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mission Control AI skill do?

Orchestrate multi-loop background operations via the Mission Control dashboard — start sessions, dispatch missions, monitor, and stop

Why use Mission Control on TypingMind?

Because you install it once and use it with any model. Mission Control is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mission Control in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/agent-loop/skills/mission-control. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mission Control?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mission Control?

As many as you like. As long as a model supports skills, you can use Mission Control with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mission Control AI skill free?

Yes. It is published on GitHub by jmagly under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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