用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/SimHacker/moollm --skill multi-presence命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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基于 SOC 职业分类
| name | multi-presence |
| description | Same card active in multiple rooms simultaneously |
| license | MIT |
| tier | 1 |
| allowed-tools | ["read_file","write_file","list_dir"] |
| origin | Actor model, parallel processing, distributed systems |
| commands | ["PLAY card IN room","INSTANCES card","BROADCAST message TO card","MERGE instance-1 instance-2"] |
| statuses | ["active","blocked","ready","paused","completed"] |
| related | ["card","society-of-mind","character","room","prototype","coherence-engine","speed-of-light","data-flow"] |
| tags | ["moollm","actor","parallel","instances","distributed"] |
"The same card, active in many rooms at once."
Multi-Presence allows a single card (character, tool, skill) to be instantiated in multiple rooms simultaneously, each instance with its own state.
Like running the same program in multiple terminals. Same code, different contexts, parallel execution.
Send your researcher character to explore three branches at once:
research-room-A/
→ Researcher instance (exploring hypothesis A)
research-room-B/
→ Researcher instance (exploring hypothesis B)
research-room-C/
→ Researcher instance (exploring hypothesis C)
All three run in parallel. Compare results. Merge insights.
The Debugger character in Room A notices something. The Debugger in Room B has context that helps. They can communicate:
[Room A: Debugger]
"I found a pattern but don't understand it."
[Room B: Debugger]
"That matches what I'm seeing here. Together it suggests..."
Same card, different vantages, shared insight.
A large task splits across rooms:
Task: "Analyze all 50 documents"
document-batch-1/
→ Analyst instance (docs 1-10)
document-batch-2/
→ Analyst instance (docs 11-20)
[... etc ...]
aggregator/
→ Results flow in from all instances
> PLAY analyst-card IN room-A
Analyst instance created in room-A
> PLAY analyst-card IN room-B
Analyst instance created in room-B
> PLAY analyst-card IN room-C
Analyst instance created in room-C
Now analyst-card has three activations, each with independent state.
Each activation has its own:
# room-A/activations/analyst-001.yml
card: analyst-card
instance_id: analyst-001
tags: [, , ]
Within one LLM call, all instances can communicate:
[LLM epoch]
Analyst-A: "Found Pattern X in docs 1-10"
Analyst-B: "Found Pattern Y in docs 11-20"
Analyst-C: "Pattern X + Y together suggest Z!"
Aggregator: "Capturing insight Z as primary finding"
[End epoch — all written to files]
No round-trips. Instant collaboration.
Multi-presence follows the Actor Model:
| Actor Model | Multi-Presence |
|---|---|
| Actor | Card activation |
| Mailbox | Room's inbox |
| Message | Thrown object |
| Spawn | PLAY card |
| State | Instance YAML |
Each activation is an independent actor with:
Multiple instances can vote or reach consensus:
# Three reviewers examine a document
room-review/activations/
reviewer-001.yml # Vote: APPROVE
reviewer-002.yml # Vote: APPROVE
reviewer-003.yml # Vote: NEEDS_WORK
# Consensus protocol
consensus:
method: majority
votes: [APPROVE, APPROVE, NEEDS_WORK]
result: APPROVE (2/3)
# research-project/
swarm:
card: researcher-card
instances: 5
distribution:
- room: literature-review/
focus: "Prior work"
- room: data-analysis/
focus: "Dataset exploration"
- room: methodology/
focus: "Approach options"
- room: experiments/
focus: "Running tests"
- room: writing/
focus: "Draft sections"
coordination:
sync_interval: "After each major finding"
aggregation: "Weekly synthesis in main room"
Five researchers, one project, parallel progress.
1. PLAY card IN room → Activation created
2. Activation runs → State updated
3. Activation finishes → Can DELETE or TRANSFORM
4. TRANSFORM into result → Becomes output card
Activations can:
Activations can block waiting for external tools:
# room-A/activations/analyst-001.yml
card: analyst-card
status: blocked
blocked_on:
tool: web-search
query: "latest research on topic X"
submitted: "2024-01-15T10:30:00"
expected_duration: "~5 seconds"
The Coherence Engine leaves blocked activations alone:
Epoch scan:
analyst-001: BLOCKED on web-search → skip
analyst-002: ACTIVE → process
analyst-003: ACTIVE → process
[web-search returns]
Next epoch:
analyst-001: READY (result arrived) → resume
analyst-002: ACTIVE → process
analyst-003: BLOCKED on file-read → skip
| Status | Meaning |
|---|---|
active | Running, process this epoch |
blocked | Waiting for tool result, skip |
ready | Tool returned, resume processing |
paused | User paused, skip until resumed |
completed | Done, can be cleaned up |
When a tool returns, the result is written to the activation:
# After web-search returns
card: analyst-card
status: ready
blocked_on: null
tool_results:
- tool: web-search
query: "latest research on topic X"
completed: "2024-01-15T10:30:05"
result:
articles:
- title: "New Findings on X"
url: "https://..."
The activation resumes with the result in context.
Multiple activations can have outstanding tool calls simultaneously:
analyst-001: blocked on web-search
analyst-002: blocked on file-read
analyst-003: blocked on api-call
analyst-004: active (no tool call)
[All tools return in parallel]
Next epoch: all four ready to process!
This is async/await for LLM agents — non-blocking, parallel, resumable.
MULTI-PRESENCE — Same card in multiple rooms
ACTOR — Independent activation with state
CARD-IN-PLAY — An instantiated card
ACTIVATION — Runtime instance of a card
See: PROTOCOLS.yml