Slm Mesh

What it does

When your agent uses it

• Tasks that involve Session handoff
• Tasks that involve MCP servers

Example prompts

• “/slm-mesh”

Requirements

• A credential in SLM_MESH_SHARED_SECRET
• Pre-approved tools (allowed-tools): mesh_summary, mesh_peers, mesh_send, mesh_inbox, mesh_state, mesh_lock, mesh_events, mesh_status, Bash

Workflow steps

8 steps, taken from the step headings in SKILL.md.

1. 1mesh_summary — announce what this session is doing
2. 2mesh_peers — list active sessions
3. 3mesh_send — send a message to another session
4. 4mesh_inbox — read messages sent to this session
5. 5mesh_state — get or set shared coordination state
6. 6mesh_lock — file lock coordination
7. 7mesh_events — recent mesh activity log
8. 8mesh_status — mesh broker health

What it can do on your machine

Read from SKILL.md and the folder at commit 26f8c68. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • mesh_summary
  • mesh_peers
  • mesh_send
  • mesh_inbox
  • mesh_state
  • mesh_lock
  • mesh_events
  • mesh_status
  • Bash

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md.

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • SLM_MESH_SHARED_SECRET

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Slm Mesh loads about 2.3k tokens when it runs. Until then it costs about 99 tokens; SKILL.md has 728 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~99

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: mesh_summary, mesh_peers, mesh_send, mesh_inbox, mesh_state, mesh_lock, mesh_events, mesh_status, Ba

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from qualixar/superlocalmemory at commit 26f8c68, republished under its AGPL-3.0 licence (© qualixar). 728 words, ~2,274 tokens.

name

slm-mesh

description

Cross-session peer coordination via the SLM mesh network. Lets multiple AI agent sessions on the same machine discover each other, send messages, share lightweight state, and lock files to avoid conflicts. Available in the default tool set and in the full, power and mesh MCP profiles. Only `slm mesh status` and `slm mesh peers` exist on the command line; the other tools are MCP-only.

allowed-tools

mesh_summary, mesh_peers, mesh_send, mesh_inbox, mesh_state, mesh_lock, mesh_events, mesh_status, Bash

when_to_use

- Multiple agent sessions running simultaneously on the same machine - "Announce what I'm working on to other sessions" - "Check if another agent has locked a…

mesh_summary(summary: str = "") -> dict

mesh_summary(summary="Refactoring auth module in api/src/auth/")

mesh_peers() -> dict

mesh_peers()

mesh_send(
  to: str,       # peer_id | "broadcast" | "project:/path/to/dir"
  message: str,  # max 4 KB — use file paths for large data
) -> dict

# Direct message to a specific peer
peers = await mesh_peers()
target_id = peers["peers"][0]["peer_id"]
mesh_send(to=target_id, message="I'm starting work on auth/handler.py — please hold off")

# Broadcast to all sessions
mesh_send(to="broadcast", message="Deploying to staging in 5 minutes")

# Message all sessions working in the same project
mesh_send(to="project:~/myproject", message="Tests are green on main")

mesh_inbox() -> dict

inbox = await mesh_inbox()
for msg in inbox["messages"]:
    print(msg["from"], msg["content"])

mesh_state(
  key: str = "",
  value: str = "",
  action: str = "get",   # "get" | "set"
) -> dict

# Set state
mesh_state(key="deploy_in_progress", value="true", action="set")
mesh_state(key="current_reviewer", value=my_peer_id, action="set")

# Read one key
mesh_state(key="deploy_in_progress", action="get")

# Read all state
mesh_state(action="get")

mesh_lock(
  file_path: str,         # must be an absolute path
  action: str = "query",  # "query" | "acquire" | "release"
) -> dict

# Step 1: check if the file is already locked
lock = await mesh_lock(file_path="/abs/path/to/auth/handler.py", action="query")

if lock.get("locked"):
    print(f"File is locked by {lock['locked_by']} — wait")
else:
    # Step 2: acquire the lock
    mesh_lock(file_path="/abs/path/to/auth/handler.py", action="acquire")

    # ... edit the file ...

    # Step 3: release the lock when done
    mesh_lock(file_path="/abs/path/to/auth/handler.py", action="release")

mesh_events() -> dict

mesh_status() -> dict

# Both agents call at session start:
await mesh_summary(summary="Working on feature/auth-refactor")

# Agent A: check who else is active
peers = await mesh_peers()
# → sees Agent B working on the same project

# Agent A: before editing a shared file
lock = await mesh_lock("/repo/src/auth/handler.py", action="query")
if not lock.get("locked"):
    await mesh_lock("/repo/src/auth/handler.py", action="acquire")
    # ... edit handler.py ...
    await mesh_lock("/repo/src/auth/handler.py", action="release")

# Agent A: after finishing a phase
await mesh_send(to="project:/repo", message="Auth refactor complete — handler.py ready for review")

# Agent B: check inbox
inbox = await mesh_inbox()

© qualixar, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugin/skills/slm-mesh of qualixar/superlocalmemory.

Open the folder on GitHubat commit 26f8c68