Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/alonf/mcppythondemo/distributed-meshnpx skills add alonf/MCPPythonDemo --skill distributed-meshgit clone --depth 1 https://github.com/alonf/MCPPythonDemoWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00016 | $0.03069 |
| Opus 5 | $0.00008 | $0.01535 |
| Sonnet 5 | $0.00003 | $0.00614 |
| Haiku 4.5 | $0.00002 | $0.00307 |
Grade A, and why
distributed-mesh scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**Zone 3 — Remote-Opaque:** Different org, no shared auth. Transport: `curl` to fetch published contracts (SUMMARY.md). One-way visibility — you see only what they publish. This is a copy
97% identical to distributed-mesh — 574 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SCOPE
✅ THIS SKILL PRODUCES (exactly these, nothing more):
mesh.json— Generated from user answers about zones and squads (which squads participate, what zone each is in, paths/URLs for each), usingmesh.json.examplein this skill's directory as the schema templatesync-mesh.shandsync-mesh.ps1— Copied from this skill's directory into the project root (these are bundled resources, NOT generated code)- Zone 2 state repo initialization (if applicable) — If the user specified a Zone 2 shared state repo, run
sync-mesh.sh --initto scaffold the state repo structure - A decision entry in
.squad/decisions/inbox/documenting the mesh configuration for team awareness
❌ THIS SKILL DOES NOT PRODUCE:
- No application code — No validators, libraries, or modules of any kind
- No test files — No test suites, test cases, or test scaffolding
- No GENERATING sync scripts — They are bundled with this skill as pre-built resources. COPY them, don't generate them.
- No daemons or services — No background processes, servers, or persistent runtimes
- No modifications to existing squad files beyond the decision entry (no changes to team.md, routing.md, agent charters, etc.)
Your role: Configure the mesh topology and install the bundled sync scripts. Nothing more.
Context
When squads are on different machines (developer laptops, CI runners, cloud VMs, partner orgs), the local file-reading convention still works — but remote files need to arrive on your disk first. This skill teaches the pattern for distributed squad communication.
When this applies:
- Squads span multiple machines, VMs, or CI runners
- Squads span organizations or companies
- An agent needs context from a squad whose files aren't on the local filesystem
When this does NOT apply:
- All squads are on the same machine (just read the files directly)
Patterns
The Core Principle
"The filesystem is the mesh, and git is how the mesh crosses machine boundaries."
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 288 lines · 16 tokens per session scan A a9e91ed408df
distributed-mesh is a skill published in the GitHub repository alonf/MCPPythonDemo (0 stars, last pushed 4mo ago), licensed MIT. It adds 16 tokens to every session and 3,069 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 97% identical to distributed-mesh, differing in 574 lines, and is treated as a copy.
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