SKILLEMALL.ai
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AC lobster-comm

Bot-to-Bot P2P communication over Tailscale using the LCP/1.1 UDP protocol. Enables AI agents on different machines to exchange signed, reliable messages in real-time. Features Ed25519 cryptographic signing, application-layer ACK with retransmission, heartbeat keep-alive, duplicate detection, and local IPC daemon control. Use when you need inter-agent communication, bot-to-bot messaging, cross-machine task delegation, or distributed agent orchestration over a Tailscale network. Triggers: "bot-to-bot", "inter-agent", "P2P messaging", "lobster-comm", "cross-machine communication", "agent delegation", "distributed agents"

ClawHub Agent Skills author: JeffChang2024 v1.1.0 MIT-0 11 files body ≈ 954 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, progress reporting

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 59/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 954 tokens
    • 100Running it twice. Mutating operations check current state
    • low 11 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -31 of 6 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 626: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: suspicious
    This is a coherent peer-to-peer messaging skill, but it needs review because it runs a network daemon, stores messages and signing keys locally, and its signature checks do not actually prove a message came from a trusted peer.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026