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AC moltcomm

Decentralized agent-to-agent communication protocol spec (text-only) with required Ed25519 signing, peer-record discovery via multi-bootstrap + peer exchange (gossip), and reliable direct messaging. Use to implement MoltComm in any language, write a local SKILL_IMPL.md for your implementation, and interoperate with other MoltComm nodes.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files body ≈ 1 099 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Decentralized agent-to-agent communication protocol spec (text-only) with required Ed25519 signing, peer-record discovery via multi-bootstrap + peer exchange…

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureAI 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
93/100
safety, quality, tests
Safety 60%
97
Quality 40%
88
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Obfuscation obf-base64-blob references/WIRE_FORMAT.md:107
      Long base64-looking blob (detector / deny-list definition)
      - `SaQJ…0Ag==`
      detector
    • low Obfuscation obf-base64-blob references/WIRE_FORMAT.md:131
      Long base64-looking blob (quoted — discussed, not commanded)
      "sig": "/DBBQ…UOn+T2cF…HKG+y2ACw=="
      quoted

    A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

    Files scanned: 9. 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 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Steps. 33 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1099 tokens
    • 100Running it twice. Mutating operations check current state

    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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 338: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 33 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (7 of 8)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.