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AB moltflow-a2a

Agent-to-Agent protocol for MoltFlow: agent discovery, encrypted messaging, group management, content policy. Use when: a2a, agent card, agent message, encrypted, content policy, agent discovery.

LeoYeAI/openclaw-master-skills Claude Code author: LeoYeAI MIT 1 file body ≈ 3 153 tokens Open the sourcegithub.com↗ analyzed 29 h ago

Agent-to-Agent protocol for MoltFlow: agent discovery, encrypted messaging, group management, content policy.

As a process B 72/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting

IntegrationWhatsAppAI 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
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
84
Run on models
none yet
Process rating
B
72/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: openclaw-master-skills, openclaw-master-skills

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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token SKILL.md:97
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "public_key": "base…key"
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:121
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      {"public_key": "base…key", "algorithm": "X25519", "created_at": "2026…00Z"}
      quoted

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "requiredEnv"
    • note frontmatter-key unknown frontmatter key "primaryEnv"

    Process rating: all ten parameters 72/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 23 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3153 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 14 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 195: enough signal without eating the budget
    • +4Structure: 34 headings
    • +3Step-by-step instructions: 17 items
    • +3Output format is stated explicitly
    • +4Has examples (20 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.