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BC opennexum

Contract-driven multi-agent orchestration with ACP. Contract sync, webhook + dispatch-queue dual dispatch, cross-review, auto-retry, batch progress tracking.

ClawHub Agent Skills author: ayao99315 v2.1.4 MIT-0 79 files body ≈ 606 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsInfrastructuretype 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
B
86/100
safety, quality, tests
Safety 60%
90
Quality 40%
79
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 10

    ✓ No critical or high findings

    Medium and low: 10
    • low Secrets in code secret-high-entropy-token packages/prompts/pnpm-lock.yaml:66
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…QYA+ISs0/2l3T9/kj42…aQT/dfNXWX/ZZCQ==}
    • low Secrets in code secret-high-entropy-token packages/prompts/pnpm-lock.yaml:138
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha512-6+gjmF…uh8+uw3mnrvgs+dSPQ…dZG+D4garKg==}
    • low Secrets in code secret-high-entropy-token packages/prompts/pnpm-lock.yaml:144
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…DUr+vOv8…A8A==}
    • low Secrets in code secret-high-entropy-token packages/prompts/pnpm-lock.yaml:184
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…wPB+3FuG…pcq+FstO…nTw==}
    • low Secrets in code secret-high-entropy-token packages/prompts/pnpm-lock.yaml:241
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…Nvd+qJlO…bk1/PSIV+80/pxLg==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:122
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…QYA+ISs0/2l3T9/kj42…aQT/dfNXWX/ZZCQ==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:194
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha512-6+gjmF…uh8+uw3mnrvgs+dSPQ…dZG+D4garKg==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:200
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…DUr+vOv8…A8A==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:240
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…wPB+3FuG…pcq+FstO…nTw==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:297
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…Nvd+qJlO…bk1/PSIV+80/pxLg==}

    Files scanned: 79. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "requires"

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 85Steps. 15 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 606 tokens
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 157: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

    External checks

    ClawHub: suspicious
    OpenNexum appears to be a real multi-agent orchestration skill, but it gives agents durable project-control instructions and default commit-and-push behavior that can publish code without a clear human review gate.
    LLM: suspicious (high) · VirusTotal: · 28 May 2026