SKILLEMALL.ai
GitHub ↗ RU Sign in

AC acpx

Multi-agent collaboration and task delegation via the Agent Client Protocol (ACP) using acpx. Form agent teams from Claude Code, Codex, OpenCode, Gemini, Cursor, Copilot, and other ACP-compatible agents. Run parallel workstreams, switch agent modes, orchestrate deliberation and consensus, or delegate coding tasks to another agent. Triggers include "delegate to Claude", "use Claude Code", "ask Claude to", "parallel agents", "acpx", "ACP", "agent delegation", "form a team", "council", "multi-agent", "debate", "consensus", "code review team", "security audit", "have Claude/Codex/Gemini review/implement/fix", or any request involving multiple AI agents collaborating.

ClawHub Agent Skills author: Wang Lei v1.0.0 MIT-0 4 files body ≈ 2 256 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: when it triggers, failures and branches, consistency

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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
C
62/100
Has gaps
Failures and branches w 10
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: 4. 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 62/100

    • 0Failures and branches. Linear process with no failure handling
    • 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
    • 40Consistency. Frontmatter name (acpx) differs from the folder (acpx-team)
    • 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. 9 steps
    • 100Execution cost. Instruction body is 2256 tokens
    • 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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 13 example trigger phrases
    • +3Description length 671: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 9 items
    • +3Output format is stated explicitly
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: suspicious
    This is a legitimate multi-agent delegation guide, but it needs review because it normalizes approval-bypassing agent modes and broad sharing with external AI tools without enough safeguards.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026