SKILLEMALL.ai

AC claude-code-bridge

Bridges OpenClaw (QQ, Telegram, WeChat, and other messaging channels) to a persistent Claude Code CLI session running in a background tmux process. Enables starting, stopping, restarting, and monitoring Claude Code sessions directly from any chat interface. Supports specifying a working directory or launching in sandbox mode (temp directory, auto-cleanup on stop). Automatically detects session state on every message, routes user input to the active Claude Code session, and handles tool-approval prompts so the user can approve or deny Claude Code actions without leaving their chat app. Trigger phrases: "start claude code", "open claude code", "cc status", "stop claude code", "restart cc", "启动claude code", "开启claude code", "启动cc", "开启cc", "连接cc", "cc状态", "关闭cc", "退出cc", "重启cc", "在...打开cc", "沙盒打开cc", "沙盒模式启动cc", "/cc start", "/cc stop", "/cc restart", "/cc status".

ClawHub Agent Skills author: Linghaoz v1.0.0 MIT-0 5 files · 1 script body ≈ 1 592 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationTelegramSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Inputs and preconditions w 11
30
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: 5. 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 63/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 28 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 25 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1592 tokens
    • low 10 top-level sections: this looks like several domains in one skill
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 874: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 14 example trigger phrases
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    The skill appears to do what it claims, but it gives chat conversations persistent remote control over a local Claude Code terminal and needs careful review before use.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026