SKILLEMALL.ai

AC rdk-x5-media

RDK X5 多媒体处理:音频录制/播放(arecord/aplay/PulseAudio)、hobot_codec 视频编解码、RTSP 拉流/推流、HDMI 分辨率配置、MIPI LCD 触摸屏适配、VNC 远程桌面服务端安装与配置。Use when the user wants to record or play audio, encode/decode video, configure HDMI output, set up LCD screen, use RTSP streaming to view camera on PC, set up VNC desktop server, or stream camera video to remote viewer. Do NOT use for VNC 连接失败排查 (use rdk-x5-network), running AI algorithms (use rdk-x5-ai-detect), camera hardware setup (use rdk-x5-camera), or system backup (use rdk-x5-system).

ClawHub Agent Skills author: qiaolongli v1.0.0 MIT-0 2 files body ≈ 550 tokens Open the sourceclawhub.ai analyzed 5 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerMedia and videoInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
56/100
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 · 0

    ✓ No critical or high findings

    Files scanned: 2. 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 56/100

    • 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
    • 50Steps. 2 steps
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 550 tokens
    • 100Running it twice. No mutating operations

    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
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 514: enough signal without eating the budget
    • +4Structure: 12 headings
    • +4Has examples (9 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: clean
    This appears to be a hardware demo helper with some sensitive camera and credential examples that need care, but no evidence of hidden or malicious behavior.
    LLM: benign (medium) · VirusTotal: · 29 May 2026