SKILLEMALL.ai

AC pc-builder-assistant

Use for budget desktop PC build planning and recommendations, PC hardware DIY, upgrades, configuration completion, compatibility checks, hardware guidance, local LLM GPU/VRAM/RAM sizing, and gaming, streaming, creator, aesthetic, compact or ITX builds. Bundled prices are China-market CNY references; explicit user overlays keep currencies separate. 中文支持预算装机、装机 DIY、整机推荐、旧机升级、配置补全、搭配或兼容检查、硬件问答,以及游戏直播、生产力、本地 AI、外观海景房和紧凑或 ITX 主机。 Do not use for laptops, server procurement, ordering or payment, remote control, security isolation, or standalone software, game or agent tutorials.

ClawHub Agent Skills author: gongyu0918 v0.1.32 MIT-0 34 files body ≈ 595 tokens Open the sourceclawhub.ai analyzed 21 h ago

Use for budget desktop PC build planning and recommendations, PC hardware DIY, upgrades, configuration completion, compatibility checks, hardware guidance…

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

GeneratorAI and agentsLearningtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
86
Run on models
none yet
Process rating
C
55/100
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token skill-card.md:62
      High-entropy token-like string (may be an id, hash or a credential)
      - [Deep…32B model card](https://huggingface.co/deepseek-ai/Deep…32B)

    Files scanned: 34. 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 55/100

    • 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
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 595 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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • -37 of 12 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 578: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 17 items
    • +4Reference files are cited in the instructions (13 of 13)
    • +1License stated

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

    External checks

    ClawHub: clean
    The skill is a disclosed PC-building advisor with local catalog scripts; it has data-quality caveats but no hidden credential access, persistence, or unsafe execution.
    LLM: benign (high) · VirusTotal: · 11 Sept 2026