SKILLEMALL.ai

AC diagnose-hardware-root-causes-rd

Diagnose hardware performance shortfalls with measurable problem definition, first-principles models, equation-led decomposition, 5M1E coverage, multidisciplinary review and evidence-ranked causal trees. Use for root-cause analysis, causal diagrams, out-of-spec parameters, 5 Whys, engineering contradictions or hardware troubleshooting.

ClawHub Agent Skills author: yuanzhian-patsnap v1.0.0 MIT-0 3 files body ≈ 3 375 tokens Open the sourceclawhub.ai analyzed 3 d ago

Diagnose hardware performance shortfalls with measurable problem definition, first-principles models, equation-led decomposition, 5M1E coverage…

As a process C 61/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerAI 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
61/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "copyright"

    Process rating: all ten parameters 61/100

    • 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
    • 30Running it twice. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 100Steps. 70 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3375 tokens
    • low 13 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 337: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 70 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)
    • +3All 1 scripts are documented

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

    External checks

    ClawHub: clean
    This skill provides a disclosed hardware root-cause workflow and a local PNG exporter without hidden network, credential, persistence, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 13 Aug 2026