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BB root-cause-pareto

Build a decision-grade Pareto for downtime, defects, complaints or delays - with unit-of-measure discipline, category hygiene, exposure normalization and a follow-up metric. Use when the user mentions pareto analysis, kök neden, duruş analizi, downtime, arıza analizi, defect ranking, şikayet analizi, 80/20 analysis. Differentiator - treats the Pareto as the start of a causal investigation, not a bar chart deliverable.

davila7/claude-code-templates Agent Skills author: davila7 MIT 1 file body ≈ 674 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Build a decision-grade Pareto for downtime, defects, complaints or delays - with unit-of-measure discipline, category hygiene, exposure normalization and a…

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, failures and branches, progress reporting

GeneratorData and analyticsCustomer supporttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
B
68/100
Nearly there
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: 1. 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 68/100

    • 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
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 674 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 421: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 19 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.