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AB five-whys

Use for the 5 Whys, or when the user wants the root cause of a failure: "why did this happen?", "this keeps breaking", "write the postmortem", "find the real cause". Asks why repeatedly with evidence at each step, then sets countermeasures. Skip for one-off typos or when the cause is already proven.

ysskrishna/ai-agent-skills Agent Skills author: ysskrishna 2 files body ≈ 794 tokens Open the sourcegithub.com↗ analyzed 15 h ago

Use for the 5 Whys, or when the user wants the root cause of a failure: "why did this happen?", "this keeps breaking", "write the postmortem", "find the real…

As a process B 74/100 · Nearly there — weak spots: result and completion, running it twice

ProcedureInfrastructureData and analyticsOperations and projectstype 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
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
74/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
When it triggers w 12
70
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 74/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 2 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 23 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 794 tokens
    • 100Progress reporting. Reports progress

    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)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 300: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.