SKILLEMALL.ai

AC log-to-incident-report

Use when (1) user provides error logs and needs structured incident report with root cause. (2) impact. (3) and fix steps.

ClawHub Agent Skills author: 王继鹏 v1.0.0 MIT-0 8 files body ≈ 1 069 tokens Open the sourceclawhub.ai analyzed 2 d ago

(2) impact. (3) and fix steps.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
51/100
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: 7. 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 51/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (log-to-incident-report) differs from the folder (wangjipeng-log-to-incident-report)
    • 100Tools and files. No external tools needed
    • 100Steps. 49 steps
    • 100Execution cost. Instruction body is 1069 tokens
    • 100Running it twice. No mutating operations
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 122: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 49 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

    External checks

    ClawHub: clean
    This skill is a straightforward log-to-incident-report assistant, but users should redact sensitive log data before using it.
    LLM: benign (medium) · VirusTotal: · 29 May 2026