SKILLEMALL.ai

AC debugging-log-analyser

Parse error logs, stack traces, and crash reports into a structured root cause diagnosis. Use when an application is throwing exceptions, crashing, or producing unexpected errors and you need to understand why and what to fix. Produces a structured diagnosis with error classification, stack trace walkthrough, probable root cause with confidence level, affected code path, a concrete code-level fix suggestion, and ordered next debugging steps.

ClawHub Agent Skills author: mohitagw15856 v1.0.0 MIT-0 2 files body ≈ 956 tokens Open the sourceclawhub.ai analyzed 23 h ago

Parse error logs, stack traces, and crash reports into a structured root cause diagnosis.

As a process C 62/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
62/100
Has gaps
Failures and branches w 10
0
When it triggers w 12
20
Running it twice w 4
30
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

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 62/100

    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 38 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 956 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

    • +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 445: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 38 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: clean
    This skill is a straightforward debugging-log analysis prompt with no executable code, persistence, credential handling, or hidden data movement.
    LLM: benign (high) · VirusTotal: · 16 Jul 2026