SKILLEMALL.ai

AC gstack-openclaw-diagnose

Structured diagnosis for hard bugs and performance regressions. Builds a deterministic feedback loop FIRST, then reproduces, hypothesises (3-5 ranked), instruments, fixes with regression test, cleans up. Use when: bug resists a quick fix, flaky failure, performance regression, user has tried 2+ things, or user says diagnose.

ClawHub Agent Skills author: ilmych v1.0.0 MIT-0 2 files body ≈ 1 289 tokens Open the sourceclawhub.ai analyzed 23 h ago

Structured diagnosis for hard bugs and performance regressions.

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

AnalyzerData and analyticsResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 100Steps. 32 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1289 tokens
    • 100Progress reporting. Reports progress
    • low The skill ranks results itself: that belongs to the system behind the tool, not the model

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 326: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 32 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: clean
    This skill is a plain debugging workflow guide with no executable payload or hidden behavior found.
    LLM: benign (high) · VirusTotal: · 28 May 2026