SKILLEMALL.ai

AB alibabacloud-livedebug

Live-Debug runtime diagnostics: dynamic logging, method snapshots, dynamic metrics, dynamic spans, and JVM inspection. Use for live-debug ServiceTask on Alibaba Cloud CMS (aliyun cms2 apm service-task): dynamic log/snapshot/metric/span probes, Java JVM commands (OGNL evaluate, decompile, thread info, memory info, inspect object, search type/method, runtime info), disable/clear probes, and query capture results via SLS. Java supports commands + LOG/SNAPSHOT probes; Python supports LOG/SNAPSHOT/METRIC/SPAN/SPAN_TAG probes only. Synonyms: live debug, service task, probe, dynamic logging, method snapshot, take a snapshot, inspect running JVM, runtime diagnostics. Do NOT use for APM/agent onboarding, CMS alerts, RUM, Prometheus rules, or billing.

ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 11 files · 7 scripts body ≈ 2 445 tokens Open the sourceclawhub.ai analyzed 3 d ago

Live-Debug runtime diagnostics: dynamic logging, method snapshots, dynamic metrics, dynamic spans, and JVM inspection.

As a process B 67/100 · Nearly there — weak spots: result and completion, running it twice

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
When it triggers w 12
50
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: 11. 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 67/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 11 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 30 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2445 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 751: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 30 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    The skill matches its live-debugging purpose, but it needs Review because it can add probes to running Alibaba Cloud services and collect broad runtime data without enough safety prompts.
    LLM: suspicious (high) · 7 Aug 2026