SKILLEMALL.ai

AC alibabacloud-pcap-analyzer

Analyze local pcap/pcapng captures to diagnose network transfer problems. Use when given a pcap file to explain slow network transfer, connection issues, abrupt TCP session termination, security association setup error, private network tunnel establishment problem, DNS, TLS or encrypted session establishment error, MTU or oversized packet drop, or receiver buffer exhaustion. Read-only; no credentials required. Triggers: "pcap analysis", "packet capture analysis", "analyze pcap file", "slow network transfer", "TCP retransmission", "connection reset", "IPsec/IKE negotiation failed", "VPN negotiation failure", "TLS handshake failed", "DNS resolution failure", "MTU issue", "zero window", "abrupt TCP session termination", "domain name lookup error", "encrypted session establishment error", "oversized packet drop".

ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 7 files body ≈ 1 952 tokens Open the sourceclawhub.ai analyzed 3 d ago

Analyze local pcap/pcapng captures to diagnose network transfer problems.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSales and CRMInfrastructuretype 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
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 15 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1952 tokens

    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)
    • +3Description length 820: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 16 example trigger phrases
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 1 scripts are documented

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

    External checks

    ClawHub: clean
    This skill is a local packet-capture analyzer with expected, disclosed file reading and optional report writing, but users should choose output paths carefully.
    LLM: benign (high) · VirusTotal: · 31 Aug 2026