SKILLEMALL.ai
GitHub ↗ RU Sign in

AB network-log-analysis

Device-level network log analysis using raw syslog data without SIEM platforms. Guides forensic timeline construction from rsyslog/syslog-ng collectors, device console logs, and SNMP trap data. Covers syslog pattern recognition across Cisco IOS-XE, Juniper JunOS, and Arista EOS message formats, multi-device event correlation using grep/awk/sort, anomaly detection via baseline deviation, and chronological timeline reconstruction with NTP-aware timestamp normalization.

ClawHub Agent Skills author: Vahagn Madatyan v1.0.0 MIT-0 4 files body ≈ 4 768 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 69/100 · Nearly there — weak spots: result and completion, when it triggers, running it twice

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
89
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security references/syslog-patterns.md:172
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Privilege escalation | `PRIV…NGE` | `UI_AUTH_EVENT` | `%AAA-…BLE` |

    Files scanned: 4. 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 69/100

    • 0Result and completion. Does not say what the result is
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4768 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 25 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 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
    • +2Single-language instructions
    • +3Description length 471: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (7 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: 89.

    External checks

    ClawHub: clean
    This skill is a Markdown-only guide for analyzing network syslog data, with expected sensitivity around infrastructure logs and no hidden execution or data exfiltration behavior found.
    LLM: benign (high) · VirusTotal: · 29 May 2026