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CB correlating-security-events-in-qradar

Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 2 339 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage…

As a process B 72/100 · Nearly there — weak spots: failures and branches, running it twice

ProcedureInfrastructureData and analyticstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
94
Quality 40%
82
Run on models
none yet
Process rating
B
72/100
Nearly there
Failures and branches w 10
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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • low Exfiltration net-credential-use references/api-reference.md:40
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s "https://qradar/api/siem/offenses?filter=…" -H "SEC: $TOKEN"
      security skill
    • low Exfiltration net-credential-use references/api-reference.md:43
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s "https://qradar/api/siem/offenses/12345" -H "SEC: $TOKEN"
      security skill
    • low Exfiltration net-credential-use references/api-reference.md:68
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s "https://qradar/api/reference_data/sets/Watchlist_IPs" -H "SEC: $TOKEN"
      security skill
    • low Risky intent intent-offensive-security SKILL.md:117
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      Correlate authentication failures with network flows to detect lateral movement:
      detector
    • low Risky intent intent-offensive-security SKILL.md:267
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Lateral Movement Chain**: Track authentication events across multiple internal hosts from a single source
    • low Risky intent intent-offensive-security SKILL.md:269
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Privilege Escalation**: Correlate user account changes (group additions) with prior suspicious authentication

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 72/100

    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2339 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
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 356: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 22 items
    • +3Output format is stated explicitly
    • +4Has examples (11 code blocks)
    • +1License stated

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