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BB investigating-phishing-email-incident

Investigates phishing email incidents from initial user report through header analysis, URL/attachment detonation, impacted user identification, and containment actions using SOC tools like Splunk, Microsoft Defender, and sandbox analysis platforms. Use when a reported phishing email requires full incident investigation to determine scope and impact.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 2 482 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Investigates phishing email incidents from initial user report through header analysis, URL/attachment detonation, impacted user identification, and…

As a process B 68/100 · Nearly there — weak spots: failures and branches

AnalyzerAI and agentstype 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
B
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
82
Run on models
none yet
Process rating
B
68/100
Nearly there
Failures and branches w 10
0
When it triggers w 12
50
Tools and files w 18
60
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Risky intent intent-offensive-security SKILL.md:75
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - Automated detection identifies credential harvesting URLs or malicious attachments
      detector
    • low Risky intent intent-offensive-security SKILL.md:76
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - A phishing campaign targeting the organization requires scope assessment
    • low Risky intent intent-offensive-security SKILL.md:276
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      disposition="True Positive - Credential Phishing Campaign"
      detector
    • low Risky intent intent-offensive-security SKILL.md:288
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | **Credential Harvesting** | Phishing technique using fake login pages to capture username/password combinations |
    • low Risky intent intent-offensive-security SKILL.md:316
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Payload:      Credential harvesting URL

    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 "mitre_attack"
    • note frontmatter-key unknown frontmatter key "mitre_f3"
    • note frontmatter-key unknown frontmatter key "nist_csf"

    Process rating: all ten parameters 68/100

    • 0Failures and branches. Linear process with no failure handling
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 26 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2482 tokens
    • 100Running it twice. Mutating operations check current state
    • 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 352: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 26 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.