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BC detecting-attacks-on-historian-servers

Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement indicators as historians pivot between IT and OT networks. Use when monitoring historians bridging IT/OT zones for compromise, investigating historian-specific CVE exploitation, or validating historian data integrity after a suspected OT incident.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 2 789 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries…

As a process C 63/100 · Has gaps — weak spots: failures and branches, running it twice, progress reporting

AnalyzerSecurityData 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
B
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
82
Run on models
none yet
Process rating
C
63/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Risky intent intent-offensive-security SKILL.md:44
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - When investigating lateral movement through historian servers between IT and OT zones
    • low Risky intent intent-offensive-security SKILL.md:67
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      lateral movement indicators, and exploitation of historian-specific
    • low Risky intent intent-offensive-security SKILL.md:209
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
      "description": "Attackers may install tools on historian for lateral movement",
      quoted
    • low Risky intent intent-offensive-security SKILL.md:240
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
      print(f"\n--- LATERAL MOVEMENT CHECKS ---")
      quoted
    • low Risky intent intent-offensive-security SKILL.md:293
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      LATERAL MOVEMENT INDICATORS:

    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 63/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 10 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2789 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

    • +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 442: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 10 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +1License stated

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