SKILLEMALL.ai
GitHub ↗ RU Sign in

AB cyber-security-engineer

Security engineering workflow for OpenClaw privilege governance and hardening. Use for least-privilege execution, approval-first privileged actions, idle timeout controls, port + egress monitoring, and ISO 27001/NIST-aligned compliance reporting with mitigations.

modbender/skill-library-mcp Agent Skills author: modbender MIT 26 files · 2 scripts body ≈ 879 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Security engineering workflow for OpenClaw privilege governance and hardening.

As a process B 65/100 · Nearly there — weak spots: failures and branches, running it twice

ProcedureData and analyticsSecurityInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
84
Run on models
none yet
Process rating
B
65/100
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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
    • medium Dangerous commands cmd-persistence scripts/install-openclaw-runtime-hook.sh:62
      Persistence mechanism (cron / launchd / scheduled task / autorun registry) (string literal in code, not executed)
      PLIST="${HOME}/Library/LaunchAgents/ai.openclaw.gateway.plist"
      code literal

    Files scanned: 26. 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 65/100

    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 53 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 879 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • -31 of 14 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 263: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 53 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.