SKILLEMALL.ai

BC security-scan

Scan your Claude Code configuration (.claude/ directory) for security vulnerabilities, misconfigurations, and injection risks using AgentShield. Checks CLAUDE.md, settings.json, MCP servers, hooks, and agent definitions. Use when auditing a .claude/ directory — CLAUDE.md, settings.json, MCP servers, hooks, or agent definitions.

The skillemall take

This skill scans Claude Code configuration for vulnerabilities using AgentShield—checks CLAUDE.md, settings.json, MCP servers, hooks, and agent definitions. Promises to catch injection attacks and misconfigurations.

Verification shows no critical or high-risk findings, one medium or low issue. Safety score 99, quality 87, but process score only 57—the checking logic lacks confidence. Never ran on actual models. Single file, no scripts, 1046 tokens total.

Works for quick config audits. If you need serious injection-attack protection in production, the process score is a red flag. Install if you're scanning your own configs for compliance, skip if you expect reliable threat detection.

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 1 file body ≈ 1 046 tokens Open the sourcegithub.com↗ analyzed 23 h ago

Scan your Claude Code configuration (.claude/ directory) for security vulnerabilities, misconfigurations, and injection risks using AgentShield. Checks…

As a process C 57/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

AnalyzerGitHubAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
Run on models
none yet
Process rating
C
57/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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 SKILL.md:102
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      1. **Attacker (Red Team)** — finds attack vectors

    Files scanned: 1. 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 57/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 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
    • 85Steps. 28 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1046 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 329: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 28 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.