SKILLEMALL.ai

BC Cyber Security Roadmap

Generates personalized cybersecurity learning paths based on experience level, goals, and learning preferences.

ClawHub Agent Skills author: ToolWeb v1.0.0 MIT-0 3 files body ≈ 2 041 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

GeneratorOperations and projectsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
95
Quality 40%
64
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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:32
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    "penetration testing",
    quoted
  • low Risky intent intent-offensive-security SKILL.md:57
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    "specialization": "penetration testing",
    quoted
  • low Risky intent intent-offensive-security SKILL.md:75
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    "title": "Penetration Testing Essentials",
    quoted
  • low Risky intent intent-offensive-security SKILL.md:98
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    "web application pentesting",
    quoted
  • low Risky intent intent-offensive-security SKILL.md:102
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    "OSCP (Offensive Security Certified Professional)"
    quoted

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 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. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (Cyber Security Roadmap) differs from the folder (toolweb-cybersec-roadmap-v2)
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 25 steps
  • 100Execution cost. Instruction body is 2041 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 111: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 25 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
This skill is a documented external API for generating cybersecurity learning roadmaps, with manageable privacy considerations around the learner details and identifiers it asks users to send.
LLM: benign (high) · VirusTotal: · 29 May 2026