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Orchestrate continuous security validation, threat modeling, vulnerability management, and security gate enforcement across SDLC phases

jmagly/aiwg Agent Skills author: jmagly 1 file body ≈ 2 835 tokens Open the sourcegithub.com↗ analyzed 9 h ago

Orchestrate continuous security validation, threat modeling, vulnerability management, and security gate enforcement across SDLC phases

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

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
C
85/100
safety, quality, tests
Safety 60%
95
Quality 40%
70
Run on models
none yet
Process rating
C
61/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: flow-security-review-cycle (jmagly/aiwg)

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:59
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - Security testing during Construction (SAST, DAST, penetration testing)
  • low Risky intent intent-offensive-security SKILL.md:113
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    Run automated security scans and coordinate manual penetration testing.
    detector
  • low Risky intent intent-offensive-security SKILL.md:121
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    6. **Penetration Testing** - Manual security testing
  • low Risky intent intent-offensive-security SKILL.md:137
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    Conduct manual penetration testing:
  • low Risky intent intent-offensive-security SKILL.md:142
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - Attempt privilege escalation

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "namespace"
  • note frontmatter-key unknown frontmatter key "commandHint"

Process rating: all ten parameters 61/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 100 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2835 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill

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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 135: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 100 items
  • +4Has examples (8 code blocks)

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