SKILLEMALL.ai
GitHub ↗ RU Sign in

CC implementing-llm-guardrails-for-security

Implements input/output validation guardrails for LLM applications using NVIDIA NeMo Guardrails (Colang), custom Python validators for PII detection, and the Guardrails AI framework, intercepting user inputs (prompt injection, PII, off-topic queries) and model outputs (hallucinations, toxic content, schema compliance). Use when adding safety controls to an LLM app/chatbot/RAG pipeline or validating outputs conform to expected schemas.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 1 949 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Implements input/output validation guardrails for LLM applications using NVIDIA NeMo Guardrails (Colang), custom Python validators for PII detection, and the…

As a process C 55/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

AnalyzerAI and agentstype 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
88/100
safety, quality, tests
Safety 60%
90
Quality 40%
85
Run on models
none yet
Process rating
C
55/100
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

What is at stake

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

Instruction override 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 text contains phrases like "ignore previous instructions" or "you are now…". That is an attempt to hijack the agent: it may break your rules, the system limits or company policy.

For the author

An honest skill does not need them: state the role and the rules directly without overriding other instructions. Otherwise catalog scanners and corporate filters will block the listing.

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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • medium Instruction override en-fake-system-prompt references/api-reference.md:73
      Fake system prompt injected into content (documentation of a security skill)
      - System prompt override ("ignore/disregard/forget previous instructions")
      security skill
    • low Secrets in code secret-aws-key references/api-reference.md:125
      AWS access key ID (placeholder value)
      | `AWS_ACCESS_KEY` | AKIA…PLE | [AWS_KEY_REDACTED] |
      placeholder

    A further 4 matches are quotations in this security skill's documentation and are not counted as findings.

    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 "atlas_techniques"
    • note frontmatter-key unknown frontmatter key "nist_ai_rmf"
    • note frontmatter-key unknown frontmatter key "d3fend_techniques"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 55/100

    • 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
    • 30Running it twice. 4 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
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1949 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
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 438: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (7 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.