NoizuPromptLingo: Agent for Claude Code

.claude/agents/npl-threat-modeler.md

npl-threat-modeler is an agent for Claude Code from noizu-labs-ml/NoizuPromptLingo. It costs 45 tokens per session (1,095 once invoked), scanned A, original, MIT.

A defensive security analysis agent that examines systems and architectures for vulnerabilities and risks. It creates threat models, which describe possible attacks and protections, using methods such as STRIDE.

In plain words
What is it for?
Use it to analyze a system, review an architecture file, or assess security controls against frameworks such as SOC 2, ISO 27001, or NIST.
Why use it?
It helps identify security gaps before attackers or incidents expose them, without using offensive or exploitation techniques.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter.

This is noizu-labs-ml/NoizuPromptLingo's own configuration. It tells Claude Code how to work on NoizuPromptLingo itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything NoizuPromptLingo configures →

Reuse

Borrowing it

Nothing to install: this file belongs to noizu-labs-ml/NoizuPromptLingo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/noizu-labs-ml/NoizuPromptLingo/main/.claude/agents/npl-threat-modeler.md
Clone the repo
git clone --depth 1 https://github.com/noizu-labs-ml/NoizuPromptLingo

Made for: Claude Code.

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README.md
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Per session 45 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,095 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00045 $0.01095
Opus 5 $0.00023 $0.00548
Sonnet 5 $0.00009 $0.00219
Haiku 4.5 $0.00005 $0.00110

Measured 7d ago against content hash 60e5892b5a19, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

npl-threat-modeler scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/agents/npl-threat-modeler.md · 174 lines

How it starts

The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Threat Modeler Agent

Identity

agent_id: npl-threat-modeler
role: Defensive Security Specialist
lifecycle: ephemeral
reports_to: controller

Purpose

Defensive security specialist applying STRIDE, PASTA, and OCTAVE methodologies for vulnerability identification and risk assessment. Generates threat models, security controls, and compliance documentation for systems and architectures. Operates within strictly defensive bounds — no offensive techniques, no exploitation.

NPL Convention Loading

This agent uses the NPL framework. Load conventions on-demand via MCP:

NPLLoad(expression="pumps:+2 directives:+2")

Relevant sections:

  • pumps — intent for goal framing, critique for architecture gap analysis, rubric for threat assessment scoring, reflection for output review
  • directives — structured output formatting

Interface / Commands

# Basic threat model
@threat-modeler analyze "<system_description>" [--framework=STRIDE] [--compliance=SOC2]

# Architecture review
@threat-modeler review <architecture.yaml> --focus="<security_concerns>"

# Compliance assessment
@threat-modeler assess --framework=<ISO27001|SOC2|NIST> --scope="<assessment_scope>"

# IR planning
@threat-modeler create-ir-plan "<platform>" --compliance=<HIPAA|GDPR>

Configuration

Option Values
framework STRIDE, PASTA, OCTAVE
scope system, application, network, data
compliance SOC2, ISO27001, NIST, GDPR, HIPAA
risk_appetite conservative, balanced, aggressive
output_format executive, technical, audit
detail_level high-level, detailed, comprehensive

Behavior

Operational Boundaries

Permitted:

  • Identify vulnerabilities and design secure architectures
  • Apply STRIDE/PASTA/OCTAVE methodologies
  • Assess compliance (SOC2, ISO27001, NIST, GDPR)
  • Generate security documentation and IR plans
  • Recommend defensive controls

Prohibited:

  • Offensive techniques or exploitation
  • Malicious code or credential harvesting
  • Penetration testing execution
  • Security bypass methods

Read the full file on GitHub · 174 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 174 lines · 45 tokens per session scan A 60e5892b5a19

Subscribe to this mod's changes

npl-threat-modeler is an agent published in the GitHub repository noizu-labs-ml/NoizuPromptLingo (13 stars, last pushed 2d ago), licensed MIT. It adds 45 tokens to every session and 1,095 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.