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Agent skill

Threat Modeling

by seb1n in seb1n/awesome-ai-agent-skills

Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited.

MITAuto-check passedSecurity

Install Threat Modeling

skills CLI
$ npx skills add seb1n/awesome-ai-agent-skills --skill threat-modeling -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install seb1n/awesome-ai-agent-skills threat-modeling --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/security/threat-modeling .claude/skills/threat-modeling && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
threat-modeling
GitHub stars
206
Token cost
~3.1k tokens
SKILL.md length
1,354 words
Files
1
Skills in repo
101
Repo updated
First seen
Licence
MIT

At a glance

Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited.

  • Works in 6 steps: Decompose the System Architecture —… → Select a Threat Modeling Methodology —… → Enumerate Threats — Apply the selected… → …
  • The user requests threat modeling
  • SKILL.md covers Workflow, Supported Technologies, Usage and Examples, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Threat Modeling is an agent skill from seb1n/awesome-ai-agent-skills. Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited. Use when the user requests threat modeling or provides relevant inputs for this workflow.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Security, covering Threat modeling. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.

When your agent uses it

  • The user requests threat modeling
  • Provides relevant inputs for this workflow

Example prompts

  • “/threat-modeling”

Requirements

  • Docker

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Decompose the System Architecture — Analyze architecture diagrams, code repositories, infrastructure-as-code files, and deployment…
  2. Select a Threat Modeling Methodology — Choose the appropriate methodology based on the project's needs. Use STRIDE for systematic…
  3. Enumerate Threats — Apply the selected methodology to each component and data flow in the DFD. For STRIDE, evaluate each element against…
  4. Assess Risk and Prioritize — Score each threat using DREAD (Damage, Reproducibility, Exploitability, Affected Users, Discoverability) or a…
  5. Define Mitigations and Security Controls — For each high and medium priority threat, specify concrete mitigation strategies: architectural…
  6. Document and Maintain the Threat Model — Produce a living document that captures the DFD, threat register, risk scores, and mitigation…

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Threat Modeling loads about 3.1k tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 1,354 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 1,354 words, ~3,084 tokens.

Download SKILL.mdSave it as .claude/skills/threat-modeling/SKILL.md (or your agent's skills folder).
name
threat-modeling
description
Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited. Use when the user requests threat modeling or provides relevant inputs for this workflow.
license
MIT
metadata.author
awesome-ai-agent-skills
metadata.version
1.0.0

Threat Modeling

This skill enables the agent to perform structured threat modeling for software applications, APIs, and infrastructure. The agent analyzes system architecture, data flows, and trust boundaries to systematically identify potential security threats using established methodologies such as STRIDE, DREAD, PASTA, and attack trees. The output is a prioritized threat register with specific, actionable mitigation strategies that development teams can integrate into their backlog.

Workflow

  1. Decompose the System Architecture — Analyze architecture diagrams, code repositories, infrastructure-as-code files, and deployment configurations to identify all components, data stores, external services, and communication channels. Map trust boundaries between networks, services, and user privilege levels. Produce a data flow diagram (DFD) showing how data moves through the system.

  2. Select a Threat Modeling Methodology — Choose the appropriate methodology based on the project's needs. Use STRIDE for systematic enumeration of threat categories per component. Use DREAD for scoring and prioritizing known threats. Use PASTA (Process for Attack Simulation and Threat Analysis) for risk-centric analysis aligned with business objectives. Use attack trees for deep analysis of specific high-value targets like authentication or payment systems.

  3. Enumerate Threats — Apply the selected methodology to each component and data flow in the DFD. For STRIDE, evaluate each element against all six threat categories: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. Document each threat with a unique identifier, description, affected component, and the trust boundary it crosses.

  4. Assess Risk and Prioritize — Score each threat using DREAD (Damage, Reproducibility, Exploitability, Affected Users, Discoverability) or a similar quantitative framework. Combine the score with business context — a threat to the payment service is higher priority than the same threat to an internal admin dashboard. Produce a ranked threat register.

  5. Define Mitigations and Security Controls — For each high and medium priority threat, specify concrete mitigation strategies: architectural changes, code-level fixes, configuration hardening, or operational controls. Map mitigations to security frameworks (NIST 800-53, CIS Controls) where applicable. Estimate implementation effort for each mitigation.

  6. Document and Maintain the Threat Model — Produce a living document that captures the DFD, threat register, risk scores, and mitigation status. Update the threat model whenever the architecture changes, new features are added, or new attack techniques emerge. Integrate threat model reviews into sprint planning and design review processes.

Supported Technologies

  • Methodologies: STRIDE, DREAD, PASTA, Attack Trees, VAST (Visual Agile Simple Threat modeling)
  • Diagramming: Data Flow Diagrams (DFD), Mermaid, draw.io, Microsoft Threat Modeling Tool
  • Architecture Types: Monoliths, microservices, serverless, event-driven, mobile backends, IoT systems
  • Infrastructure: AWS, GCP, Azure, Kubernetes, on-premises hybrid environments
  • Standards Mapping: OWASP Top 10, MITRE ATT&CK, NIST 800-53, CIS Controls

Usage

Provide the agent with access to architecture documentation, source code, infrastructure-as-code files, or a description of the system. Specify the desired methodology and any compliance standards to map against. The agent will produce a complete threat model with a prioritized threat register and mitigation plan.

Prompt example:

Perform a STRIDE threat model on our microservices architecture. The services are defined in /infra/docker-compose.yml and the source code is in /services/. Focus on the API gateway, authentication service, and payment service. Map findings to OWASP Top 10.

Examples

Example 1: STRIDE Analysis for a Microservices E-Commerce Platform

System Components: API Gateway, Auth Service, Product Service, Payment Service, PostgreSQL database, Redis cache, RabbitMQ message broker.

STRIDE Threat Table:

IDComponentSTRIDE CategoryThreat DescriptionRiskOWASPMitigation
T-01API GatewaySpoofingAttacker forges JWT tokens to impersonate usersHighA07:2021Validate JWT signatures using RS256 with key rotation; reject HS256 tokens
T-02API GatewayDenial of ServiceVolumetric attack overwhelms the gateway, blocking legitimate trafficHigh—Implement rate limiting per client IP and API key; deploy behind a CDN with DDoS protection
T-03Auth ServiceSpoofingCredential stuffing using leaked username/password databasesHighA07:2021Enforce MFA, implement rate limiting on /login, integrate breach-detection APIs (HaveIBeenPwned)
T-04Auth ServiceRepudiationUser denies performing a sensitive action (e.g., changing email)MediumA09:2021Log all authentication events and account changes to an immutable audit log with timestamps and source IP
T-05Payment ServiceTamperingAttacker modifies order total in transit between Product Service and Payment ServiceCriticalA04:2021Sign inter-service messages with HMAC; Payment Service re-fetches price from database instead of trusting the request payload
T-06Payment ServiceInformation DisclosureCredit card numbers logged in plaintext to application logsCriticalA02:2021Mask PAN data in all logs; use a PCI-compliant tokenization service; restrict log access
T-07PostgreSQLTamperingSQL injection via Product Service search endpoint alters database recordsHighA03:2021Use parameterized queries exclusively; apply least-privilege database roles per service
T-08RabbitMQInformation DisclosureMessages in transit between services are readable by network attackersMediumA02:2021Enable TLS for all RabbitMQ connections; encrypt sensitive message payloads at the application layer
T-09Redis CacheElevation of PrivilegeUnauthenticated Redis instance allows any service to read/write session dataHighA01:2021Enable Redis AUTH with a strong password; bind to private network interface only; use ACLs to restrict key access per service
Show full SKILL.md (568 more words)Show less
Example 2: Attack Tree for an Authentication System

Root Goal: Gain unauthorized access to a user account.

Gain Unauthorized Access to User Account
├── 1. Steal Valid Credentials
│   ├── 1.1 Phishing attack targeting user email [Likelihood: High]
│   ├── 1.2 Credential stuffing from breached databases [Likelihood: High]
│   └── 1.3 Keylogger malware on user device [Likelihood: Medium]
├── 2. Bypass Authentication
│   ├── 2.1 Exploit password reset flow
│   │   ├── 2.1.1 Predictable reset token (insufficient entropy) [Likelihood: Medium]
│   │   └── 2.1.2 Reset link does not expire [Likelihood: Low]
│   ├── 2.2 Session hijacking
│   │   ├── 2.2.1 Steal session cookie via XSS [Likelihood: Medium]
│   │   └── 2.2.2 Session fixation attack [Likelihood: Low]
│   └── 2.3 Forge or tamper with JWT
│       ├── 2.3.1 Algorithm confusion attack (HS256 vs RS256) [Likelihood: Medium]
│       └── 2.3.2 Weak signing secret (brute-forceable) [Likelihood: Medium]
├── 3. Exploit Authorization Flaws
│   ├── 3.1 IDOR — access another user's resources by changing user ID in URL [Likelihood: High]
│   └── 3.2 Privilege escalation — modify role claim in JWT payload [Likelihood: Medium]
└── 4. Compromise the Auth Service Directly
    ├── 4.1 SQL injection in login endpoint [Likelihood: Medium]
    └── 4.2 Exploit unpatched dependency in auth service [Likelihood: Medium]

Mitigations derived from the attack tree:

Attack PathMitigationPriority
1.2 Credential stuffingRate limit login to 5 attempts per minute per IP; integrate HaveIBeenPwned API; require MFACritical
2.1.1 Predictable reset tokenGenerate tokens with 256-bit cryptographic randomness; expire after 15 minutesHigh
2.3.1 Algorithm confusionExplicitly set algorithms: ["RS256"] in JWT verification; reject tokens with alg: none or HS256High
3.1 IDOREnforce server-side ownership checks on every resource access; never rely on client-supplied user IDsHigh
4.1 SQL injectionUse parameterized queries; deploy a WAF rule for SQL injection patterns on the login endpointHigh

Best Practices

  • Threat model early in the design phase — identifying threats before code is written is dramatically cheaper than discovering them in production. Include threat modeling as a gate in the design review process.
  • Keep the model a living document — a threat model created once and never updated is worse than useless because it creates a false sense of security. Review and update it with every major architectural change or new feature.
  • Involve cross-functional stakeholders — developers understand the code, ops understands the deployment, and product understands the business impact. Effective threat modeling requires input from all three perspectives.
  • Focus on trust boundaries — the most exploitable vulnerabilities occur where data crosses trust boundaries: between the user and the application, between services, between the application and the database. Prioritize threats at these junctions.
  • Use threat modeling to drive backlog items — every mitigation identified should become a trackable work item with an owner and a deadline. Threat models that do not produce actionable backlog items have failed.
  • Validate threats with testing — after identifying a threat, write a corresponding security test (penetration test, integration test, or DAST rule) that verifies the mitigation is effective.

Safety Boundaries

  • Work only on systems the user owns or is explicitly authorized to assess, and record the approved scope before testing.
  • Start with passive or read-only inspection. Obtain explicit approval before active scanning, exploitation, load generation, or disruptive remediation.
  • Never expose secrets, extract unrelated data, weaken production controls, or expand beyond the approved targets.
  • Preserve evidence, minimize impact, stop on instability, and provide rollback or containment steps for every material change.

Edge Cases

  • Microservices with shared databases — when multiple services read and write to the same database, trust boundaries are blurred. A vulnerability in one service can compromise data that "belongs" to another service. Model each service's database access as a separate trust boundary and enforce schema-level isolation.
  • Third-party API integrations — external APIs (payment processors, identity providers, analytics) introduce threats outside your control. Model the integration as an untrusted data source, validate all responses, and plan for API outages or data integrity failures.
  • Event-driven architectures — in systems with message queues and event buses, threats include message injection, replay attacks, and out-of-order processing. Model the message broker as a component with its own trust boundary and ensure message authentication and idempotent processing.
  • Multi-tenant SaaS platforms — threats unique to multi-tenancy include cross-tenant data leakage, noisy-neighbor denial of service, and tenant impersonation. Model tenant isolation at the network, application, and data layers separately.
  • Rapidly evolving systems with frequent deploys — if the architecture changes weekly, a quarterly threat model review is insufficient. Integrate lightweight threat assessments into the PR review process for changes that modify trust boundaries, add new data flows, or introduce new external integrations.

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in security/threat-modeling of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5

Compare with similar skills

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Categories

Questions about Threat Modeling

What does Threat Modeling do?

Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited. Threat Modeling is an agent skill from seb1n/awesome-ai-agent-skills. Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited.

When should I use Threat Modeling?

Threat Modeling fits situations like: the user requests threat modeling; provides relevant inputs for this workflow.

How do I install Threat Modeling in Claude Code?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill threat-modeling -a claude-code`. Or copy the skill folder (security/threat-modeling in seb1n/awesome-ai-agent-skills) into .claude/skills/threat-modeling in your project. Claude Code loads it when a task matches its description.

How do I install Threat Modeling in Codex?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill threat-modeling -a codex`. Or copy the skill folder (security/threat-modeling in seb1n/awesome-ai-agent-skills) into .agents/skills/threat-modeling in your project. Codex loads it when a task matches its description.

Can I use Threat Modeling in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill threat-modeling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/threat-modeling, .gemini/skills/threat-modeling, .github/skills/threat-modeling and .opencode/skills/threat-modeling in your project.

What does Threat Modeling need to run?

SKILL.md names no scripts, command-line tools or credentials: Threat Modeling is instructions for the agent only. Our summary lists: Docker.

Does Threat Modeling access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Threat Modeling safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Threat Modeling use?

Threat Modeling is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Threat Modeling use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Threat Modeling?

Skills that share tags, products or a category with Threat Modeling: Fla Ascend Performance (fla-org/flash-linear-attention, 5.8k stars), Forensify (alexgreensh/repo-forensics, 188 stars), Create Rule (cartography-cncf/cartography, 4.1k stars) and Commit Security Scan (codexstar69/bug-hunter, 519 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Threat Modeling?

seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 101 skills in this directory. The repository was last updated on August 9, 2026.

Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.