Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Security-Phoenix-demo/security-skills-claude-code --skill threat-modelinggit clone --depth 1 https://github.com/Security-Phoenix-demo/security-skills-claude-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/security-phoenix-demo/security-skills-claude-code/threat-modeling)<a href="https://agentmods.dev/skills/security-phoenix-demo/security-skills-claude-code/threat-modeling"><img src="https://agentmods.dev/badge/skills/security-phoenix-demo/security-skills-claude-code/threat-modeling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/security-phoenix-demo/security-skills-claude-code/threat-modeling"><img src="https://agentmods.dev/badge/skills/security-phoenix-demo/security-skills-claude-code/threat-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00097 | $0.04168 |
| Opus 5.5 | $0.00039 | $0.01667 |
| Sonnet 5.5 | $0.00019 | $0.00834 |
| Haiku 4.5 | $0.00010 | $0.00417 |
Grade A, and why
threat-modeling scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST http://localhost:8090/api/v1/threat-model/assessments \ How it starts
The opening of the file, as written. The whole thing — 584 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Modeling Agent Skill
Purpose: Automated threat modeling from code analysis using STRIDE/DREAD methodologies
When to use: Security assessment, architecture review, compliance documentation, attack scenario planning
Workflow
1. Scope Determination
Determine analysis scope:
- Full repository threat model
- Feature-specific threat model
- Component-level threat model
- Architecture diagram analysis
Check prerequisites:
- Repository is indexed in knowledge graph
- Business context available (optional)
- Architecture diagrams uploaded (optional)
- CVE intelligence data available (optional)
2. Architecture Extraction
Extract system architecture from:
- Knowledge graph clusters (components)
- Call chains (data flows)
- Entry points (public APIs)
- Trust boundaries (zone crossings)
- Technology stack
- External dependencies
Enrich with:
- Architecture diagram analysis (AI vision)
- Business context (industry, compliance)
- Deployment context (cloud infrastructure)
3. STRIDE Threat Analysis
Generate threats for each category:
- Spoofing: Identity and authentication threats
- Tampering: Data and code integrity threats
- Repudiation: Audit and accountability threats
- Information Disclosure: Data leakage threats
- Denial of Service: Availability threats
- Elevation of Privilege: Access control threats
For each threat, identify:
- Affected components
- Affected data flows
- Trust boundary crossings
- Attack vectors
- Prerequisites
- Detection methods
4. DREAD Risk Assessment
Score each threat (1-10):
- Damage Potential
- Reproducibility
- Exploitability
- Affected Users
- Discoverability
Calculate:
- Risk Score = Average of 5 dimensions
- Risk Level = Critical/High/Medium/Low
5. Phoenix Intelligence Enrichment
Enrich with Phoenix data (IP-protected):
- Related CVEs
- Phoenix proprietary scores (high-level only)
- Threat actor data
- MITRE ATT&CK techniques
- CAPEC attack patterns
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.
- 1mo ago Changed · +11 lines · +97 tokens per session 3bf685298158
- 2mo ago First seen · 573 lines · 0 tokens per session scan A 24b67a49de99
threat-modeling is a skill published in the GitHub repository Security-Phoenix-demo/security-skills-claude-code (81 stars, last pushed 5d ago), licensed MIT. It adds 97 tokens to every session and 4,168 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
threat-modeling compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| threat-modelingSecurity-Phoenix-demo/security-skills-claude-code | Skill | 81 | 5d ago | MIT | A | 97 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |