skills/security-threat-model/SKILL.md
Systematically identifies vulnerabilities, threats, and mitigations for systems handling sensitive data using STRIDE methodology, trust boundary mapping, and defense-in-depth principles. Use when designing or reviewing systems with PII/PHI/financial/auth data, building security-sensitive features (auth, payments, file uploads, APIs), preparing for audits or compliance (PCI, HIPAA, SOC 2), investigating incidents, or integrating third-party services. Use when user mentions threat model, STRIDE, trust boundaries, attack surface, or security review.
npx skillsauth add lyndonkl/claude security-threat-modelInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Copy this checklist and track your progress:
Security Threat Model Progress:
- [ ] Step 1: Map system architecture and data flows
- [ ] Step 2: Identify trust boundaries
- [ ] Step 3: Classify data and compliance requirements
- [ ] Step 4: Apply STRIDE to identify threats
- [ ] Step 5: Define mitigations, monitoring, and prioritize risks
Step 1: Map system architecture and data flows
Document components, external services, users, data stores, and communication paths. See Common Patterns for architecture examples. For straightforward systems → Use resources/template.md.
Step 2: Identify trust boundaries
Mark where data crosses security domains (user → server, server → database, internal → third-party). See Trust Boundary Mapping for boundary types.
Step 3: Classify data and compliance requirements
Rate data sensitivity (public, internal, confidential, restricted), identify PII/PHI/PCI, document compliance obligations (GDPR, HIPAA, PCI DSS). See resources/template.md for classification tables.
Step 4: Apply STRIDE to identify threats
For each trust boundary and data flow, systematically check all six STRIDE threat categories. See STRIDE Framework for threat identification. For complex systems with multiple attack surfaces → Study resources/methodology.md for advanced attack tree analysis and DREAD scoring.
Step 5: Define mitigations, monitoring, and prioritize risks
Propose preventive/detective/corrective controls, establish monitoring and alerting, prioritize by risk score (likelihood × impact). Self-check using resources/evaluators/rubric_security_threat_model.json. Minimum standard: Average score ≥ 3.5.
S - Spoofing Identity
T - Tampering with Data
R - Repudiation
I - Information Disclosure
D - Denial of Service
E - Elevation of Privilege
Trust boundary: Where data crosses security domains with different trust levels.
Common boundaries:
Boundary analysis questions:
Pattern 1: Web Application with Database
Pattern 2: API with Third-Party OAuth
Pattern 3: Microservices Architecture
Pattern 4: File Upload Service
Pattern 5: Mobile App with Backend API
Assume breach mindset:
Prioritize realistically:
Avoid security theater:
Document assumptions:
Update threat model:
Resources:
5-Step Process: Map Architecture → Identify Boundaries → Classify Data → Apply STRIDE → Mitigate & Monitor
STRIDE: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege
Trust Boundaries: User→App, App→DB, Internal→External, Public→Private, Client→Server, Privileged→Unprivileged
Mitigation Types: Preventive (block attacks), Detective (identify attacks), Corrective (respond to attacks)
Prioritization: High-value assets first, compliance-critical threats, realistic risk vs. cost balance
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.