external/trailofbits-security/property-based-testing/skills/property-based-testing/SKILL.md
Provides guidance for property-based testing across multiple languages and smart contracts. Use when writing tests, reviewing code with serialization/validation/parsing patterns, designing features, or when property-based testing would provide stronger coverage than example-based tests.
npx skillsauth add seikaikyo/dash-skills property-based-testingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill proactively during development when you encounter patterns where PBT provides stronger coverage than example-based tests.
Invoke this skill when you detect:
encode/decode, serialize/deserialize, toJSON/fromJSON, pack/unpacknormalize, sanitize, clean, canonicalize, formatis_valid, validate, check_* (especially with normalizers)add/remove/get operationsPriority by pattern:
| Pattern | Property | Priority | |---------|----------|----------| | encode/decode pair | Roundtrip | HIGH | | Pure function | Multiple | HIGH | | Validator | Valid after normalize | MEDIUM | | Sorting/ordering | Idempotence + ordering | MEDIUM | | Normalization | Idempotence | MEDIUM | | Builder/factory | Output invariants | LOW | | Smart contract | State invariants | HIGH |
Do NOT use this skill for:
| Property | Formula | When to Use |
|----------|---------|-------------|
| Roundtrip | decode(encode(x)) == x | Serialization, conversion pairs |
| Idempotence | f(f(x)) == f(x) | Normalization, formatting, sorting |
| Invariant | Property holds before/after | Any transformation |
| Commutativity | f(a, b) == f(b, a) | Binary/set operations |
| Associativity | f(f(a,b), c) == f(a, f(b,c)) | Combining operations |
| Identity | f(x, identity) == x | Operations with neutral element |
| Inverse | f(g(x)) == x | encrypt/decrypt, compress/decompress |
| Oracle | new_impl(x) == reference(x) | Optimization, refactoring |
| Easy to Verify | is_sorted(sort(x)) | Complex algorithms |
| No Exception | No crash on valid input | Baseline property |
Strength hierarchy (weakest to strongest): No Exception → Type Preservation → Invariant → Idempotence → Roundtrip
Based on the current task, read the appropriate section:
TASK: Writing new tests
→ Read [{baseDir}/references/generating.md]({baseDir}/references/generating.md) (test generation patterns and examples)
→ Then [{baseDir}/references/strategies.md]({baseDir}/references/strategies.md) if input generation is complex
TASK: Designing a new feature
→ Read [{baseDir}/references/design.md]({baseDir}/references/design.md) (Property-Driven Development approach)
TASK: Code is difficult to test (mixed I/O, missing inverses)
→ Read [{baseDir}/references/refactoring.md]({baseDir}/references/refactoring.md) (refactoring patterns for testability)
TASK: Reviewing existing PBT tests
→ Read [{baseDir}/references/reviewing.md]({baseDir}/references/reviewing.md) (quality checklist and anti-patterns)
TASK: Test failed, need to interpret
→ Read [{baseDir}/references/interpreting-failures.md]({baseDir}/references/interpreting-failures.md) (failure analysis and bug classification)
TASK: Need library reference
→ Read [{baseDir}/references/libraries.md]({baseDir}/references/libraries.md) (PBT libraries by language, includes smart contract tools)
When you detect a high-value pattern while writing tests, offer PBT as an option:
"I notice
encode_message/decode_messageis a serialization pair. Property-based testing with a roundtrip property would provide stronger coverage than example tests. Want me to use that approach?"
If codebase already uses a PBT library (Hypothesis, fast-check, proptest, Echidna), be more direct:
"This codebase uses Hypothesis. I'll write property-based tests for this serialization pair using a roundtrip property."
If user declines, write good example-based tests without further prompting.
Do not accept these shortcuts:
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.