Author, test, tune, and document YARA-X rules from validated artifact evidence. Use when suspicious files, scripts, documents, or binary features need local detection with stable patterns, fixtures, performance checks, and regression tests.
Installs into .claude/skills of the current project.
Are you the author of Author Yara X Rules?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/gaelic-ghost-author-yara-x-rules)
---
name: author-yara-x-rules
description: Author, test, tune, and document YARA-X rules from validated artifact evidence. Use when suspicious files, scripts, documents, or binary features need local detection with stable patterns, fixtures, performance checks, and regression tests.
---
# Author YARA-X Rules
## Overview
Create rules that detect the validated property the evidence supports, not a broader malware-family claim. Prefer structural combinations over unique-looking strings copied from one sample.
Read [references/yara-x-rule-quality.md](references/yara-x-rule-quality.md) before selecting patterns or declaring coverage.
## Workflow
1. Define the detection objective and non-goals.
2. Build the fixture set.
- Preserve representative positive samples and near-miss benign negatives with hashes and provenance.
- Use synthetic or redistributable fixtures for repository tests.
3. Select discriminators.
- Prefer format/module facts, byte structures, stable code/config fragments, and combinations of independently meaningful strings.
- Avoid mutable infrastructure, compiler boilerplate, paths, timestamps, or one generic API name as decisive evidence.
4. Author metadata and conditions.
- Include purpose, author, date, source/evidence reference, scope, confidence, and known limitations.
- Bound file type and size where it improves correctness or performance.
5. Validate with current YARA-X.
- Record version; compile/lint the rule; test all positives, negatives, malformed inputs, and a bounded benign corpus.
- Investigate timeouts, warnings, and module-undefined behavior.
6. Review false positives and coverage.
- Tune by improving evidence combinations, not by accumulating arbitrary exclusions.
7. Preserve regression evidence.
- Store allowed fixtures or deterministic generators, expected matches/non-matches, and rule revision.
## Output
Return the rule, objective, evidence basis, fixture results, performance notes, known misses/false positives, and deployment limits.
