gaelic-ghost/socket/author-yara-x-rules

Claude Code and agent skill by gaelic-ghost/socket

Trust tier 3
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Checking that the SKILL.md file is still in the repository.

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.

Install

npx vskill@latest install gaelic-ghost/socket/author-yara-x-rules

SKILL.md

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 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.
  1. 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.
  1. 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.
  1. 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.
  1. Review false positives and coverage.
  • Tune by improving evidence combinations, not by accumulating arbitrary exclusions.
  1. 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.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
7
7-day trend score
10

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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