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/author-yara-x-rules

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.

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socket
7200 skills5 MCP
Install
$ npx -y skills add gaelic-ghost/socket --skill author-yara-x-rules --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/author-yara-x-rules

Context preview

The summary Claude sees to decide when to auto-load this skill.

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.

SKILL.md

author-yara-x-rules.SKILL.md
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.

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