Semgrep Rule Creator

---
name: semgrep-rule-creator
description: Creates custom Semgrep rules for detecting security vulnerabilities, bug patterns, and code patterns. Use when writing Semgrep rules or building custom static analysis detections.
allowed-tools: Bash Read Write Edit Glob Grep WebFetch
---

# Semgrep Rule Creator

Create production-quality Semgrep rules with proper testing and validation.

## When to Use

**Ideal scenarios:**
- Writing Semgrep rules for specific bug patterns
- Writing rules to detect security vulnerabilities in your codebase
- Writing taint mode rules for data flow vulnerabilities
- Writing rules to enforce coding standards

## When NOT to Use

Do NOT use this skill for:
- Running existing Semgrep rulesets
- General static analysis without custom rules (use `static-analysis` skill)

## Rationalizations to Reject

When writing Semgrep rules, reject these common shortcuts:

- **"The pattern looks complete"** → Still run `semgrep --test --config <rule-id>.yaml <rule-id>.<ext>` to verify. Untested rules have hidden false positives/negatives.
- **"It matches the vulnerable case"** → Matching vulnerabilities is half the job. Verify safe cases don't match (false positives break trust).
- **"Taint mode is overkill for this"** → If data flows from user input to a dangerous sink, taint mode gives better precision than pattern matching.
- **"One test is enough"** → Include edge cases: different coding styles, sanitized inputs, safe alternatives, and boundary conditions.
- **"I'll optimize the patterns first"** → Write correct patterns first, optimize after all tests pass. Premature optimization causes regressions.
- **"The AST dump is too complex"** → The AST reveals exactly how Semgrep sees code. Skipping it leads to patterns that miss syntactic variations.

## Anti-Patterns

**Too broad** - matches everything, useless for detection:
```yaml
# BAD: Matches any function call
pattern: $FUNC(...)

# GOOD: Specific dangerous function
pattern: eval(...)

Missing safe cases in tests - leads to undetected false positives:

# BAD: Only tests vulnerable case
# ruleid: my-rule
dangerous(user_input)

# GOOD: Include safe cases to verify no false positives
# ruleid: my-rule
dangerous(user_input)

# ok: my-rule
dangerous(sanitize(user_input))

# ok: my-rule
dangerous("hardcoded_safe_value")

Overly specific patterns - misses variations:

# BAD: Only matches exact format
pattern: os.system("rm " + $VAR)

# GOOD: Matches all os.system calls with taint tracking
mode: taint
pattern-sources:
  - pattern: input(...)
pattern-sinks:
  - pattern: os.system(...)

Strictness Level

This workflow is strict - do not skip steps:

• Read documentation first: See Documentation before writing Semgrep rules
• Test-first is mandatory: Never write a rule without tests
• 100% test pass is required: "Most tests pass" is not acceptable
• Optimization comes last: Only simplify patterns after all tests pass
• Avoid generic patterns: Rules must be specific, not match broad patterns
• Prioritize taint mode: For data flow vulnerabilities
• One YAML file - one Semgrep rule: Each YAML file must contain only one Semgrep rule; don't combine multiple rules in a single file
• No generic rules: When targeting a specific language for Semgrep rules - avoid generic pattern matching (languages: generic)
• Forbidden todook and todoruleid test annotations: todoruleid: <rule-id> and todook: <rule-id> annotations in tests files for future rule improvements are forbidden

Overview

This skill guides creation of Semgrep rules that detect security vulnerabilities and code patterns. Rules are created iteratively: analyze the problem, write tests first, analyze AST structure, write the rule, iterate until all tests pass, optimize the rule.

Approach selection:

• Taint mode (prioritize): Data flow issues where untrusted input reaches dangerous sinks
• Pattern matching: Simple syntactic patterns without data flow requirements

Why prioritize taint mode? Pattern matching finds syntax but misses context. A pattern eval($X) matches both eval(user_input) (vulnerable) and eval("safe_literal") (safe). Taint mode tracks data flow, so it only alerts when untrusted data actually reaches the sink—dramatically reducing false positives for injection vulnerabilities.

Iterating between approaches: It's okay to experiment. If you start with taint mode and it's not working well (e.g., taint doesn't propagate as expected, too many false positives/negatives), switch to pattern matching. Conversely, if pattern matching produces too many false positives on safe cases, try taint mode instead. The goal is a working rule—not rigid adherence to one approach.

Output structure - exactly 2 files in a directory named after the rule-id:

<rule-id>/
├── <rule-id>.yaml     # Semgrep rule
└── <rule-id>.<ext>    # Test file with ruleid/ok annotations

Quick Start

rules:
  - id: insecure-eval
    languages: [python]
    severity: HIGH
    message: User input passed to eval() allows code execution
    mode: taint
    pattern-sources:
      - pattern: request.args.get(...)
    pattern-sinks:
      - pattern: eval(...)

Test file (insecure-eval.py):

# ruleid: insecure-eval
eval(request.args.get('code'))

# ok: insecure-eval
eval("print('safe')")

Run tests (from rule directory): semgrep --test --config <rule-id>.yaml <rule-id>.<ext>

Quick Reference

• For commands, pattern operators, and taint mode syntax, see quick-reference.md.
• For detailed workflow and examples, you MUST see workflow.md

Workflow

Copy this checklist and track progress:

Semgrep Rule Progress:
- [ ] Step 1: Analyze the Problem
- [ ] Step 2: Write Tests First
- [ ] Step 3: Analyze AST structure
- [ ] Step 4: Write the rule
- [ ] Step 5: Iterate unti