Semgrep Rule Creator

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:

# 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-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 until all tests pass (semgrep --test)
- [ ] Step 6: Optimize the rule (remove redundancies, re-test)
- [ ] Step 7: Final Run

Documentation

REQUIRED: Before writing any rule, use WebFetch to read all of these 4 links with Semgrep documentation:

1. Rule Syntax
2. Pattern Syntax
3. ToB Testing Handbook - Semgrep
4. Constant propagation
5. Writing Rules Index

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache compliance check results to avoid re-running expensive AWS API calls. Retrieve prior audit findings to track remediation progress across sessions.

# Check for prior security context before starting
python3 execution/memory_manager.py auto --query "prior security audit results for Semgrep Rule Creator"

Storing Results

After completing work, store security decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Audit findings: 3 critical IAM misconfigurations found and remediated" \
  --type technical --project <project> \
  --tags semgrep-rule-creator security

Multi-Agent Collaboration

Share security findings with other agents so they avoid introducing vulnerabilities in their code changes.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Completed security audit — 3 critical findings fixed, compliance score 94%" \
  --project <project>

Signed Audit Trail

All security findings are cryptographically signed with the agent's Ed25519 identity, providing tamper-proof audit logs for compliance reporting.

Semantic Cache for Compliance

Cache compliance check results (semantic_cache.py) to avoid redundant AWS API calls. Cache hit at similarity >0.92 returns prior results instantly.