Semgrep Rule Creator V2

Semgrep Rule Creator

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/semgrep-rule-creator from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

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

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Rationalizations to Reject, Anti-Patterns, Strictness Level, Documentation, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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
• Running existing Semgrep rulesets
• General static analysis without custom rules (use static-analysis skill)

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Step 1: Analyze the Problem
2. Step 2: Write Tests First
3. Step 3: Analyze AST structure
4. Step 4: Write the rule
5. Step 5: Iterate until all tests pass (semgrep --test)
6. Step 6: Optimize the rule (remove redundancies, re-test)
7. Step 7: Final Run

Imported Workflow Notes

Imported: 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

Imported: 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

Imported: 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.

Examples

Example 1: Ask for the upstream workflow directly

Use @semgrep-rule-creator-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @semgrep-rule-creator-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @semgrep-rule-creator-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @semgrep-rule-creator-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: 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>

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/semgrep-rule-creator, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Quick Reference

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

Imported: 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(...)

Imported: 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

Imported: 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

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.