What is the core workflow that Semgrep Rule Creator enforces?

It enforces a strict test-first workflow: analyze the problem, write tests first, analyze the AST structure, write the rule, iterate until all tests pass via `semgrep --test`, then optimize only after tests are green. Untested rules are explicitly rejected since they hide false positives and negatives.

When should I use taint mode versus pattern matching?

Use taint mode for data flow issues where untrusted input reaches a dangerous sink, since pattern matching alone (e.g., `eval($X)`) cannot distinguish between `eval(user_input)` and `eval("safe_literal")`. Taint mode only alerts when untrusted data actually reaches the sink, providing far better precision.

What are the hard workflow rules I must follow?

You must read required Semgrep documentation before writing any rule, make test-first mandatory, require 100% test pass (not "most tests pass"), optimize only after all tests pass, avoid generic patterns and `languages: generic` when targeting a specific language, use exactly one rule per YAML file, and never use `todoruleid`/`todook` test annotations.

What output does this skill produce?

The output is exactly two files in a directory named after the rule ID: ` .yaml` (the rule itself) and ` . ` (the test file with `### ruleid:` annotations for cases that must match and `### ok:` annotations for cases that must not match).

What common shortcuts does this skill reject?

It rejects the rationalization that "the pattern looks complete" without running tests, that "it matches the vulnerable case" is sufficient without verifying safe cases don't match, that "taint mode is overkill" when data flows from user input to a sink, that "one test is enough" instead of covering different coding styles and boundary conditions, and that "I'll optimize first" since premature optimization causes regressions.

Skill

Create Production-Quality Semgrep Rules

Name: Semgrep Rule Creator
Availability: OnlineOnly
Author: Antigravity

Guidance on iterating between pattern matching and taint mode when writing Semgrep rules to minimize false positives.

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Updated last month

Source checked Aug 6, 2026

Version 15.9.0

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Why it matters

Automate the creation of robust Semgrep rules for identifying bug patterns, security vulnerabilities, and enforcing coding standards. Ensures rules are well-tested and validated for production use.

Outcomes

What it gets done

Generate Semgrep rules with proper testing and validation.

Write rules for specific bug patterns and security vulnerabilities.

Develop taint mode rules for data flow vulnerabilities.

Enforce coding standards with custom Semgrep rules.

Install

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Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-semgrep-rule-creator | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

Semgrep Rule Creator

Guidance on iterating between pattern matching and taint mode when writing Semgrep rules. Use this when writing Semgrep rules and your initial approach has accuracy issues - whether too many false positives, false negatives, or taint propagation problems. It applies when you need to switch from one detection method to another.

What it does

This resource explains the iterative approach to writing Semgrep static analysis rules. It describes how to experiment between pattern matching and taint mode approaches when building rules, switching methods when one doesn't work well.

When to use - and when NOT to

Use this guidance when writing Semgrep rules and your current approach isn't working well. It applies when taint doesn't propagate as expected in taint mode, when you're getting too many false positives or negatives, or when pattern matching flags too many safe cases. The approach encourages switching between methods rather than forcing a single methodology.

Do NOT use this guidance if you require rigid adherence to one specific rule-writing methodology. The approach is explicitly experimental and iterative, prioritizing working rules over methodological purity.

Inputs and outputs

You bring your experience with a Semgrep rule that isn't working well - whether due to taint propagation issues, excessive false positives, or other accuracy problems.

You learn that it's acceptable to experiment and switch between pattern matching and taint mode until you achieve a working rule, rather than remaining committed to a single approach.

Who it's for

This guidance is for anyone writing Semgrep rules who encounters accuracy issues with their initial approach and needs permission to experiment with alternative detection methods.

Source README

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.

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