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Semgrep

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semgrep
semgrep

Run Semgrep static analysis scans and create custom detection rules. Use when asked to scan code with Semgrep, find security vulnerabilities, write custom YAML rules, or detect specific bug patterns. IMPORTANT: Also use this skill when users ask to 'scan for bugs', 'check code quality', 'find vulnerabilities', 'static analysis', 'lint for security', 'audit this code', or want to enforce coding standards — even if they don't mention Semgrep by name. Semgrep is the right tool for pattern-based code scanning across 30+ languages.

Overview

Publishersemgrep
Repositoryskills
Skill namesemgrep
Stars
330
Forks
32
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by semgrep on GitHub. Read the source before you install it.

Installation

Install the Semgrep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/semgrep/skills/tree/main/skills/semgrep
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/semgrep/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/semgrep .claude/skills/semgrep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Semgrep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Semgrep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Semgrep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Semgrep Static Analysis

Fast, pattern-based static analysis for security scanning and custom rule creation.

MCP Tools Available

If Semgrep MCP tools are available in your environment, prefer them for scanning:

  • semgrep_scan — Scan code files for security vulnerabilities using built-in rulesets. Pass absolute file paths and an optional config (e.g., p/security-audit, auto).
  • semgrep_scan_with_custom_rule — Scan code with a custom YAML rule you've written. Pass code content inline along with the rule.
  • semgrep_findings — Fetch existing findings from the Semgrep AppSec Platform for a repository.
  • semgrep_rule_schema — Get the full schema for writing Semgrep rules.
  • get_supported_languages — List all languages Semgrep supports.

When MCP tools aren't available, fall back to the CLI commands below.

When to Use Semgrep

Ideal scenarios:

  • Quick security scans (minutes, not hours)
  • Pattern-based bug and vulnerability detection
  • Enforcing coding standards and best practices
  • Finding known vulnerability patterns (OWASP, CWE)
  • Creating custom detection rules for your codebase
  • Data flow analysis with taint mode

Installation (CLI)

bash
# pip (recommended)
python3 -m pip install semgrep

# Homebrew
brew install semgrep

# Docker
docker run --rm -v "${PWD}:/src" semgrep/semgrep semgrep --config auto /src

Part 1: Running Scans

Quick Scan

bash
semgrep --config auto .                    # Auto-detect rules

Using Rulesets

bash
semgrep --config p/<RULESET> .             # Single ruleset
semgrep --config p/security-audit --config p/trailofbits .  # Multiple
RulesetDescription
p/defaultGeneral security and code quality
p/security-auditComprehensive security rules
p/owasp-top-tenOWASP Top 10 vulnerabilities
p/cwe-top-25CWE Top 25 vulnerabilities
p/trailofbitsTrail of Bits security rules
p/pythonPython-specific
p/javascriptJavaScript-specific
p/golangGo-specific

Output Formats

bash
semgrep --config p/security-audit --sarif -o results.sarif .   # SARIF
semgrep --config p/security-audit --json -o results.json .     # JSON

Scan Specific Paths

bash
semgrep --config p/python app.py           # Single file
semgrep --config p/javascript src/         # Directory
semgrep --config auto --include='**/test/**' .  # Include tests

Configuration

.semgrepignore

tests/fixtures/
**/testdata/
generated/
vendor/
node_modules/

Suppress False Positives

python
password = get_from_vault()  # nosemgrep: hardcoded-password
dangerous_but_safe()  # nosemgrep

Part 2: Creating Custom Rules

When to Create Custom Rules

  • Detecting project-specific vulnerability patterns
  • Enforcing internal coding standards
  • Building security checks for custom frameworks
  • Creating taint-mode rules for data flow analysis

Approach Selection

ApproachUse When
Taint modeData flows from untrusted source to dangerous sink (injection vulnerabilities)
Pattern matchingSyntactic patterns without data flow requirements (deprecated APIs, hardcoded values)

Prioritize taint mode for injection vulnerabilities. Pattern matching alone can't distinguish between eval(user_input) (vulnerable) and eval("safe_literal") (safe).

Quick Start: Pattern Matching

yaml
rules:
  - id: hardcoded-password
    languages: [python]
    message: "Hardcoded password detected: $PASSWORD"
    severity: ERROR
    pattern: password = "$PASSWORD"

Quick Start: Taint Mode

yaml
rules:
  - id: command-injection
    languages: [python]
    message: User input flows to command execution
    severity: ERROR
    mode: taint
    pattern-sources:
      - pattern: request.args.get(...)
      - pattern: request.form[...]
    pattern-sinks:
      - pattern: os.system(...)
      - pattern: subprocess.call($CMD, shell=True, ...)
    pattern-sanitizers:
      - pattern: shlex.quote(...)

Pattern Syntax Quick Reference

SyntaxDescriptionExample
...Match anythingfunc(...)
$VARCapture metavariable$FUNC($INPUT)
<... ...>Deep expression match<... user_input ...>
OperatorDescription
patternMatch exact pattern
patternsAll must match (AND)
pattern-eitherAny matches (OR)
pattern-notExclude matches
pattern-insideMatch only inside context
pattern-not-insideMatch only outside context
metavariable-regexRegex on captured value

Testing Rules

Test-first is mandatory. Create test files with annotations:

python
# test_rule.py
def test_vulnerable():
    user_input = request.args.get("id")
    # ruleid: my-rule-id
    cursor.execute("SELECT * FROM users WHERE id = " + user_input)

def test_safe():
    user_input = request.args.get("id")
    # ok: my-rule-id
    cursor.execute("SELECT * FROM users WHERE id = ?", (user_input,))

Run tests:

bash
semgrep --test --config rule.yaml test-file

Command Reference

TaskCommand
Run testssemgrep --test --config rule.yaml test-file
Validate YAMLsemgrep --validate --config rule.yaml
Dump ASTsemgrep --dump-ast -l <lang> <file>
Debug taint flowsemgrep --dataflow-traces -f rule.yaml file

Rule Creation Workflow

  1. Analyze the problem - Understand the bug pattern, determine taint vs pattern approach
  2. Create test cases first - Write ruleid: and ok: annotations before the rule
  3. Analyze AST - Run semgrep --dump-ast to understand code structure
  4. Write the rule - Start simple, iterate
  5. Test until 100% pass - No "missed lines" or "incorrect lines"
  6. Optimize patterns - Remove redundancies only after tests pass

Output structure:

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

Detailed References

Official Semgrep Documentation:

Local References:

Anti-Patterns to Avoid

Too broad:

yaml
# BAD: Matches any function call
pattern: $FUNC(...)

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

Missing safe cases:

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

# GOOD: Include safe cases
# ruleid: my-rule
dangerous(user_input)

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

Rationalizations to Reject

ShortcutWhy It's Wrong
"Semgrep found nothing, code is clean"Semgrep is pattern-based; can't track complex cross-function data flow
"The pattern looks complete"Untested rules have hidden false positives/negatives
"It matches the vulnerable case"Matching vulnerabilities is half the job; verify safe cases don't match
"Taint mode is overkill"For injection vulnerabilities, taint mode gives better precision
"One test case is enough"Include edge cases: different coding styles, sanitized inputs, safe alternatives

CI/CD Integration

GitHub Actions

yaml
name: Semgrep

on:
  push:
    branches: [main]
  pull_request:
  schedule:
    - cron: '0 0 1 * *'

jobs:
  semgrep:
    runs-on: ubuntu-latest
    container:
      image: returntocorp/semgrep

    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - name: Run Semgrep
        run: |
          if [ "${{ github.event_name }}" = "pull_request" ]; then
            semgrep ci --baseline-commit ${{ github.event.pull_request.base.sha }}
          else
            semgrep ci
          fi
        env:
          SEMGREP_RULES: >-
            p/security-audit
            p/owasp-top-ten
            p/trailofbits

Resources

Rule Writing:

General:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Semgrep AI skill do?

Run Semgrep static analysis scans and create custom detection rules. Use when asked to scan code with Semgrep, find security vulnerabilities, write custom YAML rules, or detect specific bug patterns. IMPORTANT: Also use this skill when users ask to 'scan for bugs', 'check code quality', 'find vulnerabilities', 'static analysis', 'lint for security', 'audit this code', or want to enforce coding standards — even if they don't mention Semgrep by name. Semgrep is the right tool for pattern-based code scanning across 30+ languages.

Why use Semgrep on TypingMind?

Because you install it once and use it with any model. Semgrep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Semgrep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/semgrep/skills/tree/main/skills/semgrep. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Semgrep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Semgrep?

As many as you like. As long as a model supports skills, you can use Semgrep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Semgrep AI skill free?

It is published on GitHub by semgrep. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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