pip install cognis-regexlab
regexlab scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ regexlab-emit --version
regexlab 1.0.0

$ regexlab-emit --help
usage: regexlab [-h] [--version] {explain,test,bench,scan,patterns} ...

Test, explain & benchmark regexes + a library of security patterns. regex
without footguns.

positional arguments:
  {explain,test,bench,scan,patterns}
    explain             break a regex into plain English
    test                run a regex against input
    bench               benchmark a regex
    scan                scan input with the security pattern library
    patterns            list built-in security patterns

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit

Blocks above are real regexlab output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"findings": [
    {
        "id": "123456",
        "title": "Suspicious Network Activity",
        "description": "Potential malicious activity detected on network 192.168.1.100",
        "severity": "medium",
        "created_at": "2023-02-15T14:30:00Z"
    },
    {
        "id": "789012",
        "title": "Malware Detection",
        "description": "Detected malware 'BadBot' on system 'Windows-10-PC'",
        "severity": "high",
        "created_at": "2023-02-15T14:31:00Z"
    }
]
}

Usage — step by step

1. Install (Python 3.9+):

   pip install regexlab            # or: pipx install regexlab
   

2. Explain a pattern in plain English:

   regexlab explain '^(\d{4})-(\d{2})-(\d{2})$'
   

3. Test it against input. Pass the subject inline with -t (or -i to read from a file), and set flags with --flags:

   regexlab test '\bERROR\b' -t "2026 ERROR boot" --flags i
   

4. Benchmark a regex to catch catastrophic backtracking:

   regexlab bench '(a+)+$' --iterations 1000
   

5. Scan with the security pattern library and read the result. Use --min-severity to filter and --format json for machine-readable output:

   regexlab scan -i app.log --min-severity high --format json -o scan.json
   

6. List built-in patterns and gate in CI:

   regexlab patterns
   regexlab scan -i app.log --min-severity high   # non-zero exit signals findings
   

Contents

• Why regexlab? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why regexlab?

Test, explain & benchmark regexes + a library of security patterns — without standing up heavyweight infrastructure.

regexlab is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

Features

• ✅ Parse Flags
• ✅ Flags To List
• ✅ Detect Redos Risk
• ✅ Explain Pattern
• ✅ Test Pattern
• ✅ Benchmark Pattern
• ✅ Scan Text
• ✅ Match To Dict
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-regexlab
regexlab --version
regexlab scan .                       # scan current project
regexlab scan . --format json         # machine-readable
regexlab scan . --fail-on high        # CI gate (non-zero exit)

Example

$ regexlab scan .
  [HIGH    ] REG-001  example finding             (./src/app.py)
  [MEDIUM  ] REG-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[input] --> P[regexlab<br/>analyze + score]
  P --> OUT[report]

Use it from any AI stack

regexlab is interoperable with every popular way of using AI:

• MCP server — regexlab mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe regexlab scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

How it compares

	Cognis regexlab	typical tools
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (regexlab mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/regexlab.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/regexlab.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/regexlab.git" # uv
pip install cognis-regexlab                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/regexlab:latest --help        # Docker
brew install cognis-digital/tap/regexlab                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/regexlab/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/regexlab	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If regexlab saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.

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