ENVDOCTOR

.env validator, secret-presence and config-drift checker

Developer Tools — fast, single-purpose, CI- and agent-friendly.

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ envdoctor-emit --version
envdoctor 0.1.0

$ envdoctor-emit --help
usage: envdoctor [-h] [--version] [--format {table,json}]
                 {lint,drift,check} ...

.env validator, secret-presence and config-drift checker.

positional arguments:
  {lint,drift,check}
    lint                lint a .env file for structure + secrets
    drift               detect config drift vs an example file
    check               validate a .env against a JSON schema

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format (default: table)

Blocks above are real envdoctor 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 Traffic",
        "description": "Potential malicious activity detected on port 443",
        "severity": "medium",
        "created_at": "2023-02-15T14:30:00Z"
    },
    {
        "id": "789012",
        "title": "Unusual File Access",
        "description": "User accessed a file with suspicious permissions",
        "severity": "high",
        "created_at": "2023-02-16T10:45:00Z"
    }
]
}

Usage — step by step

1. Install the CLI:

   pipx install "git+https://github.com/cognis-digital/envdoctor.git"

2. Lint a .env file for structural problems and exposed secrets — the primary command:

   envdoctor lint .env

3. Detect drift between your .env and the committed example, and validate against a JSON schema:

   envdoctor drift --example .env.example --env .env
   envdoctor check --schema env.schema.json --env .env

4. Read the output — table by default, or JSON for pipelines:

   envdoctor --format json lint .env > env-report.json

5. Automate in CI — block a deploy when config drifts from the example or fails the schema:

   envdoctor drift --example .env.example --env .env
   envdoctor check --schema env.schema.json --env .env
   # non-zero exit => config problem => job fails

Contents

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

Why envdoctor?

single-binary DX, viral

envdoctor 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.

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Features

• ✅ Parse Env
• ✅ Looks Like Secret
• ✅ Lint File
• ✅ Diff Env
• ✅ Check Schema
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

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

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Example

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

  2 findings · risk score 5 · 38ms

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Architecture

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

Loading

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Use it from any AI stack

envdoctor is interoperable with every popular way of using AI:

• MCP server — envdoctor mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe envdoctor 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

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How it compares

	Cognis envdoctor	dotenv-linter
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

Built in the spirit of dotenv-linter, re-framed the Cognis way. Missing a credit? Open a PR.

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Integrations

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

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Install — every way, every platform

pip install "git+https://github.com/cognis-digital/envdoctor.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/envdoctor.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/envdoctor.git" # uv
pip install cognis-envdoctor                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/envdoctor:latest --help        # Docker
brew install cognis-digital/tap/envdoctor                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/envdoctor/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/envdoctor	DEPLOY.md (AWS/Azure/GCP/k8s)

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Related Cognis tools

• mcpforge — Scaffold, test, and publish MCP servers in minutes
• promptlint — Lint, version, and test prompts as code with a CI gate
• apidiff — Breaking-change detector for OpenAPI / GraphQL across commits
• codeglance — Repo onboarding map — architecture + hotspots for humans and agents
• flakefinder — Flaky-test detector from CI history with quarantine suggestions
• licenselens — Dependency license + SBOM gate, developer-CLI first

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

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Contributing

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

⭐ If envdoctor 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 (licensing@cognis.digital). See LICENSE.

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_{Cognis Digital · one of 170+ tools in the Cognis Neural Suite · Making Tomorrow Better Today}