MILSTDLINT

Lint documents against MIL-STD / DoD formatting and classification-marking rules.

Part of the Cognis Neural Suite.

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ milstdlint-emit --version
milstdlint 0.1.0

$ milstdlint-emit --help
usage: milstdlint [-h] [--version] {lint} ...

Lint documents against MIL-STD / DoD formatting and classification-marking
rules (static analysis only).

positional arguments:
  {lint}
    lint      Lint one or more document files.

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

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

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

{
"findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Network Activity",
        "description": "Anomalous network traffic detected from IP 192.168.1.100",
        "severity": "medium",
        "created_at": "2023-02-20T14:30:00Z"
    },
    {
        "id": "2345678901",
        "title": "Potential Malware Infection",
        "description": "Malicious code detected on host 192.168.1.101",
        "severity": "high",
        "created_at": "2023-02-20T14:31:00Z"
    }
]
}

Usage — step by step

milstdlint statically lints documents against MIL-STD / DoD formatting and classification-marking rules. Console script: milstdlint.

1. Install from a clone:

   pip install -e .

2. Lint one or more files — exits non-zero on any ERROR-severity finding:

   milstdlint lint report.txt annex.txt

3. Tighten the gate — promote formatting warnings to errors with --strict:

   milstdlint lint --strict report.txt

4. Read the output — --format json emits per-file findings plus a summary:

   milstdlint lint report.txt --format json | jq '.summary'

   summary.failed / summary.total_errors tell you what to fix.
5. Emit SARIF for code scanning — --format sarif produces a SARIF 2.1.0 log that GitHub code scanning (and any SARIF viewer) renders inline:

   milstdlint lint docs/*.txt --format sarif > milstdlint.sarif

6. Automate in CI — block merges that break marking rules:

   - run: pip install -e .
   - run: milstdlint lint docs/**/*.txt --strict

Output formats

--format	Use it for
table (default)	Human-readable terminal output.
json	Piping into agents, jq, or compliance pipelines.
sarif	GitHub code scanning, Azure DevOps, SARIF viewers (SARIF 2.1.0).

Try the demos

The demos/ directory has ten ready-to-run scenarios, each with a realistic marked document and a SCENARIO.md (what it contains, what to expect, exact command, how to act):

Demo	Shows
01-basic	Under-marked SECRET memo (multiple errors).
02-clean	Clean UNCLASSIFIED baseline (zero findings).
03-mixed	Mostly-correct OPORD with two isolated defects.
04-cui-spec	Clean CUI interface control document.
05-nofile-banner	Missing overall banner (BANNER-MISSING).
06-line-length-tab	Formatting hygiene + the --strict gate.
07-portion-mismatch-ts	(TS) portion under a SECRET banner (under-marking).
08-clean-tdp	Clean multi-level technical data package cover sheet.
09-unknown-token	Invalid portion token (PORTION-UNKNOWN).
10-sarif-ci	SARIF 2.1.0 export for GitHub code scanning.

python -m milstdlint lint demos/07-portion-mismatch-ts/intsum_undermarked.txt
python -m milstdlint lint demos/10-sarif-ci/release_gate.txt --format sarif

Contents

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

Why milstdlint?

Lint documents against MIL-STD / DoD formatting and classification-marking rules. — without standing up heavyweight infrastructure.

milstdlint 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.

↑ back to top

Features

• ✅ Lint Text
• ✅ Lint File
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

↑ back to top

Quick start

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

↑ back to top

Example

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

  2 findings · risk score 5 · 38ms

↑ back to top

Architecture

flowchart LR
  IN[target / manifest] --> P[milstdlint<br/>checks + rules]
  P --> OUT[findings (JSON / SARIF)]

Loading

↑ back to top

Use it from any AI stack

milstdlint is interoperable with every popular way of using AI:

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

↑ back to top

How it compares

	Cognis milstdlint	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

↑ back to top

Integrations

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

↑ back to top

Install — every way, every platform

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

↑ back to top

Related Cognis tools

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

↑ back to top

Contributing

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

⭐ If milstdlint 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.

---

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