Gitstory

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ gitstory-emit --version
gitstory 0.1.0

$ gitstory-emit --help
usage: gitstory [-h] [--version] [--format {table,json}] {changelog,bump} ...

Changelog and release notes from conventional commits.

positional arguments:
  {changelog,bump}
    changelog           build a changelog from a git log
    bump                recommend the next semantic version

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 gitstory output — reproduce them from a clone.

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

{
"timestamp": "2023-02-15T14:30:00Z",
"findings": [
    {
        "id": "1234567890abcdef",
        "title": "Suspicious Network Traffic",
        "description": "Potential malicious activity detected on port 443.",
        "severity": "medium",
        "labels": ["network", "traffic"],
        "created_at": "2023-02-15T14:30:00Z"
    },
    {
        "id": "2345678901cdefghij",
        "title": "Unusual File Access",
        "description": "Access to a sensitive file detected from an unknown location.",
        "severity": "high",
        "labels": ["file", "access"],
        "created_at": "2023-02-15T14:35:00Z"
    }
]
}

Usage — step by step

1. Install (Python 3.9+):

   pip install gitstory
   

2. Build a changelog from a git log. Feed it the structured log (or plain subjects) on stdin:

   git log --format='%H%x1f%s%x1f%b%x1e' v1.2.0..HEAD | gitstory changelog -t v1.3.0
   

3. Recommend the next semantic version from the same commits:

   git log --format='%H%x1f%s%x1f%b%x1e' v1.2.0..HEAD | gitstory bump v1.2.0
   

4. Read the output: the table view prints the markdown changelog plus a summary line (commit counts, breaking changes, recommended version). Use --format json to consume markdown, recommended_version and bump_level programmatically.
5. Automate a release step (write notes + capture the next tag):

   git log --format='%H%x1f%s%x1f%b%x1e' $(git describe --tags --abbrev=0)..HEAD \
       | gitstory changelog --format json -c $(git describe --tags --abbrev=0) > release.json
   

Contents

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

Why gitstory?

classic viral DX utility

gitstory 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 Commit
• ✅ Parse Log
• ✅ Group Commits
• ✅ Bump Version
• ✅ Render Markdown
• ✅ Build Changelog
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

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

Use it from any AI stack

gitstory is interoperable with every popular way of using AI:

• MCP server — gitstory mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe gitstory 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 gitstory	git-cliff
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 git-cliff, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (gitstory 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/gitstory.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/gitstory.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/gitstory.git" # uv
pip install cognis-gitstory                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/gitstory:latest --help        # Docker
brew install cognis-digital/tap/gitstory                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/gitstory/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/gitstory	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• mcpforge — Scaffold, test, and publish MCP servers in minutes
• promptlint — Lint, version, and test prompts as code with a CI gate
• envdoctor — .env validator, secret-presence and config-drift checker
• 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

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 gitstory 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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