Timeliner

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ timeliner-emit --version
timeliner 1.0.0

$ timeliner-emit --help
usage: timeliner [-h] [--version] [--format {table,json}] files [files ...]

Merge logs/CSVs into one forensic super-timeline.

positional arguments:
  files

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

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

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

{
"feed_name": "My Example Feed",
"spec_version": "2.0",
"entities": [
    {
        "id": "1234567890123456",
        "type": "indicator",
        "name": "Example Indicator",
        "description": "This is an example indicator.",
        "created_by": "John Doe",
        "modified": "2023-02-20T14:30:00.000Z"
    }
],
"objects": [
    {
        "id": "1234567890123456",
        "type": "observed-data",
        "name": "Example Observation",
        "description": "This is an example observation.",
        "created_by": "John Doe",
        "modified": "2023-02-20T14:30:00.000Z"
    }
],
"notes": [
    {
        "id": "1234567890123456",
        "type": "note",
        "name": "Example Note",
        "description": "This is an example note.",
        "created_by": "John Doe",
        "modified": "2023-02-20T14:30:00.000Z"
    }
]
}

Usage — step by step

1. Install the CLI (console script timeliner):

   pip install cognis-timeliner
   

2. Merge sources into a timeline — timeliner takes one or more log/CSV files as positional arguments and prints a normalized, time-sorted super-timeline:

   timeliner auth.log syslog.csv access.log
   

3. Emit machine-readable JSON for piping into other tools:

   timeliner auth.log syslog.csv --format json > timeline.json
   

4. Read the output — each event has ts, source, and event fields; the table view prints (undated) for unparseable timestamps and a trailing count of events/sources. Slice it with jq:

   timeliner *.log --format json | jq '.[] | select(.source=="auth.log")'
   

5. Automate in CI — normalize incident artifacts into one timeline:

   - run: pip install cognis-timeliner
   - run: timeliner artifacts/*.log artifacts/*.csv --format json > timeline.json
   

Contents

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

Why timeliner?

Build a forensic super-timeline by merging & normalizing log/artifact CSVs — without standing up heavyweight infrastructure.

timeliner 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

• ✅ Todo
• ✅ Fixme
• ✅ Xxx
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[disk / memory artifact] --> P[timeliner<br/>parse]
  P --> OUT[timeline + IOCs]

Use it from any AI stack

timeliner is interoperable with every popular way of using AI:

• MCP server — timeliner mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe timeliner 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 timeliner	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 (timeliner 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/timeliner.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/timeliner.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/timeliner.git" # uv
pip install cognis-timeliner                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/timeliner:latest --help        # Docker
brew install cognis-digital/tap/timeliner                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/timeliner/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/timeliner	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 timeliner 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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