PROMPTPACK

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ promptpack-emit --version
promptpack 0.1.0

$ promptpack-emit --help
usage: promptpack [-h] [--version] [--db DB] [--format {table,json}]
                  {commit,list,get,history,tag,rollback,render,diff,ab,choose} ...

Versioned prompt registry with A/B and rollbacks.

positional arguments:
  {commit,list,get,history,tag,rollback,render,diff,ab,choose}
    commit              add a new immutable version
    list                list prompts
    get                 show a version's body
    history             version history of a prompt
    tag                 point a tag at a version
    rollback            roll a tag back to a prior version
    render              render a version with variables
    diff                unified diff between two refs
    ab                  attach weighted A/B variants to a tag
    choose              select an A/B variant (deterministic with --key)

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

Blocks above are real promptpack 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 Traffic",
        "description": "A potential threat was detected on a network interface.",
        "severity": "medium",
        "created_at": "2023-02-15T14:30:00Z"
    },
    {
        "id": "2345678901",
        "title": "Malware Detection",
        "description": "A malicious file was detected on a system.",
        "severity": "high",
        "created_at": "2023-02-16T10:45:00Z"
    }
]
}

Usage — step by step

1. Install the CLI (Python 3.9+):

   pip install git+https://github.com/cognis-digital/promptpack.git
   

2. Commit an immutable version of a prompt to the registry:

   promptpack commit greeting --file greeting.txt -m "first cut"
   

3. Tag a version and render it with variables substituted:

   promptpack tag greeting prod --ref latest
   promptpack render greeting --ref prod --var name=Ada
   

4. Inspect history, diff two refs, or read JSON for tooling:

   promptpack history greeting
   promptpack diff greeting 1 2
   promptpack --format json list
   

5. Run a deterministic A/B selection (e.g. in a serving path):

   promptpack ab greeting prod 1:1 2:3
   promptpack choose greeting prod --key user-123
   

Contents

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

Why promptpack?

promptops

promptpack 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

• ✅ Fast, single-purpose CLI
• ✅ JSON / SARIF output for pipelines
• ✅ CI fail-gate (--fail-on)
• ✅ MCP server for AI agents
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

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

Use it from any AI stack

promptpack is interoperable with every popular way of using AI:

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

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

Related Cognis tools

• agentsmith — Config-first scaffolding and orchestration for multi-agent workflows
• skillhub — Local skill registry and installer for AI agents
• toolguard — Runtime allowlist and policy for agent tool-calls
• evalbench — Offline LLM / agent eval harness with regression gates
• ragkit — Batteries-included local RAG pipeline — ingest, index, serve
• memorybank — Portable long-term memory store for agents, exposed over MCP

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 promptpack saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a sharedOpenAI-compatible /v1 backbone. See INTEROP.md for thesuite 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.