Metadata-Version: 2.4
Name: intent-cli-python
Version: 2.1.0
Summary: Semantic history for agent-driven development. Records what you did and why.
Author: Zeng Deyang
License-Expression: MIT
Project-URL: Homepage, https://github.com/dozybot001/Intent
Project-URL: Repository, https://github.com/dozybot001/Intent
Keywords: agent,git,semantic-history,intent,developer-tools
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Version Control :: Git
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# Intent

[中文](README.CN.md) | English

Semantic history for agent-driven development. Preserves **how the product took shape** and **how work resumes across sessions and agents**.

## Why

Git records how code changes. But it doesn't record **why you're on this path**, what you decided along the way, or where you left off.

Intent adds that missing layer: **semantic history** — a small set of formal objects that preserve product formation history and survive context loss.

> Development is moving from *writing code* to *guiding agents and distilling decisions*. The history layer should reflect that.

```mermaid
flowchart LR
  subgraph traditional["Traditional Coding"]
    direction TB
    H1["Human"]
    C1["Code"]
    H1 -->|"Git"| C1
  end
  subgraph agent["Agent Driven Development"]
    direction TB
    H2["Human"]
    AG["Agent"]
    C2["Code"]
    H2 -."❌ no semantic history".-> AG
    AG -->|"Git"| C2
  end
  subgraph withintent["Agent with Intent"]
    direction TB
    H3["Human"]
    AG2["Agent"]
    C3["Code"]
    H3 -->|"Intent"| AG2
    AG2 -->|"Git"| C3
  end
  traditional ~~~ agent ~~~ withintent
```

## Three objects, one graph

| Object | What it captures |
|---|---|
| **Intent** | A goal recognized from your query |
| **Snap** | A semantic checkpoint that captures what changed, what was learned, and later feedback |
| **Decision** | A long-lived constraint that spans multiple intents |

Objects link automatically. Decisions auto-attach to every active intent; intents auto-attach to every active decision. Relationships are bidirectional and append-only.

```mermaid
flowchart LR
  D1["🔶 Decision 1"]
  D2["🔶 Decision 2"]

  subgraph Intent1["🎯 Intent 1"]
    direction LR
    S1["Snap 1"] --> S2["Snap 2"] --> S3["..."]
  end

  subgraph Intent2["🎯 Intent 2"]
    direction LR
    S4["Snap 1"] --> S5["Snap 2"] --> S6["..."]
  end

  D1 -- auto-attach --> Intent1
  D1 -- auto-attach --> Intent2
  D2 -- auto-attach --> Intent2
```

## Install

```bash
pipx install intent-cli-python   # CLI
npx skills add dozybot001/Intent -g  # Agent skill
```

Requires Python 3.9+ and Git. The CLI provides the commands; the skill teaches the agent when to use them.

> **Tips:** Because `itt` is a new command, agents are not trained on it yet. We recommend typing `/` at the start of each session, selecting the skill, and pressing Enter to enter the workflow.

## IntHub

```mermaid
flowchart TB
  Hub["IntHub — Collaboration Layer"]
  Intent["Intent — Semantic History Layer"]
  Git["Git — Code History Layer"]
  Hub <--> Intent <--> Git
```

IntHub is the remote collaboration layer on top of Intent. The first path is **IntHub Local** — download from a [GitHub release](https://github.com/dozybot001/Intent/releases), then:

```bash
itt hub link --api-base-url http://127.0.0.1:7210
itt hub sync
```

Open `http://127.0.0.1:7210` in the browser.

## Docs

- [Vision](docs/EN/vision.md) — why semantic history matters
- [CLI Design](docs/EN/cli.md) — object model, commands, JSON contract
- [Roadmap](docs/EN/roadmap.md) — phase plan
- [Dogfooding](docs/EN/dogfooding.md) — cross-agent case study
- [IntHub Local](docs/EN/inthub-local.md) — run a local IntHub instance

## License

MIT
