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  <img src="https://raw.githubusercontent.com/brycewang-stanford/stata-code/main/branding/logo/horizontal@1024.png" alt="stata-code logo" width="520" />
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<p align="center">
  <a href="README.md"><strong>English</strong></a> | <a href="README.zh.md">中文</a>
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# stata-code

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<div align="center">

<table>
  <tr>
    <td align="center">
      <a href="https://copaper.ai"><img src="https://raw.githubusercontent.com/brycewang-stanford/stata-code/main/branding/partners/copaper-logo.png" alt="CoPaper.AI" width="200" /></a>
    </td>
    <td width="48"></td>
    <td align="center">
      <a href="https://sccei.fsi.stanford.edu/reap"><img src="https://raw.githubusercontent.com/brycewang-stanford/stata-code/main/branding/partners/stanford-reap-logo.png" alt="Stanford REAP — Center on China's Economy & Institutions" width="280" /></a>
    </td>
  </tr>
</table>

<sub><strong>Stanford REAP × CoPaper.AI</strong> · an academic–industrial AI toolkit for empirical research</sub>

</div>

<p align="center">
  <img src="https://raw.githubusercontent.com/brycewang-stanford/stata-code/main/branding/github-instructions.png" alt="stata-code: agent-native Stata bridge — one Python core, multiple frontends (Jupyter kernel, MCP server, VS Code extension)" width="720" />
</p>

> Agent-native Stata bridge — **one Python core, multiple frontends**.

`stata-code` lets you drive Stata from modern environments: an LLM agent (Claude Code, Cursor, Claude Desktop), a Jupyter notebook, or a VS Code editor session. All frontends share one Python core and return a stable, structured, **agent-friendly** result schema.

**For empirical economists.** Drive Stata in plain language: run **DiD, IV, RDD, and publication-ready `esttab` tables in one conversation** — then cross-check each estimate across Stata and Python so you only trust results that *agree* (the Cunningham cross-package robustness check).

**Try it in 60 seconds** with [Claude Code](https://github.com/anthropics/claude-code) — no global install needed:

```bash
claude mcp add stata-code --scope user -- uvx --from "stata-code[mcp]" stata-code-mcp
```

Then just ask:

> *"Using `data/cfps_panel.dta`, run a two-way fixed-effects regression of monthly wage on the treatment (controls: `age age2 edu industry`), then test heterogeneous effects with Callaway-Sant'Anna, and export an `esttab` table."*

`stata-code` writes the do-file, runs it, returns the table, and interprets the result — and can re-estimate the same ATT with [StatsPAI](https://github.com/brycewang-stanford/StatsPAI) to confirm the two stacks agree. These workflows ship as one-call MCP prompts (`did_event_study`, `iv_2sls`, `rdd`, `publication_table`, `cross_validate_did`) backed by an on-demand [recipe library](skills/stata-code/references/recipes/).

**Why `stata-code`:** MIT-licensed · ships as an MCP server, a bundled agent skill, a Jupyter kernel, **and** a VS Code extension · one structured, token-economy result schema (typed errors, native `r()` / `e()`) · cross-stack validation with StatsPAI for the Cunningham check.

```text
                    ┌────────────────────────────────────────┐
                    │     stata-code core (Python)           │
                    │                                        │
                    │   • pystata adapter (Stata 17+)        │
                    │   • v1.0 unified result schema         │
                    │   • token-economy defaults             │
                    │   • multi-session via Stata frames     │
                    │   • typed errors + suggestions         │
                    └────────────────────────────────────────┘
                       ↑              ↑              ↑
              ┌────────┴────┐  ┌──────┴─────┐  ┌────┴────────────┐
              │  Jupyter    │  │  MCP       │  │  VS Code        │
              │  kernel     │  │  server    │  │  extension      │
              └─────────────┘  └────────────┘  └─────────────────┘
```

**Status: v0.8 (June 2026)** — the core, MCP server, Jupyter kernel, and VS Code extension work end-to-end against Stata 18 MP. The test suite covers schema, runner, MCP, kernel, notebook, run-index, subprocess-pool, and VS Code modules; CI also checks linting, type safety, schema generation, package metadata, and VSIX packaging. License: **MIT**.

Three workflows the current tree explicitly supports for end users and agents:

- **Run Stata code from a Jupyter notebook.** `pip install "stata-code[kernel]"` + `stata-code-kernel install --user` registers a **Stata** kernel that the Jupyter Notebook UI, JupyterLab, and the VS Code Jupyter extension all pick up by name. Cells render Stata logs, graphs, and warnings inline (the kernel logo bundled since v0.5 makes it appear in VS Code's kernel picker too). See [As a Jupyter Kernel](#as-a-jupyter-kernel).
- **Optional agent "fix and rerun" loop.** `stata_run` returns typed `error.kind/line/context` plus `suggestions` on every failure. By default Claude Code only reports diagnostics — but if you explicitly say "fix this and rerun until it passes", the agent uses the same fields to edit your `.do` file and re-call `stata_run` until the run is green. The repair loop is **opt-in**: failed runs are diagnostics first, not automatic rewrite permission. See [Error Recovery in Agent Workflows](#error-recovery-in-agent-workflows).
- **Economist workflow guides.** The bundled skill and cookbook now cover
  modern DiD, IV/weak-IV, RDD, table export, data-MCP handoff, and
  cross-stack parity audits. `stata-code` runs and audits the Stata leg; R,
  Python, and official data MCPs remain separate tools with explicit handoff
  files and source metadata. See [`skills/stata-code/references/`](skills/stata-code/references/)
  and [`examples/`](examples/).

---

## Why this exists

The Stata AI / agent tooling landscape is fragmented; see [References-tools.md](References-tools.md):

- Existing MCP servers ([SepineTam/stata-mcp](https://github.com/sepinetam/stata-mcp), [tmonk/mcp-stata](https://github.com/tmonk/mcp-stata)) are **AGPL-3.0**, which is not a fit for closed-source or commercial integration.
- The popular VS Code AI extension ([hanlulong/stata-mcp](https://github.com/hanlulong/stata-mcp)) is MIT, but it bundles the MCP server inside the extension, making standalone reuse awkward.
- Each tool wraps `pystata` with its own result shape, so agents have to special-case each integration.
- Many existing tools were designed for humans first and then bolted onto MCP; they often dump long logs and base64 graph blobs into every reply, burning tokens by default.

`stata-code` is designed to fill that gap:

1. **MIT-licensed**, with no copyleft contagion.
2. One shared result schema for every frontend: [SCHEMA.md](SCHEMA.md).
3. Agent-native by default: typed errors, structured `r()` / `e()`, log refs, graph refs, and suggestion seeds.
4. One core, multiple frontends: Jupyter kernel, MCP server, and VS Code extension.

For the project's clean-room policy around AGPL/GPL Stata projects, see [LICENSE-POLICY.md](LICENSE-POLICY.md).

---

## Install

Requirements: **Stata 17+** (with `pystata` shipped by Stata) and **Python 3.10+**.

```bash
# from PyPI
pip install stata-code

# with the MCP server and Jupyter kernel extras
pip install "stata-code[mcp,kernel]"

# or from source (editable install for development)
git clone https://github.com/brycewang-stanford/stata-code.git
cd stata-code
pip install -e ".[mcp,kernel]"
```

> **Naming note.** The PyPI distribution is `stata-code` (hyphen), but
> the Python import is `stata_code` (underscore — Python identifiers
> can't contain hyphens). Same convention as `scikit-learn` →
> `import sklearn`. So: `pip install stata-code`,
> `from stata_code import run`.

Note: `pystata` is **not** on PyPI; it ships with Stata. `stata-code` auto-discovers it on macOS at `/Applications/Stata/utilities/pystata` and at equivalent Linux / Windows paths. If your install is elsewhere, add it to `PYTHONPATH` before importing.

Verify the local setup with the read-only doctor:

```bash
stata-code doctor
stata-code doctor --json          # machine-readable output
stata-code doctor --no-stata-probe # skip live Stata initialization
```

The doctor reports the package/Python version, MCP and Jupyter extras, `pystata`
discovery, console scripts on `PATH`, client/VS Code configuration hints, and a
best-effort Stata version/edition probe. It never edits shell, Stata, Claude, or
VS Code config.

---

## Quick Start

See [`examples/`](examples/) for end-to-end cookbook entries: basic regression, DiD, graphs, multi-session, and large matrices.

### As a Python Library

The package-level `run()` / `execute()` API uses the same subprocess-backed
runner as the MCP server, so long calls honor `timeout_ms` and `pystata`
stdout redirection stays isolated from the caller process.

```python
from stata_code import run

r = run("sysuse auto, clear")
r = run("regress mpg weight")

if r.ok:
    print(r.results.e.scalars["r2"])           # 0.6515 (native float)
    print(r.results.e.macros["cmd"])           # "regress"
    b = r.results.e.matrices["b"]
    print(dict(zip(b.cols, b.values[0])))      # {"weight": -0.006, "_cons": 39.44}
else:
    print(r.error.kind, r.error.message)       # ErrorKind.VARNAME_NOT_FOUND, "..."
    for s in r.error.suggestions:
        print("hint:", s.action)               # "Did you mean `mpg`?"
```

### As an MCP Server

After `pip install "stata-code[mcp]"`, the `stata-code-mcp` binary is on your `PATH`. You can wire it into Claude Code, Cursor, Claude Desktop, or any other MCP-compatible client.

#### Claude Code via `claude mcp add` (recommended)

If you have not installed Claude Code yet, see [anthropics/claude-code](https://github.com/anthropics/claude-code).

The fastest way is the `claude mcp add` CLI. Pick a scope based on how widely you want `stata-code` available:

```bash
# user scope — install once, available in every Claude Code workspace on this machine
claude mcp add stata-code --scope user -- stata-code-mcp

# local scope — only for the current workspace (your local Claude config, not committed)
claude mcp add stata-code --scope local -- stata-code-mcp

# project scope — written into ./.mcp.json so collaborators on this repo share it
claude mcp add stata-code --scope project -- stata-code-mcp
```

Then launch `claude` and type `/mcp` to confirm `stata-code` shows up with its 18 tools (`stata_run`, `stata_info`, `get_log`, `search_log`, `get_graph`, `get_matrix`, `inspect_data`, `install_package`, `list_sessions`, `cancel_session`, `reset_session`, `notebook_outline`, `notebook_get_cell`, `notebook_locate`, `notebook_edit_cell`, `notebook_insert_cell`, `notebook_delete_cell`, `list_runs`).

#### Error Recovery in Agent Workflows

`stata_run` does not rewrite the source `.do` file or change code on its own. It executes the submitted Stata code, so that code may still create logs, graphs, tables, or other outputs as usual. When Stata fails, `stata_run` returns typed diagnostics (`error.kind`, `error.message`, `error.line`, `error.context`) plus best-effort `suggestions`. That supports two distinct Claude Code workflows:

- For "run this do-file" or "verify this code", Claude can report the failure and suggested next steps without changing source files.
- For "fix this and rerun until it passes", Claude can use the same structured error fields to edit the `.do` file, call `stata_run` again, and iterate.

If you want the repair loop, say so explicitly. Otherwise, treat failed runs as diagnostics first, not as automatic permission to rewrite code.

#### `uvx` (no global pip install)

If you prefer not to `pip install stata-code` globally, run it ephemerally through [`uv`](https://github.com/astral-sh/uv):

```bash
claude mcp add stata-code --scope user -- uvx --from "stata-code[mcp]" stata-code-mcp
```

`uvx` will resolve and cache `stata-code` on first launch. Note: `pystata` is **not** on PyPI, so it still has to be locatable on the host. The runner adds the standard Stata install path (e.g. `/Applications/Stata/utilities/pystata` on macOS) to `sys.path` automatically; if your Stata lives elsewhere, set `PYTHONPATH` in the env block.

#### Claude Code via plugin marketplace

This repository also ships a Claude Code plugin manifest (`.claude-plugin/`). Once you've added the marketplace to your Claude Code config, two commands wire up both the MCP server and the agent skill that teaches Claude the v1.0 result schema:

```bash
claude plugin marketplace add brycewang-stanford/stata-code
claude plugin install stata-code
```

The plugin registers the `stata-code` MCP server and installs the [`stata-code` skill](skills/stata-code/SKILL.md) so Claude branches on `error.kind`, calls `get_log(ref)` lazily, and uses the notebook-edit tools without you re-explaining them every session.

#### Other MCP clients (Cursor / Claude Desktop / Cline / Continue / Windsurf / Antigravity)

Most non-Claude-Code MCP clients accept the same JSON snippet. Drop it into the client's MCP config file:

| Client | Config file |
| --- | --- |
| Claude Desktop | macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`; Windows: `%APPDATA%\Claude\claude_desktop_config.json` |
| Cursor | `~/.cursor/mcp.json` (user) or `<workspace>/.cursor/mcp.json` (project) |
| Windsurf | `~/.codeium/windsurf/mcp_config.json` |
| Cline (VS Code) | settings: `cline.mcpServers` |
| Continue | `~/.continue/config.json` under `experimental.modelContextProtocolServers` |
| Antigravity / generic | `~/.claude/mcp.json` or whatever the client documents |

```json
{
  "mcpServers": {
    "stata-code": {
      "command": "stata-code-mcp"
    }
  }
}
```

Or, when the binary is not on `PATH`, run it as a module:

```bash
python -m stata_code.mcp
```

When `stata-code-mcp` lives inside a project virtualenv (recommended for reproducibility), point the client at the absolute path:

```json
{
  "mcpServers": {
    "stata-code": {
      "command": "/abs/path/to/.venv/bin/stata-code-mcp"
    }
  }
}
```

For `uvx`-only setups, set `"command": "uvx"` and `"args": ["--from", "stata-code", "stata-code-mcp"]`.

#### MCP troubleshooting

If `stata_run` reports `adapter_crash` with `worker emitted non-JSON: '\n'`,
upgrade to `stata-code>=0.6.4`, then restart the MCP client so it launches a
fresh server process. Also check that the client is resolving the expected
`stata-code-mcp` binary; project virtualenv installs should use the absolute
`.venv/bin/stata-code-mcp` path instead of relying on a global `PATH` entry.

If an OpenAI-backed client reports `API Error: 400 Invalid schema for function
'mcp__stata-code__notebook_insert_cell'` and mentions a top-level `oneOf`,
upgrade to `stata-code>=0.6.5`, then restart the MCP client. Older server
processes keep advertising the stale schema until they are restarted.

The MCP server registers 18 tools:

| Tool | Purpose |
| --- | --- |
| `stata_run` | Execute Stata code and return a v1.0 RunResult JSON |
| `stata_info` | Report Stata edition, version, and capabilities |
| `get_log` | Fetch the full log behind a `log://` ref |
| `search_log` | Search matching lines inside a stored `log://` payload |
| `get_graph` | Fetch graph bytes behind a `graph://` ref (`ImageContent`) |
| `get_matrix` | Fetch matrix payloads behind a `matrix://` ref |
| `inspect_data` | Run `describe` + `codebook` and return compact dataset metadata |
| `install_package` | Install an SSC or explicit `net install` package and verify it resolves |
| `list_sessions` | Enumerate live sessions |
| `cancel_session` | Cancel a session; the subprocess-backed path terminates in-flight runs and short-circuits pending ones |
| `reset_session` | Drop a session's data |
| `notebook_outline` | Compact per-cell index of a `.ipynb` (cell_id, type, preview) |
| `notebook_get_cell` | One cell's full source plus a token-economic outputs summary |
| `notebook_locate` | Find cells by snippet / regex / pasted error text |
| `notebook_edit_cell` | Atomically replace one cell's source (preserves id, clears outputs) |
| `notebook_insert_cell` | Insert a new cell with a fresh nbformat 4.5+ UUID |
| `notebook_delete_cell` | Remove a cell by id |
| `list_runs` | Query run-bundle manifests (filter by notebook / cell_id / session / since / ok, page with limit / offset) |

For modern MCP clients, these tools now return structured results through
`structuredContent` with `outputSchema` metadata, while still keeping the
serialized JSON text block for older clients. The server also exposes MCP
resources:

| Resource | Purpose |
| --- | --- |
| `stata://schema/run-result` | JSON Schema for `stata_run` structured output |
| `stata://server/capabilities` | Server instructions, tools, and resource templates |
| `stata://sessions` | Current subprocess-backed Stata sessions |
| `log://...` | Full log text from a truncated `stata_run` result |
| `graph://...` | Captured graph image bytes |
| `matrix://...` | Deferred large matrix payloads |

MCP prompts are available for common agent workflows:
`run_do_file_and_report`, `debug_stata_error`,
`fix_and_rerun_until_passes`, `replication_audit`,
`plan_cross_stack_parity_audit`, `data_mcp_to_stata_handoff`,
`summarize_estimation_results`, `run_notebook_cell_and_report`,
`fix_and_rerun_notebook_cell`, `did_event_study`, `iv_2sls`, `rdd`,
`publication_table`, and `cross_validate_did`.

### As a Jupyter Kernel

`stata-code` ships a Jupyter kernel as part of the Python package — there is no separate "Jupyter plugin" in the JupyterLab extension marketplace. Installation is two steps: `pip install` the package with the `kernel` extra, then register the kernelspec with Jupyter.

**Prerequisites**: Stata 17+ installed locally with a valid license (the kernel calls Stata via `pystata`), and Python 3.10+ with `jupyter`/`jupyterlab` already on the same environment.

```bash
# 1. Install stata-code with the kernel extra (pulls in ipykernel)
pip install "stata-code[kernel]"

# 2. Register the kernelspec into Jupyter's user data dir
stata-code-kernel install --user
# Or, equivalently:
# python -m stata_code.kernel install --user
```

Verify the kernel is registered:

```bash
jupyter kernelspec list
# should include an entry named `stata`
```

Then open Jupyter Notebook / JupyterLab (or a `.ipynb` in VS Code), pick **Stata** in the kernel selector, and run Stata commands in cells. Logs, graphs, and warnings render inline.

> JupyterLab's Extension Manager only installs front-end JS extensions, so it cannot install a kernel — `pip install` plus the `install --user` step above is the only supported path.

### As a VS Code Extension

The companion extension is on the Marketplace as [`brycewang-stanford.stata-code-vscode`](https://marketplace.visualstudio.com/items?itemName=brycewang-stanford.stata-code-vscode). It spawns `stata-code-mcp` as a child process and adds syntax highlighting, an Outline view for `**#` sections and `program define` blocks, code-lens "Run cell" and "Run section" actions on `.do` files, a **seven-view sidebar** (sessions / last result / **data variables** / run history / logs / graphs / **outputs**) — including an agent-native equivalent of Stata's **Variables window** and an **Outputs** panel that surfaces the `esttab` tables and `export` files each run writes to disk — status-bar indicators, completions, help lookup, conservative variable rename, and inline diagnostics from the v1.0 typed errors.

```bash
# from the VS Code CLI
code --install-extension brycewang-stanford.stata-code-vscode
```

Or open the **Extensions** sidebar in VS Code and search `stata-code`. The extension is also available from [Open VSX](https://open-vsx.org/) so Cursor, Windsurf, and other VS Code-compatible editors can install it without going through the Microsoft Marketplace.

On first activation the extension probes for `stata-code-mcp` on `PATH` (and in any workspace `.venv` / `venv`). If nothing resolves, it shows a one-time install hint with the exact `pip install "stata-code[mcp]"` command — choose **Don't show again** to silence it for the installed extension version.

If the extension or an MCP client cannot find the server, run
`stata-code doctor --no-stata-probe` in the same Python environment. It reports
whether `stata-code-mcp` is on `PATH` and suggests absolute-path or
`python -m stata_code.mcp` fallbacks for GUI clients whose `PATH` differs from
your shell.

#### Cell and section conventions

The extension recognizes two complementary structural markers inside `.do` files. Either can be mixed in the same file; they do not conflict.

| Marker | Purpose | Example |
| --- | --- | --- |
| `* %% [title]` | Cell boundary. Each marker gets a **▶ Run Cell** code-lens; "Run Cell" submits the lines between this marker and the next one. Compatible with the Jupyter-style cell convention used by `kylebutts/vscode-stata`. | `* %% 02 model fit` |
| `**# title` … `**###### title` | Section heading, 1–6 levels deep. Each heading gets a **▶ Run Section** code-lens and contributes to the Outline view. "Run Section" submits the heading through the next equal- or higher-level heading, matching the hierarchical execution model from `ZihaoVistonWang.stata-all-in-one`. | `**## DiD specification` |

`program define … end` blocks are also surfaced in the Outline, nested under whichever section contains them.

The extension still requires the MCP extra on your system Python (`pip install "stata-code[mcp]"`), so that `stata-code-mcp` resolves on `PATH` and can import the MCP SDK. Stata 17+ and a valid Stata license are required as for any other frontend.

---

## Token-Economy Defaults

A typical `stata_run` response is about **10x smaller** than servers that dump logs and images directly. Three design choices drive this:

1. **Logs return `head` + `tail` + `ref`** by default. Full logs are fetched on demand via `get_log(ref)`. A Stata regression log can be about 6,000 tokens; `stata-code` returns about 600 by default.
2. **Graphs return refs, not inline base64**. A 30 KB PNG can become about 50,000 base64 tokens; returning a ref avoids that unless the agent actually needs the bytes.
3. **Errors are typed**. Agents can check `err.kind == "varname_not_found"` instead of regex-parsing English logs.

For example, a misspelled variable returns a structured error:

```json
{
  "ok": false,
  "rc": 111,
  "error": {
    "kind": "varname_not_found",
    "varname": "mpgg",
    "line": 3,
    "context": {
      "before": ["use auto"],
      "failing": "summarize mpgg",
      "after": []
    },
    "suggestions": [
      {"action": "Did you mean `mpg`?", "command": "describe"}
    ]
  }
}
```

The full schema is in [SCHEMA.md](SCHEMA.md).

---

## Architecture

```text
stata_code/
├── core/
│   ├── _runtime.py    # process-singleton pystata wrapper
│   ├── _refs.py       # LRU ref store for log/graph/matrix payloads
│   ├── schema.py      # Pydantic v2 models for the v1.0 result schema
│   ├── errors.py      # rc → ErrorKind mapping + suggestion seeds
│   ├── runner.py      # in-process execute(); collects everything via sfi
│   └── _pool.py       # subprocess workers for public API / MCP hard timeouts
├── mcp/
│   └── server.py      # MCP server (18 tools)
└── kernel/
    └── kernel.py      # Jupyter kernel
```

`runner.py` is the only place that directly talks to `pystata`. The public Python API and MCP server route calls through `_pool.py`, whose workers call `runner.execute()` in an isolated subprocess; the Jupyter kernel uses the in-process runner for notebook interactivity.

---

## Comparison

| | stata-code | SepineTam/stata-mcp | hanlulong/stata-mcp | nbstata |
| --- | --- | --- | --- | --- |
| License | **MIT** | AGPL-3.0 | MIT | GPL-3.0 |
| Standalone MCP | ✓ | ✓ | bundled with VS Code | — |
| Jupyter kernel | ✓ | — | — | ✓ |
| Unified result schema | ✓ ([SCHEMA.md](SCHEMA.md)) | per-tool | per-tool | per-tool |
| Token-economy defaults | ✓ (log refs, graph refs) | — | — | — |
| Typed errors + suggestions | ✓ (31 kinds) | — | — | — |
| Multi-session | ✓ (Stata frames) | partial | — | — |
| Mature ecosystem | early | ✓ (statamcp.com, cookbook) | ✓ (11k installs) | ✓ |

`stata-code` is the younger, MIT-licensed, agent-native alternative in this problem space. Among the AGPL options, SepineTam's `stata-mcp` is currently more mature; `stata-code` is aimed at cases where copyleft contagion is unacceptable and agents need structured results.

---

## Roadmap

### Done (current tree)

- v1.0 result schema ([SCHEMA.md](SCHEMA.md))
- `pystata`-based runner with native-typed `r()`, `e()`, and matrices
- Multi-session via Stata frames (`session_id` accepts `[A-Za-z0-9_-]+`; ids such as `model-a` are mapped to private legal frame names internally while the public id is echoed back)
- Per-line error attribution: line number, context, commands_executed
- Graph capture: `png` / `svg` / `pdf` with ref store and source-command attribution
- Log truncation with ref store
- Warning extraction: 5 categories + generic notes
- 31-kind error taxonomy with canonical suggestions
- MCP server: 18 tools, including notebook navigation / search / atomic edits, the run-bundle index (`list_runs`), log grep (`search_log`), dataset inspection (`inspect_data`), and package installation (`install_package`)
- Jupyter kernel: rewired to the v1.0 pipeline, kernel logos bundled
- Matrix size cap + `get_matrix(ref)` for large matrices (>10k cells)
- Subprocess-backed hard timeout and cancellation for the public Python API and MCP server: `timeout_ms`, `cancel(session_id)`, and MCP `cancel_session`
- Per-cell repair loop on `.ipynb` via `notebook_outline` / `notebook_get_cell` / `notebook_edit_cell` with optimistic-concurrency `expected_source` guards and `origin_cell_id` echo on `RunResult`
- Persistent run bundles + `list_runs` query over `manifest.json` files (filter by cell / origin / session / since / ok; page with limit / offset)
- Read-only `stata-code doctor` / `verify` diagnostics for package version,
  extras, `pystata` discovery, console scripts, client hints, and optional live
  Stata version probing
- Economist workflow layer: skill references and examples for modern DiD,
  IV/weak-IV, RDD, table export, data-MCP handoff, and cross-stack parity
  audits
- JSON Schema artifact auto-generated from `schema.py`: [`schema/run_result.schema.json`](schema/run_result.schema.json)
- VS Code extension published to the Marketplace as [`brycewang-stanford.stata-code-vscode`](https://marketplace.visualstudio.com/items?itemName=brycewang-stanford.stata-code-vscode): syntax highlighting, section outline/navigation, code-lens cell and section runners, seven-view sidebar (sessions / last result / data variables / run history / logs / graphs / outputs), status bar, completions, conservative variable rename, diagnostics, MCP child-process spawn
- Clean-room license policy ([LICENSE-POLICY.md](LICENSE-POLICY.md))

### Next Up

- Console fallback for Stata 11–16, re-implemented against the v1.0 schema
- Decide whether to move the Jupyter kernel from the direct in-process runner to the subprocess pool, or keep documenting the current interactivity-first tradeoff
- Extra VS Code polish: extension-host end-to-end tests, first-run diagnostics, and command palette UX
- **v1.0** — Stable schema, broader Stata edition coverage

See [SCHEMA.md §7](SCHEMA.md) for explicitly out-of-scope items.

---

## Testing

```bash
pip install -e ".[dev,mcp,kernel]"
pytest                              # full suite, including Stata tests when Stata is available
pytest -m "not stata_required"      # CI subset; no Stata needed
pytest -m "stata_required" -v       # real-Stata integration tests only
```

The `stata_required` marker tags the real-Stata integration tests. CI uses `pytest -m "not stata_required"` so it does not collect them. Locally without Stata, those tests skip cleanly with the `"pystata / Stata 17+ not available"` message.

---

## Contributing

- Read [LICENSE-POLICY.md](LICENSE-POLICY.md) before opening a PR.
- Add a one-line acknowledgement to your first PR description; the template is in the policy file.
- Tests are required for any new schema field or runner behavior.

---

## License

The code is licensed under [MIT](./LICENSE). [LICENSE-POLICY.md](LICENSE-POLICY.md) explains how this project relates to other Stata projects.

## Trademark Notice

Stata is a registered trademark of StataCorp LLC. This project is independent and not affiliated with or endorsed by StataCorp.

## Acknowledgements

The Stata tooling landscape that this project builds on and learns from is surveyed in [References-tools.md](References-tools.md). All listed projects retain their own licenses and authorship; please consult each repository before reuse.

---

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