intpot

Part of ModePot.   Project website   Created by Tugrul Guner

One typed definition. Three interfaces.

Define typed tools once for CLI, HTTP, or MCP. Convert apps between Typer, FastAPI, and FastMCP.

Quick start · Playground · Deep docs · README quick start · Convert an app · Conversion scope · Architecture · Community · Contributing

Why intpot?

A useful Python function often needs three interfaces: a command for people, an HTTP endpoint for applications, and an MCP tool for AI agents. Maintaining three copies means three signatures, three sets of descriptions, and three places for behavior to drift.

intpot gives you two ways out:

The output is ordinary Python. Ejected and converted apps do not depend on intpot.

Quick start

Install every runtime for the complete experience:

pip install "intpot[all]"

Write once, serve everywhere

Save this as app.py:

from intpot import App

app = App("my-app")

@app.tool()
def add(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b

@app.tool()
def greet(name: str, greeting: str = "Hello") -> str:
    """Greet someone by name."""
    return f"{greeting}, {name}!"

Then serve in any mode:

intpot serve app.py --cli          # Run as Typer CLI
intpot serve app.py --api          # Run as FastAPI on port 8000
intpot serve app.py --mcp          # Run as MCP server for AI agents

In CLI mode, everything after the flags belongs to your app:

$ intpot serve app.py --cli add 2 3
5
$ intpot serve app.py --cli greet World --greeting Hi
Hi, World!

With API mode running, call the same names as POST routes with JSON request bodies:

$ curl -s -X POST http://127.0.0.1:8000/add \
    -H "Content-Type: application/json" \
    -d '{"a": 2, "b": 3}'
5

MCP mode exposes add and greet as FastMCP tools with schemas derived from their type annotations and defaults.

Or eject to standalone framework code:

intpot eject app.py --to api       # Export as standalone FastAPI app
intpot eject app.py --to cli       # Export as standalone Typer CLI
intpot eject app.py --to mcp       # Export as standalone FastMCP server

Scaffold a new project

intpot init my-server --type mcp
intpot init my-app --type cli
intpot init my-api --type api

Install only the frameworks you need if you do not want the full extra:

pip install intpot          # Core commands and Typer output
pip install "intpot[mcp]"   # Add FastMCP support
pip install "intpot[api]"   # Add FastAPI support

Convert an existing app

intpot detects Typer, FastAPI, and FastMCP apps from a Python file and supports every conversion between them:

# MCP server -> Typer CLI
intpot to cli server.py

# CLI app -> FastMCP server
intpot to mcp app.py

# CLI app -> FastAPI app
intpot to api app.py

# Write output to a file
intpot to cli server.py --output cli_app.py

# Convert all apps in a directory
intpot to cli ./myproject/
intpot to mcp ./myproject/ --output ./converted/

Directory output mirrors the source tree, so myproject/alpha/tools.py becomes converted/alpha/tools_mcp.py. Sources sharing a filename in different packages stay separate files.

Use intpot inspect app.py first when you want to see the normalized tool definitions before generating code. Converted output is readable Python that you can review, test, and change.

Conversion assessment before output

This supplementary diagram describes the public converter, not the live App path or a literal execution trace. Conversion uses ApplicationSchema with ToolSchema records. Target-specific assessment checks body bindings, eager definition expressions, and supported string/postponed annotations. Inspection stays available when a known missing binding prevents conversion; scaffold permission cannot bypass that refusal. These bounded checks do not evaluate types, recover arbitrary classes or certify Python equivalence. Detection imports trusted source code and is not a sandbox.

The runnable example requests a working original HTTP API, then reports a missing Model in the generated CLI annotations and refuses before creating output. Open the full-size assessment diagram.

Give your coding agent intpot context

Install project-local instructions for Claude Code, Cursor, Windsurf, GitHub Copilot, Cline, or OpenAI Codex:

# Auto-detect agents in your project
intpot add skills

# Target a specific agent
intpot add skills --agent claude
intpot add skills --agent cursor
intpot add skills --agent windsurf
intpot add skills --agent copilot
intpot add skills --agent cline
intpot add skills --agent codex

# Specify a project directory
intpot add skills --path ./myproject/

What intpot handles

• One definition, three live interfaces: serve an intpot.App through Typer, FastAPI, or FastMCP.
• Six conversion directions: move existing apps between all three frameworks.
• Standalone output: eject or convert to normal framework code with no intpot runtime dependency.
• Behavior-aware transforms: preserve recoverable function bodies and the direct imports they use, and translate framework conventions such as typer.echo() into return values.
• Interface fidelity: carry types, defaults, and async functions through supported conversions, and apply FastAPI parameter sources when generating an API.
• Inspectable semantic core: use intpot.load().schema for an immutable ApplicationSchema, inspect target projections before generation, or use detached ToolInfo compatibility objects through .tools.
• Project tooling: scan directories, scaffold projects, and install agent guidance for six coding agents.

Python API

Universal App (write once, serve everywhere)

from intpot import App

app = App("my-app")

@app.tool()
def greet(name: str, greeting: str = "Hello") -> str:
    """Greet someone."""
    return f"{greeting}, {name}!"

# Serve as any framework
app.serve(mode="cli")                         # Run as Typer CLI
app.serve(mode="api", port=8000)              # Run as FastAPI on 127.0.0.1
app.serve(mode="mcp")                         # Run as MCP server

# Eject to standalone code
cli_code = app.eject("cli")                   # Returns Typer code string
api_code = app.eject("api")                   # Returns FastAPI code string

# Inspect the canonical application schema
print(app.schema.to_dict())
for tool in app.schema.tools:
    print(tool.name, tool.parameters)

Conversion API (convert existing framework code)

import intpot

# From a file
app = intpot.load("mcp_server.py")
api_schema = app.project("api")               # Immutable target semantics
cli_code = app.to_cli()
api_code = app.to_api()

# From a live instance
from fastmcp import FastMCP

mcp = FastMCP("my-server")

@mcp.tool()
def greet(name: str) -> str:
    return f"Hello, {name}!"

app = intpot.load(mcp)
print(app.to_cli())

# Write directly to a file
app.write("output/cli_app.py", "cli")
app.write("output/api_app.py", "api")

App (universal runtime):

• .tool(name=None, description=None) — decorator to register functions as tools; both arguments override the defaults taken from the function name and docstring. Functions with *args or **kwargs are rejected because those parameters have no consistent CLI, HTTP API, and MCP representation.
• .serve(mode, host, port) — serve as CLI, API, or MCP
• .eject(target) — generate standalone framework code
• .schema — immutable ApplicationSchema snapshot shared with conversion
• .tools — detached ToolInfo compatibility objects

IntpotApp (conversion wrapper, returned by intpot.load()):

• .to_cli(), .to_mcp(), .to_api() — return generated code as strings
• .schema — immutable source ApplicationSchema, compiled once
• .project(target) — immutable target projection used by generation
• .write(path, target) — generate and write to a file in one step
• .tools — detached ToolInfo compatibility objects; mutating them does not alter the schema
• .source_type — detected framework type

What conversion preserves

intpot is designed to produce code you can own, rather than hide conversion behind a runtime adapter. ApplicationSchema is the application-level semantic boundary; immutable ToolSchema and ParameterSchema records carry the parts that the three frameworks share:

• tool names, descriptions, types, defaults, and async behavior;
• recoverable function bodies and the direct imports they reference;
• framework metadata that has a target equivalent, including supported Query, Header, Path, and Body parameter sources when generating FastAPI;
• scalar returns translated into a shape the target framework can serve correctly.

Frameworks do not have one-to-one equivalents for every feature. FastAPI dependencies (Depends, Security, nested dependencies, and route/router/app-level dependencies) are preserved by inspection, but API-to-CLI/MCP conversion refuses them with UnsupportedFastAPIDependencyError rather than emitting code with missing values. Full dependency injection mapping remains tracked in #20. Review generated code when a source uses nested Typer command groups, repeatable CLI options, Annotated[..., Body(...)], Pydantic model parameters, routes with multiple HTTP methods, streaming, background tasks, or framework-specific error handling.

Typer/Click parameter callbacks remain inspectable as unsupported_callback=True on parameter metadata (to_dict() includes the marker only when true). CLI-to-API/MCP projection and generation raise intpot.UnsupportedCLIParameterCallbackError, naming all affected tools/parameters and the target, rather than dropping callback validation or transformation. Assignment-style and Annotated declarations are detected. The CLI reports refusal on stderr with exit status 1 before writing output; --dry-run also refuses. This is a focused known-loss guard, not a general conversion assessment or callback transpiler. Source CLI behavior is unchanged.

intpot carries direct import statements referenced by a tool body. It does not yet copy same-module helpers, constants, classes, models, or closure values that the body references, and it does not follow dependencies across imported modules. Factory-created apps are not detected by the current AST pre-check. Loading a source file directly also does not add its directory to sys.path, so a sibling import such as from helpers import normalize may require installing the package or setting PYTHONPATH. External services and configuration are not provisioned for you. If intpot cannot recover a body, public conversion raises intpot.MissingFunctionBodyError before writing output. app.assess() returns JSON-compatible body-recovery diagnostics (body_recovery_complete, missing_function_body, and per-tool diagnostics); this is not a general fidelity guarantee. intpot inspect --json includes an assessment when bodies are missing. Schema inspection and the original source remain usable.

Explicit allow_scaffold=True on .project(), .to_cli(), .to_api(), .to_mcp(), or .write() (CLI: --allow-scaffold) emits labeled NotImplementedError implementations, not successful dummy results. Mixed directory conversions refuse before any output is written. This permission does not bypass callback or dependency refusals. Recovered pass, ellipsis, and docstring-only no-ops are not missing bodies. Low-level generators remain scaffold-capable for compatibility; they do not perform converter assessment, and live App serving/ejection is unchanged. See missing_body.py for an executable example.

Callback and dependency refusal assessment (v0.13.0)

Availability: requires v0.13.0 or newer. app.assess() and intpot inspect --json report unsupported_cli_parameter_callback per affected tool/parameter/target, and unsupported_fastapi_dependency per affected API tool/target. Dependency diagnostics include route context (path, operation_id, name) and normalized dependency names. Both include source_location: null, a consequence, suggested_action, and reference_analysis: "skipped_due_to_refusal": body-binding analysis for that target was not run, so the refusal does not imply that no binding issues exist. The consequence describes what would be lost without equivalent implementation; conversion is refused, so the source behavior is not silently dropped. --allow-scaffold does not bypass either refusal. Non-API dependency metadata is not treated as FastAPI injection. Run .venv/bin/python examples/conversion_refusals.py from a checkout to see source behavior, structured reports, and before-write refusal.

Bounded binding chains (v0.15.0)

Availability: requires v0.15.0 or newer. Until v0.15.0 is published, use a source checkout containing this change; v0.14.0 does not include references[*].dependency_chain.

For missing_binding, missing_definition_binding and missing_annotation_binding, each reference adds dependency_chain: one shortest list of emitted Python definition names from the target's public wrapper to that reference's owning definition. The missing binding remains reference.name, separate from the list. For example, ["calc", "_calc_impl", "scale", "leaf"] ends at the helper using the missing name. Equal-length paths use lexically sorted edges, and cycles are visited once.

This is a static binding-reference explanation, not an execution trace, call graph, proof of reachability, or evaluation-order guarantee. Edges use the existing scope-aware body-global analysis and only that tool's emitted implementation, wrapper and retained helpers. Local/parameter shadowing does not create a global helper edge. Global rebinding or an unanalyzable owner makes its path unavailable; an unconnected helper keeps dependency_chain: null. Attribute calls, dynamic dispatch, imports and arbitrary alias/type/default evaluation are not resolved into additional edges.

Existing codes, missing names, suggestions, source-definition locations and ordering remain intact. Generated wrapper origins remain unavailable. Paths add no recovery capability, change no conversion policy and do not bypass before-write refusal or scaffold restrictions. Python assess() and CLI inspect --json expose the same paths; source-code output and string-returning conversion APIs are unchanged.

Run uv run --all-extras --with httpx python examples/binding_chains.py from the source checkout. It executes an original helper-backed HTTP API and its generated CLI, uses an explicitly caller-authored incomplete schema for the transitive missing-name fixture, and separately checks real source-loading CLI/Python parity and refusal before writes. Source inspection does not gain unresolved-helper recovery from that fixture.

Bounded definition provenance (v0.14.0)

Availability: requires v0.14.0 or newer; until v0.14.0 is published, use a source checkout containing this change. v0.13.0 does not include captured definition coordinates.

Inspected ToolInfo and immutable ToolSchema carry optional source_location and support_source_locations. JSON locations have path, line, and end_line: an absolute owning-file path and inclusive, 1-based definition bounds. The first line is the def/async def, not its decorators. Helper locations are keyed by recovered helper name. Schema adapters and projections preserve these snapshots.

Capture reads and compiles the owning file without executing it, then requires a matching existing function code object. Where existing inspector recovery unwraps a callable, provenance describes the recovered original, not wrapper execution. Dynamic functions, closures and otherwise unverifiable callables have no captured location. Missing, unreadable or changed function bodies also yield no location. This is bounded code/definition provenance at inspection time, not proof that annotations/default values or the whole application are unchanged. Manually supplied schema metadata is caller-owned.

A diagnostic's top-level source_location identifies the affected tool definition, including callback/dependency refusals; it does not locate the callback, dependency, or exact missing-name occurrence. When provenance is available, binding references also carry the originating implementation/helper definition location. Synthetic public wrappers have source_location: null. Without provenance, reference shapes remain compatible and top-level locations stay null. Cached schemas retain their inspection snapshot; they do not continuously track filesystem edits.

Conversion policy, code-string return types, exception precedence and refusal before writes are unchanged. Inspection still imports trusted source code and is not a sandbox. Run uv run --all-extras --with httpx python examples/source_provenance.py for source behavior, helper coordinates, Python/CLI report parity and refusal checks.

Actionable binding context (v0.13.0)

Availability: requires v0.13.0 or newer; v0.12.0 does not include these additive fields.

loaded.assess() and intpot inspect app.py --json expose references, suggested_action, and source_location on missing_binding, missing_definition_binding, and missing_annotation_binding diagnostics. Existing codes, tool/target identity, names, messages and refusal exceptions remain unchanged. Each reference pairs a missing name with its generated_definition, including owned helpers and generated wrappers/implementations. These are emitted Python identifiers, not original-source coordinates. References are sorted by generated definition then name. Suggestions name the missing bindings; eager definition dependencies must exist before those definitions, while body and deferred-annotation dependencies must exist before use.

In published v0.13.0, source_location is always null (None in Python). v0.14.0 adds bounded definition provenance as described above; until publication, use a source checkout containing this change. An application source_path is not sufficient provenance, and no generated AST position is presented as an original line. Diagnostics are observations of bounded rendered-name checks, not a dependency graph or a proof of conversion readiness. Scaffold permission cannot bypass refusal, and inspection still imports trusted code.

Run the diagnostic-context example from the source checkout. It checks the original API via TestClient, prints real JSON context, and verifies that CLI/MCP refusal leaves output directories absent.

Missing body bindings (v0.11)

Public conversion now raises intpot.MissingBindingError before output writes when a retained implementation refers to globals absent from the generated target module. app.assess() and conditional intpot inspect --json assessment include per-target missing_binding diagnostics with tool, target, and sorted names. Existing body_recovery_complete and missing_function_body fields retain their meaning: recovering a body does not mean its dependencies are available.

Analysis reuses Python scope-aware binding analysis against statically rendered target implementations, accounting for locals, nested scopes, comprehensions, retained imports, builtins and generated self-recursion bindings. It renders but does not execute generated code. Binding availability assumes a normally file-loaded Python module, including loader-provided __file__, __cached__, and other standard module globals. Their values belong to the generated file; this does not preserve the original path, resources, or package environment. Custom exec namespaces must provide their own module globals. Targets blocked by existing rendering guards report reference_analysis_unavailable in assessment instead of implying a successful binding check. Inspection still imports trusted source code and is not a sandbox.

This first slice checks retained body bindings, not complete annotations/defaults, import availability, conditional binding initialization, runtime state, validation parity, or arbitrary Python equivalence. Recovery of the bounded subset is described below. Callback and FastAPI dependency refusal remain authoritative; allow_scaffold=True / --allow-scaffold does not bypass missing-binding refusal. Live App serving/ejection and direct low-level generator compatibility are unchanged. See examples/missing_binding.py for an original helper-based implementation with a dynamically initialized dependency that returns 21 and is explicitly refused.

Definition-evaluation binding diagnostics

Availability: requires v0.11.1 or a source checkout containing this change. Repository documentation can precede package publication.

Run uv run --all-extras --with httpx python examples/definition_bindings.py from a source checkout for a working original HTTP API whose MCP export reports the eager missing Model annotation and whose CLI export reports the v0.12.0 postponed-annotation diagnostic described below. Both refuse before output, even with scaffold opt-in. The consumer check requires httpx.

Public converter assessment also reports missing_definition_binding when generated function decorators, defaults, or non-postponed annotation expressions refer to an absent or later module binding. Diagnostics retain tool, target, sorted names, and an actionable message; provide the required import/definition before the affected function, or adapt the source to the supported subset. Checks include generated wrappers and the ordered support functions owned by each tool, without executing generated code.

Projection, conversion, and writes raise MissingBindingError for these diagnostics, including --dry-run and --allow-scaffold; mixed-directory conversion writes nothing. Inspection remains available, and body-recovery fields still describe bodies only.

This is a bounded ordered binding check, not full annotation/default readiness. Later framework resolution, called initializer behavior, arbitrary conditional initialization, import availability, and Python equivalence are not certified. Lambda bodies and generator bodies are deferred; comprehension locals are not module dependencies. Live App and direct low-level generators are unchanged.

String/postponed annotation bindings (v0.12.0)

Availability: requires v0.12.0 or newer. Older versions, including v0.11.1, do not include missing_annotation_binding. Until v0.12.0 is available from your package index, use a source checkout containing this change.

Public assessment reports missing_annotation_binding for missing global roots in parsed string annotations and annotations postponed by an emitted future import. It checks the actual generated implementations, wrappers and owned support functions against bindings in the completed emitted module, not just those preceding a function. Names are sorted and deduplicated per tool/target. Annotation-only declarations do not create bindings. Projection and writes refuse before output, including dry-run and scaffold opt-in; inspection and body-recovery fields remain unchanged.

Type-position strings are parsed inside nested quotes, unions and recognized builtin/typing generics. Unknown/custom subscriptions and assignment aliases keep strings as values; their type-argument semantics are not certified. Imported typing/typing_extensions aliases for Literal and Annotated preserve value strings: Literal["red"] and Annotated[int, "label"] do not invent type dependencies. Strings in call arguments are values, not recursively parsed types. Analysis never evaluates the annotation expression; source detection still imports trusted user code and is not a sandbox.

This is name availability, not type resolution, alias-value validation or class recovery. Late framework registration/resolution, imported attributes, dynamic aliases, malformed or excessively nested annotation syntax and arbitrary expression behavior remain uncertified. An empty diagnostic list is not a guarantee that get_type_hints() or a framework succeeds.

Bounded helper recovery (v0.11)

Converters recover required undecorated functions and immutable literal constants from an inspected callable's owning module, including transitive helpers, ordinary imports, and recursive helper calls. Ordered support_definitions is separate from source_imports, immutable in ToolSchema, and detached in ToolInfo compatibility views. Generated output remains ordinary framework Python with no intpot runtime bridge.

Recovery reads the actual callable's globals and source file, verifies helper code and literal defaults against the live function, and rejects ambiguous or rebound bindings. It does not copy unrelated startup statements or app registration. Mutable globals, closures, decorated helpers, classes, dynamic defaults/global lookup, and helpers that write global/nonlocal state remain outside the supported subset. Unsupported dependencies still diagnose/refuse before writes; explicit scaffold permission does not bypass them. Conflicting support bindings and collisions with generated public names are refused. This is bounded source recovery, not purity analysis or arbitrary Python equivalence. Live App serving/ejection is unchanged; recovery currently applies to existing framework applications inspected by the CLI, API, and MCP inspectors.

Run uv run python examples/helper_recovery.py to exercise the helper chain returning 21 through generated CLI, HTTP API, and MCP consumers. examples/missing_binding.py retains a refusal control using a dynamically initialized module value rather than a recoverable literal. These capabilities are included in v0.11.0; the documented unsupported cases remain explicit.

Architecture

Both halves of intpot meet at one canonical ApplicationSchema. It owns immutable ToolSchema and ParameterSchema records. Existing ToolInfo and ParameterInfo objects remain detached compatibility views at the edges.

   @app.tool()                        source .py file
   (intpot.App)                    (Typer / FastMCP / FastAPI)
        |                                    |
        |                              1. DETECT
        |                              2. INSPECT
        |                                    |
        +-----------> ApplicationSchema <----+
                           ToolSchema[]
                              |
              +---------------+---------------+
              |                               |
        build a live                    3. GENERATE
      framework instance            (render a template)
              |                               |
       serve --cli/--api/--mcp        .py output on disk
                                    (to cli/mcp/api, eject)

Conversion follows four stages: detect the framework, inspect its tools, compile one immutable ApplicationSchema, then project target-specific semantics before rendering. The runtime side compiles @app.tool() registrations into the same schema. serve keeps the registered callables for live execution; eject renders detached views of the stable schema. Access .schema.to_dict() when a human, build tool, or agent needs to inspect exactly what Intpot compiled.

to_dict() is directly JSON-serializable. Every non-JSON-native default uses an unambiguous {"$intpot": {"type": ...}} envelope, preserving distinctions such as list versus tuple and bytes versus an ordinary dictionary. Enum and opaque defaults, as well as timezone-aware/folded temporal values, are rejected with TypeError because standalone generated code cannot preserve their semantics reliably.

Conversion examples

The repository includes checked-in source and generated output for every direction:

examples/ also contains advanced inputs with direct imports, async tools, request bodies, Depends(), and path parameters. The FastAPI dependency example remains inspectable, but API-to-CLI/MCP conversion intentionally refuses it until issue #20 is implemented. The FastAPI input also includes routes with multiple HTTP methods. See semantic_schema.py for a runnable canonical-schema inspection example. See callback_cli.py for executable source validation, callback inspection, and intentional conversion refusal.

CLI Reference

intpot --version (or -V) prints the installed version; intpot <command> --help works for any command below.

intpot serve

Serve an intpot App as CLI, API, or MCP server.

intpot serve <source> --cli|--api|--mcp [--host <host>] [--port <port>] [--] [args...]

Anything intpot doesn't recognise is forwarded, so your own options work as-is. Use -- when your app defines a flag intpot also defines:

intpot serve app.py --cli -- greet World --port 5

intpot inspect

Show the tools intpot extracts from a source, without generating anything. Useful for checking what a conversion will see before you run it.

intpot inspect <source> [--json] [--verbose]

$ intpot inspect mcp_server.py

Source: mcp_server.py (mcp)
┏━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━┓
┃ Name  ┃ Description            ┃ Parameters            ┃ Return Type ┃ Async ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━┩
│ greet │ Greet someone by name. │ name: str, greeting:  │ str         │ No    │
│       │                        │ str='Hello'           │             │       │
└───────┴────────────────────────┴───────────────────────┴─────────────┴───────┘

intpot eject

Export an intpot App as standalone framework code.

intpot eject <source> --to <cli|mcp|api> [--output <path>]

intpot init

Scaffold a new project from a template.

intpot init <name> --type <mcp|cli|api>

intpot to cli / to mcp / to api

Convert a source file — or every app in a directory — to the target framework. For a directory, the output mirrors the source tree: each generated file keeps its source's position, with only the filename changing.

intpot to cli <source> [--output <path>] [--dry-run] [--allow-scaffold] [--verbose]
intpot to mcp <source> [--output <path>] [--dry-run] [--allow-scaffold] [--verbose]
intpot to api <source> [--output <path>] [--dry-run] [--allow-scaffold] [--verbose]

All three take the same arguments:

to cli accepts MCP or API sources, to mcp accepts CLI or API, to api accepts CLI or MCP. A source that already matches the target is skipped.

--dry-run shows generated output without writing generated files, but it is not a sandbox: detection still imports the source and executes arbitrary module-level code. Only point intpot at source you trust:

$ intpot to cli mcp_server.py --dry-run
# --- Would generate: mcp_server_cli.py ---
"""CLI app generated by intpot."""
...

intpot add skills

Install intpot skills/rules for AI coding agents. Auto-detects which agents are configured in the project, or specify one explicitly.

intpot add skills [--agent <name>] [--path <dir>]

Supported agents and output locations:

Codex has to be asked for by name — intpot add skills --agent codex. It reads AGENTS.md, but so does nearly every other tool now, so the presence of that file says nothing about whether you use Codex. Since installing appends to it, guessing wrong would edit your own documentation. The same reasoning is why Copilot keys off .github/copilot-instructions.md rather than .github/, which only means the project is on GitHub.

Running with no --agent and no detected marker exits without writing anything. Copilot and Codex installations use bounded intpot-managed blocks, so rerunning the command updates or repairs intpot guidance while preserving surrounding project content. For Codex, intpot warns when the resulting AGENTS.md exceeds Codex's default 32 KiB instruction limit.

Community

The ModePot Discord is the shared community for intpot, summonpot, dexpot, and the rest of the project family. Join to discuss use cases, ask implementation questions, and help shape declaration-first Python frameworks.

Use GitHub issues for reproducible bugs and scoped feature proposals. Use Discord for open-ended design discussion, early ideas, and help applying the frameworks to real projects.

Contributing

intpot has a small core with clear extension points: inspectors turn frameworks into ToolInfo, generators turn ToolInfo into source, and runtime builders expose live interfaces. Contributions can improve conversion fidelity, add real-world examples, strengthen generated-code tests, or make the developer experience clearer.

• Start with a good first issue.
• Browse work where help is wanted.
• Read CONTRIBUTING.md for the pull request process and docs/reviewing.md for the contracts changes must preserve.

Open an issue before substantial public-contract work; focused fixes, tests, documentation, and maintenance may be direct pull requests. User-facing work uses an issue-backed or Towncrier-generated orphan changelog fragment—never a pull-request-number fragment.

Development setup

Requires uv.

git clone https://github.com/tugrulguner/intpot.git
cd intpot
uv sync --all-extras
uv run pre-commit install

Run the full check suite:

make check   # lint + typecheck + test

Individual targets:

make lint              # ruff check + format check
make typecheck         # pyright
make test              # pytest
make format            # auto-format code
make changelog-draft   # preview the next release section
make changelog         # assemble changelog.d/ into CHANGELOG.md (release only)

Changelog entries are written as one fragment file per PR in changelog.d/ rather than by editing CHANGELOG.md, and CI asks every PR for one. See changelog.d/README.md — it's short.

Roadmap

See ROADMAP.md for the current polish backlog and the planned full AST transform pipeline.

Support intpot

If intpot removes a duplicate interface from your project, consider starring the repository. Stars help other Python developers find the project, while issues and pull requests make it better.

License

MIT