name	rig-developer
description	Use when implementing or reviewing Rig itself, including changes to the CLI, runners, run artifacts, worktree isolation, MCP adapter, environment diagnostics, or agent harness documentation.

Rig Developer

Use this skill for changes inside the Rig repository.

Rig is a local AI coding harness, not an agent-to-agent protocol, workflow engine, PTY automation framework, or package manager for agent assets.
Keep the core model stable: Task -> Run -> AgentAdapter -> Artifacts.
Prefer declarative .rig/config.yaml agent definitions over vendor-specific Python branches.
Keep skills, hooks, prompts, and MCP client config as external assets that Rig can detect or document but should not silently install.

Preserve run artifacts: task.md, command.json, stdout.log, stderr.log, result.md, and status.json; worktree runs also write diff.patch.
Keep CLI and MCP behavior aligned. MCP tools should call the same run store, orchestrator, worktree, and artifact paths as the CLI.
Keep safety defaults explicit. RIG_MCP_ROOT constrains accepted cwd values, and RIG_MCP_ALLOW_APPLY=1 gates MCP patch application.
Use fake commands for tests. Real agent CLIs, credentials, or network access belong only in explicit opt-in smoke checks.
For new runners or prompt modes, add focused config parsing tests, CLI tests, artifact assertions, and docs examples.

Run from the repository checkout:

uv run pytest -q
uv run ruff check .
uv run mypy rig tests

Use uv run rig ... when manually exercising the CLI from this checkout.

name	rig-developer
description	Use when implementing or reviewing Rig itself, including changes to the CLI, runners, run artifacts, worktree isolation, MCP adapter, environment diagnostics, or agent harness documentation.

Use this skill for changes inside the Rig repository.

Rig is a local AI coding harness, not an agent-to-agent protocol, workflow engine, PTY automation framework, or package manager for agent assets.
Keep the core model stable: Task -> Run -> AgentAdapter -> Artifacts.
Prefer declarative .rig/config.yaml agent definitions over vendor-specific Python branches.
Keep skills, hooks, prompts, and MCP client config as external assets that Rig can detect or document but should not silently install.

Preserve run artifacts: task.md, command.json, stdout.log, stderr.log, result.md, and status.json; worktree runs also write diff.patch.
Keep CLI and MCP behavior aligned. MCP tools should call the same run store, orchestrator, worktree, and artifact paths as the CLI.
Keep safety defaults explicit. RIG_MCP_ROOT constrains accepted cwd values, and RIG_MCP_ALLOW_APPLY=1 gates MCP patch application.
Use fake commands for tests. Real agent CLIs, credentials, or network access belong only in explicit opt-in smoke checks.
For new runners or prompt modes, add focused config parsing tests, CLI tests, artifact assertions, and docs examples.

Run from the repository checkout:

uv run pytest -q
uv run ruff check .
uv run mypy rig tests

Use uv run rig ... when manually exercising the CLI from this checkout.

rig-developer