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SpAIder install wizard, agent management, and Claude Code skill installer

Project description

spaider-cli

The official command-line interface for SpAIder, the memory infrastructure for AI agents.

spaider-cli is the recommended way to install, manage, and integrate SpAIder with your AI coding tools (Claude Code, Cursor, and others). It takes you from "never heard of SpAIder" to "working Studio + MCP-integrated Claude Code with a memory skill loaded" in one command.

pipx install spaider-cli
spaider init

Status

v0.1.0. All commands implemented and working.

Command Status
spaider --version / --help
spaider init ✅ One-shot setup wizard
spaider doctor ✅ Read-only health audit
spaider agent create / list / rotate-key / delete
spaider mcp install ✅ Claude Code + Cursor

What it does

  1. spaider init: one command from clone to a working, MCP-integrated stack:

    • Detect Docker (CLI + daemon).
    • Prompt for the LLM provider (OpenAI / Anthropic / Ollama) and validate the key with a live probe.
    • Generate the secrets you shouldn't have to type (JWT, connector key, Neo4j password) and write .env. Re-run safe: existing secrets are preserved, never regenerated, so re-running never locks the stack out of its own database.
    • Start the Docker stack and wait for /health.
    • Provision a dev-${USER} agent (or --agent <name>) and capture its key.
    • Install the MCP server + skill into ~/.claude/ (idempotent merge).

    Flags: --provider, --llm-key, --llm-base-url, --skip-docker, --skip-mcp, --agent.

  2. spaider doctor: read-only audit covering Docker, .env, LLM key, backend /health, embedding-dimension consistency, and the ~/.claude MCP + skill wiring. Exit 0 when all green/warn, 1 on a blocking failure.

  3. spaider agent ...: create / list / rotate-key / delete agents against the REST API (resolves agents by name).

  4. spaider mcp install: non-destructively merge SpAIder into your MCP client config (--for claude-code | cursor, --scope user | project); also writes the agent-side skill file so LLMs reflexively know when to call SpAIder.

The skill file

A key part of the value: spaider mcp install ships not just the MCP server config but a skill file (~/.claude/skills/spaider.md) that tells the LLM when and why to use SpAIder's tools. Without it, MCP tools sit unused. With it, the LLM proactively reaches for SpAIder when the conversation warrants, exactly like Read/Edit/Bash are reflexive in Claude Code today.

Source content is at src/spaider_cli/skills/claude_code.md (Apache 2.0); copy and adapt for your own tools if useful.

Development

git clone https://github.com/Spaider-studio/spaider.git
cd spaider/cli
pip install -e ".[dev]"
pytest

Or from the SpAIder monorepo root:

make cli-dev    # editable install of the CLI
make cli-test   # run cli/tests/

Release (maintainers only)

CI runs as the cli-tests job of .github/workflows/ci.yml (every PR + push to main that touches cli/); releases run from .github/workflows/cli-release.yml on tag push.

One-time setup before the first PyPI release:

  1. Create a PyPI account for the project.

  2. Register a Trusted Publisher (pending publisher) for spaider-cli:

    • PyPI Project → Publishing → Add a new pending publisher.
    • Owner: Spaider-studio, repo: spaider, workflow: cli-release.yml, environment: pypi.
    • GitHub: Settings → Environments → Create environment named pypi (no protection rules needed for the first release).
  3. Tag and push (CLI releases use namespaced cli/v* tags; plain v* tags release the container images instead):

    git tag cli/v0.1.0
    git push origin cli/v0.1.0
    

    The workflow builds sdist + wheel, runs twine check, then publishes via the OIDC trusted-publisher flow (no API tokens stored in the repo).

To rehearse without publishing, use the manual workflow_dispatch trigger with dry_run: true: it builds the artifacts but skips PyPI.

License

Apache 2.0. See LICENSE.

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