AI Workstation Open Source Intelligence

A distribution layer for researching, verifying, comparing and composing open-source AI projects with explicit evidence and uncertainty boundaries.

Status

M1 Alpha / pre-release. The repository contains:

• three complete Skill workflows;

• a validated skills-only .codex-plugin/plugin.json package;

• a repo-scoped local plugin marketplace;

• six read-only project-intelligence tools;

• a transport-neutral Python core;

• a deterministic offline mock provider;

• a fail-closed HTTP provider for AI Workstation's public Radar API;

• an MCP Python SDK v2 stdio server with server-wide safety instructions;

• a live public-contract probe, sanitized fixture capture and offline fixture validator;

• machine-readable schemas, bilingual evaluations and automated tests;

• Codex CLI, plugin and project-scoped MCP configuration guidance.

This is not yet a hosted public MCP service or a universal-directory release. The live HTTP provider is opt-in and must pass production contract validation before an external release.

Related MCP server: pharma-assistant-mcp

Product boundaries

Every tool result separates:

1. verified source facts;

2. analysis and recommendations;

3. unknown or unverified information;

4. risks and limitations.

The first release is read-only. It does not execute repository code, mutate GitHub projects, save collections, authenticate users or process payments.

First Skills

• open-source-project-research

• open-source-project-comparison

• open-source-stack-planner

First tools

• search_ai_projects

• get_project_facts

• get_license_evidence

• compare_ai_projects

• find_alternatives

• compose_ai_stack

Repository layout

.
├── .codex-plugin/plugin.json   installable skills-only plugin manifest
├── .agents/plugins/            repo-scoped local marketplace
├── skills/                     reusable Skill workflows
├── src/aiworkstation_osi/      core, providers, MCP, probe and capture tools
├── schemas/                    input manifest and unified result schema
├── evals/                      bilingual evaluation cases
├── tests/                      unit, package, provider and MCP tests
├── examples/                   local invocation and Codex config examples
├── docs/                       architecture, packaging and validation runbooks
├── AGENTS.md
├── SECURITY.md
└── PRIVACY.md

Local setup

python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[mcp]"
python -m unittest discover -s tests -v

Install the local Skills plugin

The repository root is a skills-only plugin package. Register its marketplace:

codex plugin marketplace add zxhwolfe-dev/aiworkstation-open-source-intelligence --ref main
codex plugin marketplace list

For a local clone:

codex plugin marketplace add /ABSOLUTE/PATH/TO/aiworkstation-open-source-intelligence

Restart the ChatGPT desktop app, open the Plugins Directory, choose AI Workstation Local Plugins, and install AI Open Source Intelligence.

The plugin package currently installs the three Skills only. It deliberately does not claim a bundled or registered MCP connection yet. Connect the local stdio MCP server separately until a portable bundled command or registered hosted MCP endpoint is ready.

See docs/plugin-packaging.md.

Offline mock usage

The default provider performs no network access:

osi-m0 provider-info
osi-m0 list-tools
osi-m0 invoke search_ai_projects \
  --arguments '{"query":"self-hosted RAG with Docker and web UI"}'

The returned MOCK_DATA risk is intentional.

Public Radar HTTP provider

Enable the public AI Workstation adapter explicitly:

export OSI_PROVIDER=http
export AIWORKSTATION_RADAR_BASE_URL=https://aiworkstation.cn
export OSI_HTTP_TIMEOUT_SECONDS=30
export OSI_HYDRATE_LIMIT=5

osi-m0 provider-info
osi-m0 invoke get_project_facts \
  --arguments '{"project_id":"infiniflow/ragflow","locale":"en"}'

The adapter:

• uses public read endpoints only;

• requires public snapshot identity for project facts;

• rejects mixed-snapshot comparisons;

• requires selector evidence status to be available or disclosed partial;

• keeps near matches outside formal recommendations;

• converts missing and sentinel license values to explicit unknowns;

• flags non-standard license labels for manual review;

• never imports private akaiagents modules.

MCP server

Run a local stdio MCP server for Codex or another MCP host:

OSI_PROVIDER=mock osi-mcp

Use the public HTTP provider:

OSI_PROVIDER=http \
AIWORKSTATION_RADAR_BASE_URL=https://aiworkstation.cn \
osi-mcp

The server uses the MCP Python SDK v2 line and exposes only the six declared read-only tools. Its server instructions lead with the fact/recommendation boundary, prohibition on repository execution, license caveat and recommended tool sequence. Tests connect in memory without opening a subprocess or port.

For Codex setup, see docs/codex-setup.md and examples/codex-config.toml.

Validate the public contract

Run both language probes:

osi-probe --base-url https://aiworkstation.cn --locale en
osi-probe --base-url https://aiworkstation.cn --locale zh

Capture sanitized response shapes for review:

osi-capture-contracts \
  --base-url https://aiworkstation.cn \
  --locale en \
  --project-id infiniflow/ragflow \
  --output-dir tmp/public-validation/contracts-en

osi-validate-contracts \
  --directory tmp/public-validation/contracts-en

The capture stores four public response fixtures and a manifest. It removes queries, credentials, client IDs and internal publication fields; bounds long strings and lists; and records query fingerprints instead of query text. The offline validator checks identity, snapshot, evidence, no-match and near-match contracts before manual review.

Follow docs/production-validation.md before inviting external testers.

Architecture

User / plugin or MCP host
     |
Three packaged Skills
     |
MCP Python SDK v2 stdio server
     |
Transport-neutral ToolRegistry
     |
Mock provider OR hardened AI Workstation HTTP provider
     |
Current healthy validated Radar release

zxhwolfe-dev/akaiagents remains a read-only reference and private data production system. This repository integrates through explicit public HTTP contracts rather than importing its private Python modules.

See:

• docs/architecture.md

• docs/m1-alpha.md

• docs/plugin-packaging.md

• docs/akaiagents-integration.md

• docs/codex-setup.md

• docs/production-validation.md

• schemas/tool-manifest.json

• schemas/tool-result.schema.json

• docs/security-and-privacy.md

• docs/error-codes.md

Remaining M1 validation

1. Pull main and run the complete local suite.

2. Observe successful GitHub Actions runs on Python 3.10 and 3.12.

3. Install and test the local Skills plugin through the repo marketplace.

4. Run English and Chinese production probes.

5. Capture, validate and manually review sanitized production fixtures.

6. Confirm exact transparency, observation-time and license-evidence fields.

7. Test the stdio server from Codex with the project-scoped configuration.

8. Add plugin MCP wiring only after a portable bundled server or registered hosted MCP connection exists.

9. Add Streamable HTTP only after host allowlists, authentication, rate limiting and deployment controls are designed.

License

No open-source license has been granted yet. The repository is public for pre-release inspection and development; reuse rights will be defined before the first public package release.