Digital Rain

Digital Rain is a privacy-first, read-only repo intelligence tool for AI coding agents. Point it at a local codebase and it returns a grounded brief: what the repo is, what fits it, what to install next, and what to watch before you scale the workflow.

What it does

• Scans a local repo for languages, frameworks, package managers, notebooks, and launch signals

• Recommends relevant MCP servers, open-source repos, research papers, models, and market references

• Exposes a local MCP server under the public label digital-rain-mcp

• Previews editor config scaffolding before any write and only writes when explicitly allowed

• Runs as a local web app, a desktop companion, or a stdio MCP server

Related MCP server: LocalNest MCP

What it does not do

• No auto-installs

• No arbitrary repo edits

• No hidden file mutation

• No required cloud account for core scans, search, or deterministic recommendations

Why local, why read-only

Digital Rain is built for teams that want repo intelligence without shipping source code to a hosted analyzer by default. The scanner reads the workspace on disk, the vault stays local, and the MCP surface is read-only unless you explicitly opt into narrow editor-config scaffolding with write=true.

Try it now

macOS / Linux

git clone https://github.com/SSX360/digital-rain.git
cd digital-rain
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
python -m pip install .[mcp]
python ingest.py --seed

Run the dashboard:

source .venv/bin/activate
python app.py

Run the MCP server:

source .venv/bin/activate
python mcp_server.py

Windows (PowerShell)

git clone https://github.com/SSX360/digital-rain.git
cd digital-rain
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -r requirements.txt
python -m pip install .[mcp]
python ingest.py --seed

Run the dashboard:

.\.venv\Scripts\Activate.ps1
python app.py

Run the MCP server:

.\.venv\Scripts\Activate.ps1
python mcp_server.py

Open the printed local URL, pick a folder in Active Project, and ask for a scan or recommendations. If you only want the MCP server, stop after the install path and start python mcp_server.py.

MCP setup

Use digital-rain-mcp for every new install. Replace the placeholder paths once, then paste the block into your editor config.

Cursor: .cursor/mcp.json

{
  "mcpServers": {
    "digital-rain-mcp": {
      "type": "stdio",
      "command": "<ABSOLUTE_PATH_TO_DIGITAL_RAIN_VENV_PYTHON>",
      "args": [
        "<ABSOLUTE_PATH_TO_DIGITAL_RAIN>/mcp_server.py"
      ],
      "env": {
        "COPILOT_WORKSPACE": "${workspaceFolder}"
      }
    }
  }
}

VS Code: .vscode/mcp.json

{
  "mcpServers": {
    "digital-rain-mcp": {
      "type": "stdio",
      "command": "<ABSOLUTE_PATH_TO_DIGITAL_RAIN_VENV_PYTHON>",
      "args": [
        "<ABSOLUTE_PATH_TO_DIGITAL_RAIN>/mcp_server.py"
      ],
      "env": {
        "COPILOT_WORKSPACE": "${workspaceFolder}"
      }
    }
  }
}

Claude Desktop: config file

{
  "mcpServers": {
    "digital-rain-mcp": {
      "type": "stdio",
      "command": "<ABSOLUTE_PATH_TO_DIGITAL_RAIN_VENV_PYTHON>",
      "args": [
        "<ABSOLUTE_PATH_TO_DIGITAL_RAIN>/mcp_server.py"
      ],
      "env": {
        "COPILOT_WORKSPACE": "<ABSOLUTE_PATH_TO_TARGET_REPO>"
      }
    }
  }
}

Path note:

• macOS / Linux venv Python usually looks like /absolute/path/to/digital-rain/.venv/bin/python

• Windows venv Python usually looks like C:\\absolute\\path\\to\\digital-rain\\.venv\\Scripts\\python.exe

Compatibility note:

• If you previously used matrixscroll-mcp or cursor-copilot, keep that only as a temporary compatibility shim. New installs should use digital-rain-mcp.

Point it at a real repo

The proof assets below come from real runs against this repo and the public trust surfaces it audits.

Repo input: digital-rain

Observed repo grounding

• Languages: python, html

• Frameworks: flask, static-site

• Notable SDKs: anthropic, vercel, numpy, pandas, pytorch

• Launch readiness: ready

Observed recommendations

• GitHub MCP Server

• Playwright MCP

• Context7

• Sigstore Python

Observed trust audit

• "Detected public site trust surface. 8 proof link(s) surfaced. No legacy naming found in the audited surface."

Observed config preview

• scaffold_editor_integration(..., write=false) returns a diff preview for .cursor/mcp.json

• scaffold_editor_integration(..., write=true) is the only narrow write path and is meant for explicit config scaffolding only

Proof assets

These images are reused from the current demo output rather than mocked marketing art.

Tool surface

Repo grounding

• analyze_workspace

• brainstorm_workspace

• benchmark_openhuman

Trust and rollout

• audit_trust_surface

• scaffold_editor_integration

• plan_repo_rollout

Compatibility note:

• plan_matrixscroll_rollout still ships as a legacy compatibility alias so existing MCP clients do not break during the rename.

Ecosystem intelligence

• recommend_ecosystem

• build_usecase_blueprint

• scan_research_radar

• scan_market_radar

Local app

Digital Rain runs as:

• A local web dashboard via python app.py

• A floating desktop companion via python desktop_launcher.py or run_desktop_companion.bat

• Local HTTP APIs for scripts and QA

• A stdio MCP server via python mcp_server.py

The backend binds to 127.0.0.1 only by default.

Desktop companion

macOS / Linux

source .venv/bin/activate
python desktop_launcher.py

Windows

run_desktop_companion.bat

The launcher starts or reuses the Flask backend on port 59712, waits for /api/health, then launches the transparent companion. It uses the active workspace pointer from the dashboard or COPILOT_WORKSPACE when set.

Project intelligence APIs

curl -s http://127.0.0.1:59712/api/health
curl -s http://127.0.0.1:59712/api/diagnostics
curl -s http://127.0.0.1:59712/api/project/status
curl -s "http://127.0.0.1:59712/api/ecosystem/recommendations?goal=browser%20testing"

Invoke-RestMethod -Uri "http://127.0.0.1:59712/api/health"
Invoke-RestMethod -Uri "http://127.0.0.1:59712/api/diagnostics"
Invoke-RestMethod -Uri "http://127.0.0.1:59712/api/project/status"
Invoke-RestMethod -Uri "http://127.0.0.1:59712/api/ecosystem/recommendations?goal=browser%20testing"
Invoke-RestMethod -Uri "http://127.0.0.1:59712/api/research/radar?goal=agent%20memory"
Invoke-RestMethod -Uri "http://127.0.0.1:59712/api/market/radar?goal=agent%20memory"
Invoke-RestMethod -Uri "http://127.0.0.1:59712/api/usecase/blueprint?goal=agent%20memory"
Invoke-RestMethod -Uri "http://127.0.0.1:59712/api/benchmark/openhuman"

Useful local functions for scripts:

from digital_rain_core import (
    analyze_workspace,
    brainstorm_workspace,
    recommend_ecosystem,
    build_usecase_blueprint,
)

profile = analyze_workspace("/path/to/project")
ideas = brainstorm_workspace("/path/to/project", goal="improve onboarding")
recommendations = recommend_ecosystem("/path/to/project", goal="browser testing")
blueprint = build_usecase_blueprint("/path/to/project", goal="launch an agent workflow debugger")

Use Case Lab

The dashboard's Use Case Lab compiles a goal into a read-only blueprint:

• Power Stack: what to build locally, what to integrate, and what to monitor

• Patterns to Borrow: public product, launch, and OSS patterns worth adapting

• Build vs Integrate: explicit recommendations for custom code versus mature dependencies

• Research Edge: relevant arXiv papers and Hugging Face model candidates

• Market Proof: Uneed, MicroLaunch, DevHunt, and Hacker News signals

• Next Implementation Steps: concrete actions for the active repo

The market radar uses public pages and the official Hacker News API where possible, with deterministic seed fallbacks when a source is unavailable.

Research Radar

Digital Rain tracks academic and model-release context in addition to open-source repo signals:

• Academic paper lookup through the arXiv API, with cached results and seed fallbacks

• Hugging Face model discovery using public Hub search metadata

• Topic selection from the active project stack, SDKs, notebooks, and user goal

• Local cache under ~/.digital-rain/cache

Public sources:

• https://info.arxiv.org/help/api/user-manual.html

• https://huggingface.co/docs/huggingface_hub/en/guides/search

• https://huggingface.co/docs/hub/en/models-the-hub

Ecosystem Ranking

Digital Rain combines local project facts with public open-source signals:

• Stack fit from detected languages, frameworks, SDKs, package managers, and goal terms

• Curated seed categories for MCP servers, coding-agent skills, RAG, memory, and vector database projects

• Cached OSSInsight and GitHub metrics such as stars, forks, issue pressure, contributors, recent growth, and freshness

• Install complexity penalties for heavier repository adoption

• Plain-language reasons and "why now" explanations

Metrics are cached under ~/.digital-rain/cache by default. Set OSS_INSIGHT_CACHE_TTL or DIGITAL_RAIN_CACHE_DIR to change cache behavior.

Privacy

Local project scanning stays on your machine. Digital Rain reads manifests, configs, README excerpts, notebooks, and local notes to build the project profile. OSSInsight and GitHub enrichment uses public repository names from the curated seed list; it does not upload your local source code.

Diagnostics redact secret-like environment keys and project metadata before returning support bundles. Market and research requests use public query terms and public source metadata; local source files are not uploaded to launch directories, HN, OSSInsight, GitHub, arXiv, or Hugging Face.

Safety tip:

• Review the source and run in a sandbox or VM if you are evaluating an unfamiliar checkout.

Refresh data

python ingest.py --seed
python ingest.py --catalog-only
python ingest.py --no-catalog

The local index powers documentation search and catalog matching. Ecosystem recommendations also work from built-in seeds when the network is unavailable.

QA

Automated tests (tests/, CI: .github/workflows/test.yml):

python -m pytest tests/ -q
python -m py_compile app.py scanner.py brainstorm.py workspace_config.py search.py oss_insight.py research_radar.py market_radar.py usecase_synthesizer.py digital_rain_core.py immersive_web.py desktop_launcher.py

Manual demo scripts:

• docs/QA_DEMO.md

• docs/QA_DEMO_3D_METAVERSE.md

• docs/MCP_FULL_SURFACE_DEMO.md

For release evidence:

python -m qa.run_gates --workspace empty --report out/qa-report.json
python -m qa.release_evidence --output-dir out/release-evidence

Optional signed-provenance upgrade

If local repo intelligence is no longer enough and you need signed commit-time provenance, Digital Rain can feed a stricter trust layer later. That upgrade path lives outside this repo: matrixscroll.com.

License

Apache License 2.0. See LICENSE.

Configuration

Per-project settings live in .cursor/co-pilot.json for compatibility with existing installations. The file controls notebook limits, vault mode, and brainstorm preferences.