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fabdendev

Ghost Writer MCP

by fabdendev
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Python
Hybrid

Ghost Writer MCP

An MCP server that scans your Git repositories, identifies interesting engineering work, and generates LinkedIn post drafts — with built-in confidentiality sanitisation.

What it does

Your repos → scan activity → classify by content potential → generate draft → sanitise → review

  1. Scan — reads git log from local repo clones (zero API calls, instant)

  2. Aggregate — groups commits by conventional-commit prefix and clusters related work

  3. Classify — LLM ranks groups by content potential, assigns pillars and angles

  4. Generate — LLM writes a LinkedIn draft in your chosen format (war story, hot take, tactical howto, TIL, deep dive)

  5. Sanitise — three-gate safety: regex blocklist → LLM review → human review

Quick start

Prerequisites

  • Python 3.12+

  • uv (recommended) or pip

  • An LLM provider: Ollama (free, local) or an Anthropic API key

  • Local clones of the repos you want to scan

Install

Option A — clone and run (recommended for customisation):

git clone https://github.com/fabdendev/ghost-writer-mcp.git
cd ghost-writer-mcp
uv sync

Option B — run directly with uvx (no clone needed):

uvx ghost-writer-mcp

Configure

cp config.example.yaml config.yaml

Edit config.yaml with your repos, blocklist, and LLM settings. See config.example.yaml for a fully documented template.

For Ollama (free, local):

llm:
  provider: ollama
  base_url: "http://localhost:11434/v1"
  classifier_model: qwen3:8b
  generator_model: qwen3:8b
  api_key: "ollama"
ollama pull qwen3:8b
ollama serve

For Anthropic (cloud):

llm:
  provider: anthropic
  classifier_model: claude-haiku-4-5-20251001
  generator_model: claude-haiku-4-5-20251001
  api_key: "${ANTHROPIC_API_KEY}"
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env

Add to Claude Code

Add to your Claude Code MCP settings (~/.claude/settings.json):

If installed from clone:

{
  "mcpServers": {
    "ghost-writer": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/ghost-writer-mcp", "fastmcp", "run", "src/server.py"]
    }
  }
}

If using uvx:

{
  "mcpServers": {
    "ghost-writer": {
      "command": "uvx",
      "args": ["ghost-writer-mcp"]
    }
  }
}

Use via MCP

From Claude Code (or any MCP client):

scan_activity                          # scan all configured repos (last 7 days)
scan_activity(repo="my-project")       # scan a single repo
scan_activity(days=30)                 # look back 30 days
generate_draft(activity_index=1)       # generate from top candidate
generate_draft(3, format="hot_take")   # override format
edit_draft(1, "make it shorter")       # refine with natural language
list_drafts(status="pending")          # see saved drafts

Use via CLI

You can also test without an MCP client:

uv run python -m src scan --days 14                    # scan all repos
uv run python -m src scan --repo my-project            # scan one repo
uv run python -m src generate 1                        # draft from top result
uv run python -m src generate 3 --format hot_take      # override format
uv run python -m src list                              # list saved drafts

Tools

Tool

Description

scan_activity

Scan repos, aggregate commits, classify and rank by content potential

generate_draft

Generate a LinkedIn draft from a classified activity

edit_draft

Refine a draft with natural language instructions

list_drafts

List saved drafts, optionally filtered by status

Scanning modes

Ghost Writer supports two scanning backends:

  • Local git (default) — reads git log from local clones. Zero API calls, instant results. Set local_path on each repo in your config.

  • GitHub API (fallback) — used automatically for repos without local_path. Requires a GitHub token (github.token in config). Fetches up to 30 commits and 20 merged PRs per repo.

You can mix both: some repos with local clones, others via API.

Confidentiality

Ghost Writer uses three safety gates to prevent leaking sensitive information:

  • Gate 1 — Blocklist: regex-based detection and replacement of company names, client names, product names, infrastructure details, and people names

  • Gate 2 — LLM Review: the LLM scans generated text for anything that looks confidential and flags it

  • Gate 3 — Human Review: drafts are saved as pending — you always get the final say

Configure your blocklist and abstractions in config.yaml:

sanitisation:
  blocklist:
    company_names: ["Acme Corp"]
    client_names: ["Big Client"]
    product_names: ["internal-tool"]
    infrastructure: ["prod-db-01.internal"]
    people: ["John Doe"]
  abstractions:
    "Acme Corp": "a mid-size tech company"
    "internal-tool": "an internal platform"

Content pillars

Define what topics you want to post about. The classifier maps activities to pillars:

content:
  pillars:
    - name: ai_engineering
      description: "Building AI agents, LLM integration, prompt engineering"
      repo_signals: ["agent", "llm", "prompt"]
      weight: 1.0
    - name: data_architecture
      description: "Data pipelines, ETL, event-driven systems"
      repo_signals: ["pipeline", "etl", "kafka"]
      weight: 0.8

Post formats

Format

Description

tactical_howto

Problem → 3-5 concrete steps → takeaway

hot_take

Contrarian claim backed by one specific thing you built

war_story

What broke, what you tried, what worked, the lesson

til

One surprising thing you learned, under 500 chars

deep_dive

3-4 sections with trade-offs and alternatives

Architecture

src/
├── server.py              # FastMCP server (4 tools)
├── cli.py                 # Standalone CLI for testing without MCP
├── config.py              # Pydantic config with env/shell resolution
├── llm_client.py          # Unified Anthropic + OpenAI-compatible client
├── scanner/
│   ├── local_git.py       # Git CLI scanner (primary)
│   ├── github_client.py   # GitHub API scanner (alternative)
│   ├── aggregator.py      # Commit grouping and clustering
│   └── activity.py        # ActivityItem dataclass
├── content/
│   ├── classifier.py      # LLM-based content scoring
│   ├── generator.py       # Draft generation with sanitisation
│   ├── abstractor.py      # Two-gate confidentiality layer
│   └── prompts/           # System prompts (classifier, generator, reviewer)
└── store/
    ├── database.py         # SQLite persistence
    └── blocklist.py        # Regex-based blocklist

Development

uv sync --extra dev
uv run pytest                 # run tests
uv run ruff check src/ tests/ # lint

License

MIT

This server cannot be installed

A
license - permissive license
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quality - not tested
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