insaight

mcp
Security Audit
Warn
Health Warn
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 5 GitHub stars
Code Warn
  • network request — Outbound network request in insaight/mcp_server.py
Permissions Pass
  • Permissions — No dangerous permissions requested

No AI report is available for this listing yet.

SUMMARY

LinkedIn prospect intelligence inside Claude — MCP server + 8 skills that research people, companies and comment threads, draft outreach, and learn what gets replies.

README.md

insaight

LinkedIn prospect intelligence inside Claude — it automates the research, not the outreach.

MIT License
Tests
Python 3.11+

insaight: research a company, rank who to pitch, classify what they post about, find the hook

Insaight scrapes public LinkedIn data via Apify, stores it in local SQLite, and hands it to Claude through an MCP server and eight skills. Data flows in once, then stays on your machine — repeat questions hit SQLite, not Apify.

Architecture

graph LR
    C["Claude Code / Desktop<br/>8 skills"] -->|MCP| S["insaight server<br/>18 tools"]
    S --> DB[("SQLite<br/>~/.insaight/posts.db")]
    S --> M["memory/<br/>style.md · playbook.md"]
    S -->|fresh scrapes only| A["Apify<br/>LinkedIn actors"]

Install in 30 seconds

Prerequisite: uv and Python 3.11+. The plugin runs the MCP server with uvx, so without uv the tools never load.

curl -LsSf https://astral.sh/uv/install.sh | sh   # skip if you already have uv

Then, in Claude Code:

/plugin marketplace add spirosbax/insaight
/plugin install insaight@insaight

Add your Apify token (free tier works):

mkdir -p ~/.insaight && echo "APIFY_API_TOKEN=apify_api_..." >> ~/.insaight/.env

Restart Claude Code and say "research Anthropic on LinkedIn". The plugin registers the MCP server and installs all eight skills; there is nothing to clone.

On the very first run, uvx builds the server before it answers — give it a few seconds. If the insaight tools never appear, check that uv is on your PATH.

Claude Desktop

Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "insaight": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/spirosbax/insaight", "insaight"],
      "env": { "APIFY_API_TOKEN": "apify_api_..." }
    }
  }
}

Restart Claude Desktop, then add the skills under Settings → Skills → Add skills, selecting the SKILL.md files from this repo's skills/ directory.

Developer setup
git clone https://github.com/spirosbax/insaight.git && cd insaight
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
pytest -q                                                      # hermetic — temp SQLite, no credentials
claude mcp add insaight -s user -- "$PWD/.venv/bin/insaight"   # local checkout instead of uvx

A checkout with a data/ directory uses it as INSAIGHT_HOME, keeping the dev database inside the repo (gitignored).

Skills

Eight skills that chain conversationally — each one's output feeds the next. They are plain Markdown with YAML frontmatter: easy to read, fork, and customize.

Skill One line
research-person Intelligence brief on an individual: themes, decision-maker signals, outreach hooks, uncommon commonalities
research-company Company analysis from company posts + up to 3 C-level executives' posts, with a prospect score
research-post Mine a post's comment thread for warm leads, decision-makers, and competitor mentions
draft-outreach Cold DM + email, two variants each, using prior research + your learned style memory
draft-post LinkedIn post in your company's voice, styled on your own past posts (URL-to-post supported)
track-outreach Log sends and outcomes in the local ledger ("I sent it", "she replied", "mark as ghosted")
reflect Analyze outcomes, propose evidence-backed memory updates — applied only on your approval
save-notion Persist research briefs to your configured Notion page (optional, needs the Notion MCP)
prospecting     research company → draft outreach → save to Notion
person-first    research person  → draft outreach
qualification   research company → read the prospect evaluation → pursue or pass
What it actually looks like in the terminal

a real Claude Code session using insaight

An unedited Claude Code session: install, research Anthropic, find the right person, draft the DM, log the send.

The memory loop

draft → send → "I sent it"          → logged (log_outreach)
       → "she replied" / "ghosted"  → outcome recorded (record_outcome)
       → every N outcomes           → reflection proposed (default 10; REFLECT_EVERY)
       → you approve                → style.md + playbook.md updated

Outcomes are logged because you say so — Insaight never reads your inbox. Every playbook claim carries its evidence ("question hooks: 4/9 replied vs statement hooks: 1/8"), and below n=10 a pattern is a hypothesis, not a rule. Nothing is written to memory without your approval. The ledger also powers prior-contact warnings ("you messaged this person 3 weeks ago — ghosted") whenever you research or draft.

MCP tool reference (18 tools)
Tool Purpose
list_accounts Discover tracked companies and personal profiles
scrape_profile Fetch fresh posts for any LinkedIn URL (Apify)
scrape_people Fetch company employees and leadership (Apify, Short or Full mode)
scrape_person_profile Enrich one person with full profile: experience, education, skills, volunteer, languages
list_posts Token-cheap index: metadata + 150-char snippet
get_posts Full content for selected posts by URN (max 20 per call)
search_posts Full-text keyword search across stored posts
list_people Query stored employees/leadership (instant, free)
scrape_post_comments Fetch a post's comment thread with author info (Apify)
list_comments Query stored comments for a post, ranked by likes
get_stats Database overview: counts, date range, categories
log_outreach Record a sent message in the ledger (flags prior contact)
record_outcome Record replied / positive / meeting / ghosted; flags when reflection is due
list_outreach Query the ledger: prior-contact checks, pending sends, history
get_outreach_stats Reply-rate breakdown by hook type, variant, and channel
get_memory Read the learned style guide + strategy playbook
update_memory Rewrite a memory file (only after an approved reflection)
get_config Read your Notion pages + company config from ~/.insaight/config.md (creates it with placeholders on first call)

Reading pattern: list_posts returns ~80 tokens per post; scan snippets, then get_posts only the interesting ones.

Where data lives

Everything is under ~/.insaight/ (override with INSAIGHT_HOME):

~/.insaight/
  .env         APIFY_API_TOKEN, ANTHROPIC_API_KEY (optional), REFLECT_EVERY
  config.md    Notion pages + company config (read by get_config)
  posts.db     SQLite: posts, people, comments, outreach ledger
  memory/      style.md + playbook.md (written by the reflect skill)
Apify actors & costs
Actor Scrapes Approx. cost
harvestapi/linkedin-profile-posts Company or personal posts ~$1.50 / 1k posts
harvestapi/linkedin-company-employees Employees and leadership ~$4 / 1k (Short), ~$8 / 1k (Full)
harvestapi/linkedin-profile-scraper Single-profile enrichment $4 / 1k ($10 / 1k with email search)
harvestapi/linkedin-post-comments Comment threads see actor page

Rates as published at time of writing — check the actor pages for current pricing.

CLI

A standalone CLI for batch work outside Claude (insaight-cli in a dev install, or uvx --from git+https://github.com/spirosbax/insaight insaight-cli):

insaight-cli scrape --accounts config/accounts.txt   # scrape tracked accounts (--no-categorize skips the Anthropic API)
insaight-cli stats                                   # database overview
insaight-cli export --format csv --output posts.csv  # export to CSV or JSON

Data, privacy & terms

Everything stays local: posts, people, the outreach ledger, and learned memory live in SQLite and Markdown on your machine, and nothing is sent anywhere except your own Apify/Anthropic/Notion accounts. No inbox access — outcomes exist because you reported them. Insaight fetches public LinkedIn data through third-party Apify actors; automated collection may conflict with LinkedIn's Terms of Service, and you are responsible for how you use this tool. Keep volumes reasonable and respect the people behind the profiles.

License

MIT

Reviews (0)

No results found