Maggy logo

Maggy

Community
alinaqi
maggy

Maggy is a local AI engineering command center. AI-prioritized inbox across issue trackers (GitHub Issues/Asana), one-click TDD execute with iCPG context enrichment, daily competitor intelligence briefing.

Overview

Publisheralinaqi
Repositorymaggy
Skill namemaggy
Stars
707
Forks
57
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by alinaqi on GitHub. Read the source before you install it.

Installation

Install the Maggy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/alinaqi/maggy/tree/main/skills/maggy
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/alinaqi/maggy.git /tmp/maggy
mkdir -p .claude/skills
cp -r /tmp/maggy/skills/maggy .claude/skills/maggy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maggy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Maggy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Maggy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Maggy Skill

Maggy is a generic, local AI engineering command center. Install once, point it at your team's issue tracker and codebases, and get:

  • AI-prioritized inbox — ranks open issues by urgency, OKR alignment, and recency
  • One-click Execute — spawns Claude Code locally with iCPG context injected
  • Competitor intelligence — daily AI briefing on your competitive landscape
  • No hardcoding — works for any team, any stack, any issue tracker

⚠️ Execute permission model (important)

Execute currently runs claude -p --dangerously-skip-permissions so the TDD pipeline isn't blocked waiting on approval prompts (subprocess has no terminal). That flag grants Claude full permission to write/edit files and run shell commands inside the target codebase, and the prompt it receives includes content from the issue tracker (which any team member can author).

Hardening already in place:

  • working_dir is validated against the list of codebase roots in ~/.maggy/config.yaml — Claude can't be pointed at arbitrary filesystem paths.
  • Only tickets from your configured trackers reach Execute; no public-internet input flows into the prompt.

Roadmap: move the unconditional flag behind per-codebase config (auto_approve: true|false) so privileged execution becomes opt-in. Until then, treat Execute like git pull && make on any ticket you push the button for — only run it on repos you own, against tickets from authors you trust.

┌──────────────────────────────────────────────────────────────┐
│  maggy               ──────────────┐                          │
│  ├── skills/         ← installed globally → ~/.claude/       │
│  ├── commands/       ← installed globally → ~/.claude/       │
│  ├── scripts/icpg/   ← used by Maggy for context enrichment  │
│  └── maggy/          ← dashboard: run `./install.sh` to use  │
│      ├── src/                                                │
│      │   ├── providers/   ← GitHub / Asana / Linear          │
│      │   ├── services/    ← inbox, competitor, executor      │
│      │   └── api/         ← FastAPI routes                   │
│      └── install.sh                                          │
└──────────────────────────────────────────────────────────────┘

When Maggy Helps

ScenarioHow Maggy helps
Morning triage of 50 open issuesAI ranks them; top items stay top
Implementing a ticketExecute → iCPG-enriched TDD pipeline
"What are competitors shipping?"Daily briefing + filterable news feed
Multiple repos per teamAuto-picks right repo based on ticket content
New team onboardingConfigure via /maggy-init, no code writing

Install and Configure

bash
# One-time install
cd $(cat ~/.claude/.bootstrap-dir)/maggy
./install.sh

# Configure
# Edit ~/.maggy/config.yaml — see maggy/config.example.yaml for the schema

# Credentials
export GITHUB_TOKEN=ghp_...
export ANTHROPIC_API_KEY=sk-ant-...

# Run
python3 -m src.main

# Or from Claude Code:
#   /maggy-init    # interactive wizard
#   /maggy         # launch dashboard

Provider Abstraction

Maggy services never see GitHub/Asana directly — they talk to an IssueTrackerProvider Protocol. Drop-in swap between:

  • GitHubIssuesProvider — scans multiple repos, aggregates open issues, maps "done" → closed
  • AsanaProvider — queries projects, respects workspace scope
  • LinearProvider — stub for future

The same inbox, Execute pipeline, and Competitor features work with any provider.


Execute Pipeline

When you click Execute on a ticket:

  1. Maggy queries the configured iCPG for relevant symbols, blast radius, and prior intents
  2. Picks the right working directory based on ticket keywords + configured codebases
  3. Spawns claude -p --dangerously-skip-permissions in that directory
  4. Runs analyze → write failing tests → implement
  5. Captures output in a session you can follow in the Sessions tab

Because the spawned Claude Code runs in the target repo, it picks up:

  • That repo's CLAUDE.md
  • Your global ~/.claude/CLAUDE.md
  • All bootstrap skills
  • .claude/hooks/, .mcp.json

So Execute gets the full bootstrap experience — not a stripped-down version.


Competitor Intelligence

Generic — works for any domain:

  1. Configure competitors.categories: ["fintech", "embedded-finance"] in ~/.maggy/config.yaml
  2. Click Discover — Claude identifies 12-18 competitors (market leaders, AI-first challengers, vertical specialists)
  3. Maggy monitors their RSS blogs + Google News daily
  4. Daily briefing is generated once per day (cached), regeneratable on demand

Not Included

Maggy MVP is focused. Not shipped:

  • Meeting bot (voice)
  • Slack integration
  • P2P network + session handoff
  • Self-improvement (/improve-maggy)
  • Linear provider (stub only)

These are v2 work.


Files

  • maggy/PLAN.md — architecture rationale
  • maggy/README.md — user docs
  • maggy/src/providers/base.py — IssueTrackerProvider Protocol
  • maggy/src/services/executor.py — TDD pipeline
  • maggy/src/services/competitor.py — discovery + briefing
  • maggy/src/services/inbox.py — AI prioritization
  • commands/maggy.md — /maggy launcher
  • commands/maggy-init.md — /maggy-init setup wizard

Frequently asked questions

What does the Maggy AI skill do?

Maggy is a local AI engineering command center. AI-prioritized inbox across issue trackers (GitHub Issues/Asana), one-click TDD execute with iCPG context enrichment, daily competitor intelligence briefing.

Why use Maggy on TypingMind?

Because you install it once and use it with any model. Maggy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Maggy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alinaqi/maggy/tree/main/skills/maggy. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Maggy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Maggy?

As many as you like. As long as a model supports skills, you can use Maggy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Maggy AI skill free?

Yes. It is published on GitHub by alinaqi under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇