maggy

Overview of maggy skill

What maggy does

maggy is a local AI engineering command center in claude-bootstrap for teams that need to turn issue intake into execution. The maggy skill is built for people who want an AI-prioritized inbox, fast handoff into local Claude Code runs, and a daily competitor-intelligence briefing without wiring together a separate ops stack.

Who should use it

Use maggy if you manage engineering work across GitHub Issues, Asana, or similar trackers and want a persistent workflow instead of one-off prompts. It is especially relevant for Project Management when you need triage, prioritization, and execution tracking in one place.

What matters before you install

The main value of maggy is not generic chat assistance; it is the combination of issue ranking, context injection, and local execution. The biggest adoption question is whether your team can accept a workflow that may trigger Claude with elevated write and shell permissions during execute runs.

How to Use maggy skill

Install maggy

Install the maggy skill with:
npx skills add alinaqi/claude-bootstrap --skill maggy

For a clean maggy install decision, confirm that you actually want a local command-center workflow tied to your repos and trackers before adding it. If your team only needs better prompting for one task, maggy may be more machinery than you need.

Read these files first

Start with SKILL.md to understand the intended workflow and safety model. Because this repository has no extra rules/, resources/, or helper scripts, the skill file itself is the primary source of truth; skim README.md or other top-level docs only if they appear in the repo later.

How to prompt maggy well

A good maggy guide starts with a concrete operational goal, not a vague request. Include:

• the tracker or inbox you want prioritized
• the repo or codebase roots maggy should work against
• what “urgent” means for your team
• any constraints on execution, review, or branch handling

Stronger input example: “Prioritize open GitHub Issues for the billing service, rank by release risk and customer impact, then execute only the top bug with TDD context.” That is better than “help me manage tickets” because it gives maggy a decision rule.

Practical workflow

Use maggy in two stages: first triage, then execute. Let it rank the inbox before asking it to spawn a local Claude Code run, because the skill is strongest when the issue signal is already filtered and the target repo is clear. For Project Management use, this makes the handoff from planning to engineering action more consistent.

maggy skill FAQ

Is maggy only for Project Management?

No. The maggy skill supports Project Management workflows, but it is really aimed at engineering teams that need issue triage plus local code execution. If you only need a status dashboard, a lighter tool may be enough.

How is maggy different from a normal prompt?

A normal prompt can summarize tickets, but maggy is designed around a repeatable workflow: prioritized inbox, execute handoff, and competitive briefing. That makes it more useful when you want the same process every day instead of rewriting instructions from scratch.

Is maggy safe to install?

The skill includes an important permission model caveat: execute may run Claude with --dangerously-skip-permissions so local edits and shell commands are not blocked mid-task. That is powerful, but it means you should only use maggy where your codebase roots and tracker inputs are controlled.

When should I not use maggy?

Do not choose maggy if you need a simple one-shot analysis, if your environment cannot tolerate local write access, or if your issue data is too noisy to prioritize reliably. In those cases, a narrower prompt or a non-executing workflow is a better fit.

How to Improve maggy skill

Give maggy better ranking signals

The quality of maggy depends on how clearly you define priority. If you want better output, provide explicit ranking criteria such as customer impact, blocker status, due date, or OKR alignment. That helps the maggy skill sort tickets in a way your team will trust.

Narrow the execution target

Most weak results come from ambiguous repo scope. Tell maggy exactly which codebase root, branch, or service is in play, and specify whether the task is bug fix, test repair, or feature work. This reduces the chance of the wrong repo being treated as the active worktree.

Improve first-pass execution quality

When you ask maggy to execute, include the issue text, acceptance criteria, relevant file paths, and any known constraints. A rough prompt like “fix the failing test” is less useful than “fix the billing test in packages/api, keep behavior unchanged, and preserve the current public API.”

Iterate after the first run

If maggy gets close but not quite right, refine the input by adding one missing decision rule rather than rewriting the whole prompt. Common failure modes are vague priority labels, incomplete tracker context, and unclear permission expectations. Tightening those inputs usually improves the next run more than asking for a broader answer.