Most repo analysis tools will tell you something is wrong. Few will tell you what to do about it.
A health score of 62 is useful. Knowing that score dropped because four PRs have been open for over a month, your bus factor is 1, and CI is failing 35% of the time — that's more useful. But what most teams actually need is the next step: a concrete plan to fix it, right now, without scheduling a retro first.
That's the gap we just closed.
Generic Advice Doesn't Ship Fixes
Until now, RepoShark's recommendations were solid but general. "Improve your PR review process" is reasonable advice, but it doesn't account for whether your problem is stale PRs, missing reviewers, or one person rubber-stamping everything. The same surface-level recommendation could mean three completely different workflows depending on your repo's actual data.
We wanted recommendations that read like they came from a senior engineer who had spent an afternoon in your repo — not a checklist pulled from a blog post.
What Changed
Every recommendation in RepoShark is now built from your repo's actual analysis data. Here's what that looks like in practice.
1. Data-Tailored Explanations
Instead of "You have stale pull requests", you'll see:
You have 4 pull requests open for 30+ days. Stale PRs accumulate merge conflicts and block progress. They also signal unclear ownership or decision paralysis.
The numbers, percentages, and contributor names come directly from your analysis. If your bus factor risk is driven by one person owning 87% of commits, the recommendation says exactly that — not "consider distributing ownership."
2. Concrete Action Steps
Each recommendation now includes 2–3 specific steps. No vague directives. For a stale PR problem, you'd see:
- Triage each stale PR: close, rebase, or flag for discussion
- Set a team policy for maximum PR age (e.g. 14 days)
- Add a weekly PR review checkpoint to your standup
These are steps you can put on a ticket and assign today.
3. AI Agent Prompts You Can Copy and Use
This is the part we're most excited about.
Every high-priority recommendation — and most medium ones — now includes a ready-to-use prompt designed for AI coding agents like Claude or ChatGPT. These aren't generic "help me fix my repo" prompts. They're scoped, specific, and pre-loaded with context from your analysis.
For example, a bus factor recommendation might include:
Analyse this repository and identify files and areas where only one contributor has made changes. Create a knowledge-sharing plan that lists: (1) critical files needing cross-training, (2) suggested reviewer pairings, (3) a CODEOWNERS file draft distributing ownership across the team.
Hit the copy button, paste it into your AI tool of choice, and you've got a working session that would have taken an hour to set up manually.
24 Recommendations, Five Categories
We now cover 24 distinct recommendation types across five categories:
| Category | What It Covers |
|---|---|
| Process | Stale PRs, direct pushes, release cadence, PR pickup time |
| Quality | CI health, large commits, commit message quality |
| Security | Missing CODEOWNERS, hotspot bus factor |
| Team | Bus factor, activity drop, review bottlenecks, review coverage |
| Documentation | Missing LICENSE, missing repo description |
Each one is prioritised as high, medium, or low based on severity, so you know what to tackle first.
Why AI Prompts Matter Here
Engineering teams are already using AI assistants daily. The bottleneck isn't access to AI — it's knowing what to ask. A developer staring at a failing CI pipeline doesn't need a chatbot; they need a well-scoped prompt that says "here's the pass rate, here are the common failure patterns, here's what to investigate first."
That's what these prompts do. They bridge the gap between "your repo has a problem" and "here's how to start a focused AI-assisted investigation." No context-switching. No prompt engineering. Just copy, paste, and go.
Built for Action, Not Dashboards
Dashboards are great for awareness. But awareness without action is just anxiety with better data visualisation. The goal of this update is to make every recommendation something you can act on within five minutes — either by following the steps yourself or by handing the AI prompt to an agent.
If your repo has problems, you should know about them. But more importantly, you should know exactly what to do next.