Slop = low-quality AI-generated code that looks fine but hides bugs and debt.
Catch risky AI-generated code before it merges. Make sure every change fits how your company actually works.
Connectory helps your company remember policies, decisions, and ownership across every repository, then checks every pull request against that context.
Genie gives your company a living source of truth for policies, decisions, and ownership, managed from a dashboard with no terminal required. SlopBuster applies that shared context to every GitHub pull request, catching risky or out-of-policy changes before they merge. Together, they make Connectory the institutional memory and governance layer for your agentic infrastructure.
Connect GitHub · Choose repositories · Review results in your pull-request workflow
Copilot / Claude / ChatGPT
SlopBuster
Analyzing...
Production Ready
Connect
Install the GitHub App
Understand
Build repository context
Review
Check each proposed change
Decide
Fix, approve, or follow up
organizational memory
Coding tools remember the repository in front of them. Connectory preserves the policies, decisions, ownership, and open questions your whole company depends on.
Your policies and decisions stay with you when you change AI tools or models. SlopBuster applies that shared context to every pull request before it merges.
Connectory Genie
One living source of company truth
SlopBuster reviews changes against live company context.
Risks and policy conflicts surface while they can still be fixed.
Keep your coding tools
Claude Code, Cursor, Codex, and AGENTS.md help developers draft, refactor, and explore code. Connectory complements them instead of asking your team to replace them.
Add the company layer
Connectory carries intent, ownership, and policy across repositories, teams, model changes, and employee turnover. Leaders can read and steer it from a dashboard, and SlopBuster uses it during every review.
Local memory versus company memory
Desk tools help one developer write code. Connectory gives leaders, security teams, and every repository one durable operating context.
| Topic | What desk tools remember (Claude, AGENTS.md, etc.) | What Connectory remembers for your company |
|---|---|---|
| Where the knowledge comes from | What an agent saved, or an index of local code | Server-side ingest of org git, PR, and contributor activity, plus human prose |
| What is stored | Facts, code patterns, conventions (the what) | People, teams, ownership, intent (policies, decisions), and open questions (the why) |
| Who can read and steer it | Developers via IDE, CLI, or MCP client | Any human via dashboard and plain language; machines via PR bot and MCP. Nothing installed to read or steer |
| How up to date it stays | Manual updates or re-index; staleness is the admitted failure mode | Evolves from live activity and curation; a policy change now governs the next review everywhere |
| How much of the org it sees | Per-machine, per-account, per-repo; sees only what is checked out | One server-side graph per org; sees everything; single source of truth |
| What it is for | Make the individual coder faster | Govern the organization: appropriateness, ownership, decisions, onboarding, risk |
Built for accountable teams
What is at risk
We ship faster than ever, and I have no idea if we are building the right things or what walks out the door when someone leaves.
What changes
A living organizational brain: decisions, ownership, and goals captured as they happen and readable in plain language, no terminal required.
What is at risk
Accountability for AI-code failures lands on me, but I cannot review thousands of agent PRs across dozens of repos myself.
What changes
Every PR judged for appropriateness against live org policy, per-person and per-agent quality signals, and bus-factor alerts across the fleet.
What is at risk
Regulators want evidence of why AI acted, inside our perimeter. "AI wrote it" is not a defense.
What changes
Typed, queryable record of policies, decisions, and evidence. Read and steer from a dashboard with no coding tool installed. SOC 2 Type II; no source code stored.
The cost of context loss
Faster code generation increases the value of durable decisions, clear ownership, and accountable review.
$1.3T
U.S. knowledge-worker turnover cost per year
70-80%
of enterprise knowledge is tacit, never written down
46%
of AI-code failures default to CTO or VP Eng
12%
of enterprises have dedicated AI governance
81%
report rising production failures from AI-generated code
Sources: Deloitte Global Human Capital Trends (2024); Gartner (2024); CloudBees State of Code Abundance (May 2026), survey of 200+ enterprise technology leaders.
SlopBusterPowered by Connectory Genie: shared organizational context available during review.
SlopBuster adds a dedicated review step after code is written. It combines the pull-request diff with repository intelligence and the policies, decisions, and ownership your team keeps in Connectory Genie.
Context changes what good code is
Stack version
Advice should match the language and framework versions your repository actually uses. A suggestion that fits one version may be unnecessary or incorrect in another.
Repo purpose
A web application, research prototype, and embedded system have different operating constraints. Review criteria should reflect the purpose of the code.
Company goals
Teams make different decisions about reliability, release speed, and maintenance. Connectory gives reviewers access to the standards your organization has chosen.
Cross-repository context
A change in one repository can affect an API, service, or policy maintained elsewhere. Shared context helps reviewers consider those connections.
SlopBuster reviews the diff with repository and organizational context, then returns a clear recommendation in GitHub.
Your coding AI writes the PR.
SlopBuster, independent, cross-repo, decides if it belongs.
Code quality
Maintainability, testing, error handling, and fit with existing patterns
AI-generated patterns
Duplicated utilities, unsupported APIs, band-aid fixes, and swallowed errors
Security
Change-scoped risks, unsafe inputs, secrets, and authorization concerns
Framework reinvention: This implements a fixed 1-second delay, but your codebase already has exponentialBackoff() in utils/retry.ts that handles jitter, max retries, and circuit breaking.
Watch how SlopBuster catches framework reinvention and explains why using existing utilities is better
opened 2 hours ago by @junior-dev
Framework reinvention detected
Your PR implements custom retry logic 67 lines. This repo already has RetryableOperation in commons/utils.ts.
A comprehensive review with Quality Radar scoring, findings categorization, and technical debt notes
By shivanikakrecha • Approved (with conditions)
Five core dimensions of code quality
This PR introduces a comprehensive LinkedIn contact search feature integrating Apify with AI-driven match evaluation. It improves the codebase by adding strong type safety and clear separation of concerns, but introduces some technical debt related to unused API fields.
Unused API fields 'seniority_levels' and 'section_*' create misleading API surface.
Tight coupling to SimpleLLM.run() return type is fragile and should be documented.
Two intelligence layers run before and during every review. Without both, it's just another diff reader.
Before review, RepoWatch runs a structured discovery sequence for the connected repository:
The result is a repo_intelligence block with the stack, language version, repository purpose, established patterns, and folders that should be excluded from review.
Code quality, AI-generated patterns, and security are evaluated with repository context available to each review.
Findings point to the changed code and explain the repository context behind the concern.
The GitHub review groups findings by severity and provides a recommendation to merge, fix first, or follow up.
Real PR review from a production codebase showing Quality Radar, findings, and technical debt tracking.
By biyer Approved (with conditions)
Critical captcha logging fixed; improved CORS and auth tests; minor logging and test warnings remain. While some tech-debt remains around logging full SQL queries and payloads, and test brittleness, these are warnings rather than blockers.
A useful review has to do more than summarize a diff. These are the questions to ask when you evaluate a review and governance platform for your engineering organization.
Does the reviewer understand the repository before judging the diff?
How Connectory approaches it: RepoWatch identifies the repository purpose, stack, language versions, established patterns, and areas that need attention.
Can the review use decisions and policies that live outside one repository?
How Connectory approaches it: Connectory Genie keeps shared policies, decisions, ownership, and open questions available to the review workflow.
Is review a separate control after code generation?
How Connectory approaches it: SlopBuster runs as a GitHub App in the pull-request workflow, separate from the tool used to write the change.
Does the result help a team decide what to do next?
How Connectory approaches it: Each review organizes its findings, explains why they matter, and provides a merge recommendation.
Review current product behavior, setup, and examples before you choose a plan.
See how Connectory reviews a pull requestDetect trends before they become problems. Surface risk signals across repos, people, and AI agents, all in one place.
Health scores, bus factor analysis, and contributor distribution across every repository.
Activity scores, AI vs human ratios, and early warning signals for burnout or flight risk.
Org-wide health, velocity trends, risk dashboard, and natural language trend summaries.
Cost per commit, effectiveness scores, incident rates, and head-to-head agent comparison.
Coaching conversation guides, growth tracking, and team health signals.
0
Lenses
Complete visibility
0x
Commons
Shared code multiplier
0d
Trends
Rolling analysis
0%
Coverage
Human + AI unified
Plans include users without per-seat fees. Pricing scales with active codebase usage, not your headcount.
Start shipping cleaner code today
No credit card required
Automate code reviews & drive higher quality code
Ship faster with fewer bugs and regressions hitting production
Enterprise governance with security, compliance & risk management
Need an enterprise plan with unlimited PRs, unlimited repos, and custom integrations? Contact sales.
Secret detection, vulnerability scanning, insecure pattern detection
PR cycle benchmarking, merge confidence, throughput trends
Custom rules, org-wide standards, merge gates
Dedicated Slack, faster compute, quarterly reports
Connect with developers who care about code quality. Get help, share feedback, and help shape the future of Connectory.
Connectory Genie keeps policies and decisions available to your team. SlopBuster applies that context when it reviews GitHub pull requests.
How Genie + SlopBuster work together
Free for open source. No credit card required.