ContribFilter
High Opportunity 7/10ContribFilter is a GitHub App that automatically triages incoming issues and pull requests to detect and flag AI-generated or low-quality contributions before they reach maintainers, using heuristic and LLM-based quality scoring, pattern detection, and configurable auto-responses or labels. Maintainers set quality thresholds and response templates once, and the tool runs silently in the background to protect their attention. Built for open source maintainers and small teams drowning in AI-slop contributions.
Target User
Open source project maintainers and small dev teams managing public or semi-public GitHub repositories who are experiencing a growing flood of low-effort, AI-generated issues and pull requests consuming their review time
Revenue Model
$5/month per repository for individuals, $19/month for teams covering up to 10 repos. Freemium entry with a free tier capped at 1 repo. Mid-scale MRR potential in the $8–20K range, modest but very sticky given the ongoing nature of the problem
Differentiator
GitHub's native tools offer only basic spam filtering with no AI-contribution-specific heuristics; existing bots like Stale focus on aging issues rather than quality gating at the point of submission — ContribFilter is the first tool purpose-built for the AI-generated noise problem
Score Breakdown
Based on Pain Points
Maintainers overwhelmed by low-quality AI-generated contributions
7The surge of auto-generated issues and pull requests from AI tools has created a denial-of-service-like attack on human attention. Maintainers face a high-volume flood of low-quality, inaccurate 'AI slop' contributions that consume reviewer time without proportional project benefit, while the maintainer pool has not grown to match.
Lack of central hub for AI agent skills discovery and integration
6With AI moving toward composable agent Skills, there is no central marketplace to find, vet, and integrate pre-built capabilities. Developers waste time recreating common agent functions rather than discovering and reusing existing solutions.