feat: add Agentic AI Contribution Recommender Engine (Issue #611)#614
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adityapichikala wants to merge 2 commits into
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feat: add Agentic AI Contribution Recommender Engine (Issue #611)#614adityapichikala wants to merge 2 commits into
adityapichikala wants to merge 2 commits into
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📝 WalkthroughWalkthroughThis PR introduces an AI-powered contribution recommender that analyzes a GitHub user's coding patterns and recommends open-source issues they are best positioned to contribute to. It builds a skill profile from public repositories, scouts matching issues via GitHub API searches, ranks them by relevance, and displays the top 3 through an interactive animated component integrated into the Tracker dashboard. ChangesAI Contribution Recommender
Sequence DiagramsequenceDiagram
participant User as User / Tracker
participant Component as ContributionRecommender
participant Hook as useContributionRecommender
participant GitHub as GitHub API
User->>Component: Mount with username + getOctokit
Component->>Hook: runRecommender(username)
Note over Hook: agentStep = "building-profile"
Hook->>GitHub: Fetch user repositories
GitHub-->>Hook: Repos (languages, stars, recency)
Note over Hook: agentStep = "scouting-issues"
Hook->>GitHub: Search good-first-issue + help-wanted<br/>(across top languages)
GitHub-->>Hook: Candidate issues
Note over Hook: agentStep = "ranking-matches"
Hook->>GitHub: Fetch repository metadata for candidates
GitHub-->>Hook: Repo data (stars, owner, etc.)
Hook->>Hook: Compute match scores & rank
Hook-->>Component: recommendations[], skillProfile, agentStep
Component->>Component: Render skill profile + results list
Component->>User: Display top 3 recommendations
User->>Component: Click refresh button
Component->>Hook: runRecommender(username, force=true)
Hook-->>Component: Updated recommendations
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes Suggested labels
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✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches🧪 Generate unit tests (beta)
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…sLab#611) - Add useContributionRecommender hook with 3-step agentic pipeline: * Step 1 (Skill Profiler): fetches user repos, detects top languages weighted by recency and stars, infers domain (frontend/backend/devops) * Step 2 (Issue Scout): searches GitHub for open issues tagged 'good first issue' or 'help wanted' matching top languages, using Promise.allSettled for non-blocking async parallelism * Step 3 (Relevance Ranker): scores issues by language rank, label quality, repo star count, and recency; returns top 3 - Add ContributionRecommender.tsx widget with premium glassmorphism UI: * Gradient accent bar and indigo/sky branded header with Sparkles icon * Skill profile badge row showing detected languages + activity level * Loading state with animated 3-step progress bar and shimmer skeleton * Framer-motion stagger animations on recommendation cards * Each card: repo name, star count, issue title, 'Why this matches you' reasoning box, labels, difficulty badge (Beginner/Intermediate), CTA * Error/empty states with retry button * Session-level caching via useRef to avoid redundant API calls * Refresh button to force re-run - Integrate widget into Tracker.tsx below DailyActivityStatus - Add @Keyframes recommender-pulse and recommender-shimmer to index.css - Document future GITHUB_SERVER_TOKEN env var in backend/.env.example Closes GitMetricsLab#611
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Actionable comments posted: 4
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Verify each finding against current code. Fix only still-valid issues, skip the
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Inline comments:
In `@src/hooks/useContributionRecommender.ts`:
- Around line 139-149: The current scoutIssues implementation maps queries to
octokit.request calls but swallows every request error with .catch(() => []), so
Promise.allSettled never sees rejections; remove the per-request .catch on the
octokit.request in the searchRequests creation inside useContributionRecommender
(the mapped call that builds searchRequests) and instead await
Promise.allSettled(searchRequests) in scoutIssues/runRecommender, inspect each
settled result (status === 'fulfilled' collect value, status === 'rejected'
collect errors), and if all are rejected throw one of the errors (or a composed
error) to surface auth/rate-limit/validation failures to the outer try/catch; if
some are fulfilled, use those partial results (combine fulfilled values) rather
than treating a mixed outcome as an empty result.
- Around line 329-409: The runRecommender pipeline can be overwritten by slower,
earlier runs; add a run guard (request id or AbortController) inside
runRecommender to ignore stale results: create a local runId (or controller) at
the start of runRecommender, store it in a ref (e.g., currentRunRef), and check
it before setting state and before caching (references: runRecommender,
cacheRef, setSkillProfile, setRecommendations, setRecommenderError,
setRecommenderLoading, setAgentStep); ensure any async branches return early if
the runId no longer matches (or if the controller is aborted) so only the latest
invocation updates state and cache.
- Around line 126-167: scoutIssues currently returns only GitHubSearchIssue[]
which loses the query language; modify scoutIssues to carry the original query
language and computed langRank through to ranking (either return an array of {
issue, matchedLanguage, langRank } or build and return a Map keyed by issue.id
with {matchedLanguage, langRank}), ensure you de-duplicate by keeping the lowest
langRank when an issue appears in multiple queries, and update
rankIssues/scoreIssue/computeMatchReason to read matchedLanguage/langRank from
the new structure instead of inferring from labels (use
skillProfile.topLanguages to compute langRank consistently).
- Around line 121-123: The buildSearchQuery function is incorrectly replacing
spaces inside quoted labels and omitting the is:issue filter; update
buildSearchQuery(language: string, label: string) to keep the label verbatim
inside the quotes (do not replace spaces with '+') and include the is:issue
token in the returned query string (e.g., add +is:issue) so the GET
/search/issues request only returns issues; refer to the buildSearchQuery
function to make this change.
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Run ID: 4d62833f-3c33-4e04-8d9b-3e43b08b5a75
📒 Files selected for processing (5)
backend/.env.examplesrc/components/ContributionRecommender.tsxsrc/hooks/useContributionRecommender.tssrc/index.csssrc/pages/Tracker/Tracker.tsx
- Fix search query construction to preserve spaces and include is:issue - Preserve language query context from scout to ranker to avoid inaccurate inference - Throw error on all search queries failing to properly bubble up rate limit / auth errors - Add requestId guard in runRecommender to prevent stale concurrent runs - Remove unused DailyActivityStatus and Backtotop imports from Tracker.tsx
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Related Issue
Description
Implements the Agentic AI Contribution Recommender Engine proposed in Issue #611.
Instead of a Python microservice (optional future follow-up), this PR delivers a fully client-side, three-step agentic pipeline that runs in the browser using the user's own GitHub token — keeping the PR self-contained with zero backend changes required.
🧠 Architecture: Multi-Step Agentic Pipeline
[Step 1] Skill Profiler → GET /users/{username}/repos → Scores languages weighted by recency + star count → Infers domains: frontend / backend / devops / mobile / data → Determines activity level: high / medium / low
[Step 2] Issue Scout (non-blocking, Promise.allSettled) → Parallel GitHub Search API queries: top 3 languages × ["good first issue", "help wanted"] → Deduplicates results across all queries
[Step 3] Relevance Ranker → Scores each issue: language rank ×30, label quality ×25, star count ×0.1, recency ×20 → Fetches repo metadata for top 15 candidates → Returns top 3 ranked recommendations
✨ New Files
src/hooks/useContributionRecommender.ts— core agentic pipeline hook with session-level caching (useRef) to avoid redundant API callssrc/components/ContributionRecommender.tsx— premium glassmorphism widget featuring:🔧 Modified Files
src/pages/Tracker/Tracker.tsx— integrates widget belowDailyActivityStatus, auto-triggers on username submitsrc/index.css— adds@keyframes recommender-pulse(activity dot) and@keyframes recommender-shimmer(loading skeleton)backend/.env.example— documents futureGITHUB_SERVER_TOKENfor the optional Python microservice follow-upHow Has This Been Tested?
/track, entered a GitHub username (with and without a PAT)npm run build— ✓ 3027 modules transformed, zero TypeScript errors, built in 21sScreenshots (if applicable)
Type of Change
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