🔍 TL;DR: a CLI tool that fixes the missed files and drifting line numbers general-purpose AI agents tend to produce during code review, backed by a hybrid design battle-tested inside Alibaba. Title: alibaba/open-code-review URL: 📌 Highlights ⚙️ Hard constraints for precision-critical steps, AI only for dynamic judgment 📝 Generates structured, line-level review comments straight from Git diffs 🕵️ Full-file scan mode for auditing unfamiliar codebases 🛡 Built-in multi-language rules for NPE, thread-safety, XSS, SQL injection 🔌 Plugs into Claude Code, Codex, Cursor, and other coding agents 📊 Beats Claude Code on Precision/F1 across 50 repos and 200 real PRs 💰 Uses about 1/9 the tokens of comparable alternatives Getting battle-tested precision from thousands of internal developers as an open-source CLI feels like a genuinely useful shortcut. #CodeReview# #AIAgents#

# Codex Features and Practical Usage 🔍 Open a PR and get back only the genuinely serious findings, not noise. Codex's GitHub integration posts automated code reviews scoped to high-priority P0/P1 risks. 🏷️ Title: PR Review (@/codex review) 🔗 URL: 📘 Overview Codex's GitHub integration has the AI review a pull request's diff and post high-signal feedback. By design it filters to only P0 and P1 issues, so review comments stay focused on high-priority risks instead of drowning you in nitpicks. ⚙️ How It Works ・For a manual review, comment `@/codex review` on the PR. Codex reacts with 👀 and then posts a GitHub code review. ・Enable Automatic reviews in settings and a review runs the moment a PR is opened, with no mention required. ・Steer the review with a "Review guidelines" section in a top-level `AGENTS.md`. Codex applies the closest `AGENTS.md` to each changed file, so you can nest deeper instructions for package-level scrutiny. ・Mentioning `@/codex` with anything other than `review` kicks off a cloud task using the PR as context, e.g. `@/codex fix the CI failures`. 🛠️ Practical Usage ・First enable the Code review toggle for your repo in the settings (Codex cloud setup is a prerequisite). ・To lock in your review lens, add a "Review guidelines" section to `AGENTS.md` with rules like "Don't log PII" and "Verify authentication middleware wraps every route." ・For a one-off focus, append it inline: `@/codex review for security regressions`. ・To go from review straight to a fix, comment `@/codex fix the P1 issue` and Codex starts a cloud task to address it. 💡 Use Cases On a team where reviewers are stretched thin, let Codex do a first pass to clear P0/P1 issues before human review, cutting down review round-trips. Codify non-negotiable concerns like security and PII handling in `AGENTS.md` so every PR is checked for them automatically. ⚠️ Caveats ・Requires Codex cloud setup on the repo and code review enabled for it. ・The manual trigger must be the exact string `@/codex review`. ・Automatic reviews depend on the toggle being configured correctly, so verify settings during onboarding. #OpenAICodex# #CodeReview#

Harness Engineering Practices P20. Make Legibility and Calibrated Uncertainty an SLO 🎯 Point Now that generation is cheap, the real bottleneck is "human review time." Diffs should be optimized not just for correctness, but for reviewability. 📝 Overview Optimize diffs for review time, not just correctness. Small, focused PRs. Explanations that tell "why." Explicit flagging of risky areas. Additionally, have the agent explicitly output "areas of low confidence" so the harness can route them to additional verification or human review. Calibrated uncertainty is more valuable than false confidence. 🔍 Explanation As agent generation speed increases, the bottleneck shifts from "writing code" to "reviewing code." Giant PRs, unexplained changes, confidently-presented but actually uncertain implementations — these explosively consume reviewer time. Treating legibility as an SLO (Service Level Objective) and measuring/optimizing PR size, explanation presence, and change rationale improves overall throughput. Having agents explicitly state "I'm not confident here" and "this needs human verification" lets reviewers focus on what matters. This is the most overlooked practice for building trustworthy autonomous agents. 🛠 How to Practice - Add "change rationale," "confidence level (high/medium/low)," and "review focus areas" fields to PR templates and require the agent to fill them - Set PR size limits and force splitting when exceeded - Require "low confidence" markers in agent output so the harness can route those areas to additional verification - Measure review time per PR and identify causes of long reviews (giant diffs, missing explanations, etc.) for improvement 💼 Use Cases - Issue-to-PR agents including change rationale and confidence markers in PRs - Code review agents suppressing low-confidence trivial comments and focusing on types humans miss - Migration: keeping per-unit PRs small and focused to distribute reviewer load ⚠ Pitfalls Over-optimizing for legibility can make agent output overly conservative. "Uncertainty expression" can also become noise — an agent that says "I'm not confident" about everything is useless. Calibration is key: accurately marking only genuinely uncertain areas is what creates value. Don't forget to measure review time either. Quantitatively tracking whether PR spam or giant diffs are crushing review bandwidth is the starting point for improvement. #HarnessEngineering# #CodeReview#

Burning tokens on AI code review? 🔥 This tool turns your codebase into a structural graph so the AI reads only relevant files — cutting tokens by a median of 82x. Title: tirth8205/code-review-graph URL: 🔥 Overview A local-first code intelligence tool that persists a structural map (graph) of your codebase via Tree-sitter parsing. It lets AI assistants review by reading only contextually relevant files instead of the whole repository. ❓ Challenges Solved AI code review tools waste tokens by re-reading large parts of the codebase on every review. ・In large monorepos, context bloats and both cost and latency worsen ・Analyzing change impact required scanning the entire project 💡 Methodology & How It Works A three-stage pipeline. ・Parsing: Tree-sitter builds ASTs, extracting functions, classes, imports, and call relationships ・Graph storage: nodes and edges persist in SQLite (no external database) ・Analysis: on changes, blast-radius analysis traces affected callers, dependents, and tests, returning minimal context It supports many languages, incremental updates under 2 seconds, MCP integration (30 tools), a GitHub Action, and D3.js visualization. 📊 Experimental Results ・Token efficiency: 38x-528x reduction (median ~82x across 6 repos) ・Impact prediction F1 score: 0.71 average ・CLI example: full context 12,921 tokens → graph context 762 tokens (~94% saved) #CodeReview# #AIAgents#

Code review taking shape in Delta ✨ An agent turns the change into a structured review guide beside its original thread. You can inspect the code, comment in context, and talk with the people and agents who built it. We use it daily, can't wait for everyone to try it very soon!

Code reviews detect defects 200% more often than most testing (Capers Jones, 12k+ projects). That’s why we built CodeCheck, automated code review that enforces complexity, architecture, and maintainability rules before issues reach production.

# Claude Code Features and Practical Usage 🚀 Just mention "@/claude" in a PR or issue and watch fixes, features, and reviews happen automatically. Claude Code becomes an AI teammate that works inside your CI. 📌 Title and Feature URL Title: GitHub Actions URL: 📝 Overview Claude Code GitHub Actions integrates Claude Code into your GitHub workflow. Mention "@/claude" on any PR or issue and Claude analyzes your code, creates pull requests, implements features, or fixes bugs. Built on the Claude Agent SDK, it is ideal for automating routine engineering tasks. 🔧 How It Works - It auto-detects whether to run in interactive mode (responding to @/claude mentions) or automation mode (running immediately with a prompt) based on your config. - It respects the repository's CLAUDE.md, following your project standards and existing code patterns. - Code runs on GitHub-hosted runners and uses Sonnet by default (Opus 4.8 can be selected). - Beyond the direct Claude API, it supports Amazon Bedrock and Google Vertex AI. 🛠 Practical Usage - The easiest setup is running "/install-github-app" inside claude in your terminal; it guides you through the GitHub app and secrets (you need repo admin rights). - For manual setup: install the Claude GitHub app ( add ANTHROPIC_API_KEY as a repository secret, and copy examples/claude.yml into .github/workflows/. - Use the action "anthropics/claude-code-action@v1". Pass instructions via "prompt" and CLI arguments via "claude_args". - claude_args examples: --max-turns 5 / --model claude-sonnet-4-6 / --mcp-config /path/to/config.json - Comment examples: "@/claude implement this feature based on the issue description", "@/claude fix the TypeError in the user dashboard component". 🎯 Use Cases - Mention on an issue to auto-create a PR that turns requirements into working code. - Request a standard review on a PR: "@/claude review this for security issues". - Use a schedule (cron) trigger to auto-generate a daily summary of yesterday's commits and open issues. - Wire in the code-review plugin to run its skill automatically on every PR update. ⚠️ Caveats - Never commit API keys directly to the repo; always reference GitHub Secrets (secrets.ANTHROPIC_API_KEY). - Costs accrue both as GitHub Actions minutes and API tokens. Use --max-turns and timeouts to prevent runaway jobs. - If Claude doesn't respond, confirm the comment uses "@/claude" (not "/claude") and that the app and secrets are configured. - v1.0 has breaking changes from beta: remove mode, change direct_prompt to prompt, and move CLI options into claude_args. #ClaudeCode# #CICD#

Codex Code Review can now use custom repository rules in AGENTS.md. Start with a check your reviewers keep repeating. Keep it concise and scoped so Codex can catch repository-specific issues even when the relevant context is siloed.