GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
제작자가 쓴 설명(영어 원문)
PR blast radius and 0-1 risk score from git history, so low-risk PRs can be auto-approved.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"opendrs": {
"command": "npx",
"args": [
"-y",
"@error-path/opendrs",
"mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add opendrs -- npx -y @error-path/opendrs mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"opendrs": {
"command": "npx",
"args": [
"-y",
"@error-path/opendrs",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"opendrs": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@error-path/opendrs",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 error-path 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.5.0)은 14일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: GitHub, Desktop Commander, Cloudflare MCP
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
A calibrated 0-1 risk score for every pull request, so AI-reviewed, low-blast-radius PRs can be auto-approved into the merge queue while risky ones still wait for a human.
AI agents now write and review code faster than people can read it, so human review has become the bottleneck. Most pull requests are low risk, and a few cause most incidents. opendrs measures each PR's blast radius and risk: what the change touches, how many files depend on it, the history of the files it changes, author experience, missing tests and critical paths. It turns that into a 0-1 score using signals from your own git history. A team then sets a policy. For example, a PR that scores below 0.3 and has passed an AI review (Claude, CodeRabbit, Copilot or any check run) is approved and sent to the merge queue or auto-merge. Riskier PRs still wait for a human. The approach follows Meta's Diff Risk Score (DRS); opendrs is an independent, open-source implementation that runs in any git repository, with no service to host.
Contents: Install · Quick start · How it works · Features · Results · CLI and MCP · Documentation · Limitations · Compared with other tools · Prior art
| Use it as | Command | Details |
|---|---|---|
| GitHub Action | uses: error-path/opendrs@v0 (Marketplace) | Comments, labels and, optionally, approves and merges. GitHub Action reference |
| CLI | npx @error-path/opendrs score | CLI, MCP server, Claude Code plugin and agent skill |
| MCP server (any agent) | npx -y @error-path/opendrs mcp | Tools: score_diff, explain_file_risk, list_hotspots, suggest_reviewers |
| Claude Code plugin (skill plus MCP server) | /plugin marketplace add error-path/opendrs, then /plugin install opendrs@opendrs | |
| Agent skill (Claude Code, Cursor, Codex and others) | npx skills add error-path/opendrs | skills/opendrs/SKILL.md |
| Docker (MCP server over stdio) | docker build -t opendrs . && docker run -i --rm -v "$PWD:/repo" opendrs | Mount the repository to score at /repo |
Website and guides: error-path.github.io/opendrs.
Score your current branch against the default branch. No configuration or API key is needed:
npx @error-path/opendrs score
You get a score, a tier (green, yellow or red), the signals that drove it with their evidence, and the policy decision. --json prints the full result, and --fail-above 0.6 makes the command exit 1 above a threshold.
To score every pull request, add .github/workflows/opendrs.yml (or run npx @error-path/opendrs init):
name: opendrs
on: pull_request: types: [opened, synchronize, reopened, ready_for_review, labeled, unlabeled] permissions: contents: read pull-requests: write checks: read jobs: opendrs: runs-on: ubuntu-latest steps:
This only comments and labels. Approval is off until you turn it on in .opendrs.yml on your default branch; see how to auto-approve low-risk PRs and the GitHub Action reference.
flowchart LR
diff["git diff merge-base...head"] --> signals hist["git history"] --> signals imports["import graph"] --> signals llm["LLM judge (optional)"] --> signals signals["Signals each with evidence and provenance"] --> model["Logistic model score = sigmoid(b + Σ w·x)"] model --> tier["Tier green / yellow / red"] tier --> policy["Policy blocks, size cap, unknowns, AI review, sampling"] policy --> decision["auto-approve deferred-review waiting-ai-review human-review"]
observed, derived, inferred or unknown) and the evidence behind it. All signalsw·x is reported, so you can see why a PR scored what it did. Out of the box the weights are hand-set priors; opendrs calibrate fits them to your repository. Calibration and backtestingA PR now often gets two rounds of AI review before a human looks at it. Those rounds catch local defects: typos, missing null checks, an obvious off-by-one. By the time a human opens the PR, that work is done, and what is left is a design and architecture judgment call made with less context than before. opendrs is built for that moment: it tells the final human how much attention the PR needs and names up to five architecture and history facts — a new import between modules, a broken public contract, a file that usually changes with one the PR didn't touch — that neither the editor nor the AI review comments showed. See the captain brief.
| Feature | What it does | Default | Docs |
|---|---|---|---|
| Risk signals | Size, spread, file history, author experience, complexity, blast radius, critical paths, tests, language | on | Signals |
| Calibration and backtest | Fit the weights to your history and measure capture at 5 / 10 / 50 % gated | run on demand | Calibration |
| Hard blocks and safety rails | Blocked paths, binaries, submodules, self-modification guard, sampling, stale-approval checks | on | Policy |
| Auto-approval | Approve green PRs after your AI review passes, then auto-merge or the merge queue | off | Auto-approve guide |
| LLM judge | Classifies every hunk into safe or risk categories; can only raise the score | off | LLM judge |
requireSafe gate | Auto-approve only when the judge calls every hunk safe with enough confidence | off | LLM judge |
| Per-source policy | Human, bot or AI-agent PRs get their own stricter green, daily cap and track record | off | Policy |
| Deferred review | A PR just above green lands now and gets a post-merge review | off | Policy |
| Landing delay | An eligible PR waits before approval, so a human can veto | off | Policy |
| Freeze windows | Suspend or tighten auto-approval on weekends or peak dates | off | Policy |
| Content blocklist | Regexes on added lines send a PR to a human | off | Configuration |
| Reviewer assignment | One of the top three experts is asked to review risky PRs | on in the Action | Policy |
| Hotspots | Rank files by risk at rest | run on demand | CLI and MCP |
| Review brief | Ranks hunks to read first, fixed check questions, safe-to-skim list, hard-rule reasons — recipes for claude-code-action, Bugbot, CodeRabbit (limited), Copilot and Codex (paste) | on | LLM reviewers, CLI and MCP |
| Pre-PR self-check | Lists the ids an author agent should fix or explain before opening the PR; opendrs reads the verdicts back from the head commit and adds one advisory line, never part of the gate | off | Self-check recipe |
| Verification pass | One batched LLM call re-checks every captain fact, review-first hunk and check with fresh context and keeps, drops or downgrades each one; never changes the score, tier or policy | off | Verification pass |
| Incident feedback | opendrs feedback records incidents for calibration | run on demand | Calibration |
Every setting lives in .opendrs.yml, read from the base branch so a PR cannot loosen its own gate: configuration reference.
| Benchmark | Result | Details |
|---|---|---|
ApacheJIT (15 Apache Java projects, official 2017–2019 test years, apache/hadoop left out because it has no positive labels) | Calibrated opendrs: AUC 0.823; catches 67 % of buggy commits in its riskiest 30 %, on par with a regression on the classic JIT metrics (0.821, 66 %) | Results on ApacheJIT |
| ApacheJIT with the LLM judge (Claude Sonnet 5, 800 sampled test commits) | At confidence 8, the commits the judge calls all-safe are buggy at 5.3 %, against a 25.5 % base rate | With the structured LLM judge |
| 27 public repositories (calibrate, then backtest) | Calibration beat the default priors on held-out commits in all nine repositories of the first run | Benchmark results |
| Command | What it does |
|---|---|
opendrs score | Score merge-base(base, head)..head |
opendrs explain | A file's history, fixes, authors, dependents and path policy |
opendrs hotspots | Rank files by risk at rest |
opendrs calibrate / opendrs backtest | Fit the model to your history; compare default and calibrated models |
opendrs feedback | Record an incident in .opendrs/incidents.jsonl |
opendrs init | Write a commented .opendrs.yml and a comment-only workflow |
opendrs mcp | Run the MCP server |
Flags, exit codes, MCP tools and the agent skill: CLI, MCP server, Claude Code plugin and agent skill.
| Page | Read it when you want to |
|---|---|
| Auto-approve low-risk PRs | set up a risk-based approval gate step by step |
| GitHub Action reference | look up inputs, outputs, permissions, branch rules and the scheduled sweep |
| Policy and safety rails | know exactly why a PR was or was not approved |
| Configuration reference | change .opendrs.yml |
| Risk signals | see what opendrs measures and how |
| What is the blast radius of a PR? | understand blast radius |
| LLM judge | turn on the optional LLM judge or the requireSafe gate |
| Feeding the review brief to an LLM reviewer | get opendrs's findings in front of Claude Code, claude-code-action, Bugbot, CodeRabbit, Copilot or Codex |
| The captain brief | know what the final human reviewer sees: priority, reviewer, and the architecture and history facts |
| Verification pass | turn on an LLM re-check of the brief's facts before a human reads them |
| [
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요