GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Beschreibung vom Entwickler (englisches Original)
PR blast radius and 0-1 risk score from git history, so low-risk PRs can be auto-approved.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"opendrs": {
"command": "npx",
"args": [
"-y",
"@error-path/opendrs",
"mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add opendrs -- npx -y @error-path/opendrs mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"opendrs": {
"command": "npx",
"args": [
"-y",
"@error-path/opendrs",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"opendrs": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@error-path/opendrs",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto error-path bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.5.0) kam vor 14 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: GitHub, Desktop Commander, Cloudflare MCP
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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 |
| [
Nur der Anfang wird gezeigt; den Rest findest du im Repo