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Sync auto-llm-issue-review.yml from .github repo
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‎.github/workflows/auto-llm-issue-review.yml‎

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name: "LLM Issue Review (Model Label Trigger)"
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on:
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issues:
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types: [labeled]
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workflow_dispatch:
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inputs:
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issue_number:
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description: "Issue number"
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required: true
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type: number
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llm_provider:
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description: "LLM provider"
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required: false
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default: ""
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type: choice
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options:
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- ""
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- openai
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- gemini
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- anthropic
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llm_model:
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description: "Model name (provider-specific)"
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required: false
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default: ""
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type: string
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trigger_label:
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description: "Label to emulate (optional)"
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required: false
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default: ""
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type: string
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permissions:
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contents: read
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issues: write
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concurrency:
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group: llm-issue-review-${{ github.repository }}-${{ github.event.issue.number || github.event.inputs.issue_number }}
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cancel-in-progress: true
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jobs:
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review:
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runs-on: ubuntu-latest
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steps:
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- name: Run LLM issue review and comment
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uses: actions/github-script@v7
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env:
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ISSUE_NUMBER: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.issue_number || github.event.issue.number }}
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TRIGGER_LABEL: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.trigger_label || github.event.label.name }}
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LLM_PROVIDER: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.llm_provider || '' }}
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LLM_MODEL: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.llm_model || '' }}
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OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL || 'https://api.openai.com/v1' }}
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
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ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
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with:
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github-token: ${{ secrets.GITHUB_TOKEN }}
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script: |
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const owner = context.repo.owner;
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const repo = context.repo.repo;
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const issueNumber = Number(process.env.ISSUE_NUMBER || "0");
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if (!issueNumber) {
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core.setFailed("ISSUE_NUMBER is required (set inputs.issue_number for workflow_dispatch).");
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return;
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}
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const triggerLabel = (process.env.TRIGGER_LABEL || "").trim();
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if (!triggerLabel) {
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core.info("No trigger label found; skipping.");
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return;
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}
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function parseProviderModelFromLabel(label) {
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const raw = String(label || "").trim();
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if (!raw) return null;
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// Explicit formats:
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// - llm:<provider>:<model>
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// - <provider>:<model> (provider in {openai, gemini, anthropic})
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let m = raw.match(/^llm:([^:]+):(.+)$/i);
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if (m) return { provider: m[1].toLowerCase(), model: m[2].trim(), raw };
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m = raw.match(/^(openai|gemini|anthropic):(.+)$/i);
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if (m) return { provider: m[1].toLowerCase(), model: m[2].trim(), raw };
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// Short formats based on common model prefixes
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if (/^gpt-/i.test(raw) || /^o\d/i.test(raw) || /^o1/i.test(raw)) {
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return { provider: "openai", model: raw, raw };
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}
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if (/^gemini/i.test(raw)) {
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const model = raw.toLowerCase() === "gemini3" ? "gemini-3" : raw;
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return { provider: "gemini", model, raw };
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}
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if (/^claude-/i.test(raw)) {
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return { provider: "anthropic", model: raw, raw };
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}
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return null;
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}
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const labelParsed = parseProviderModelFromLabel(triggerLabel);
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if (!labelParsed) {
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core.info(`Label '${triggerLabel}' does not look like a model label; skipping.`);
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return;
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}
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const provider = (process.env.LLM_PROVIDER || labelParsed.provider || "").trim().toLowerCase();
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const model = (process.env.LLM_MODEL || labelParsed.model || "").trim();
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if (!provider || !model) {
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core.setFailed(`Unable to determine provider/model from label '${triggerLabel}'.`);
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return;
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}
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const marker = `<!-- llm-issue-review:${triggerLabel} -->`;
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// Skip if already commented for this label
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const { data: comments } = await github.rest.issues.listComments({
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owner,
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repo,
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issue_number: issueNumber,
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per_page: 100,
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});
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if (comments.some(c => typeof c.body === "string" && c.body.includes(marker))) {
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core.info("A review comment for this label already exists; skipping.");
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return;
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}
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const issueResp = await github.rest.issues.get({
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owner,
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repo,
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issue_number: issueNumber,
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});
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const issue = issueResp.data;
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async function tryGetRepoFile(path) {
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try {
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const res = await github.rest.repos.getContent({
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owner,
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repo,
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path,
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});
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if (!res?.data || Array.isArray(res.data) || res.data.type !== "file") return null;
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const b64 = res.data.content || "";
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const buf = Buffer.from(b64, "base64");
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const text = buf.toString("utf8");
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return text;
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} catch (e) {
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return null;
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}
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}
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function extractLikelyPaths(text) {
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const body = String(text || "");
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const found = new Set();
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// Backticked paths
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for (const m of body.matchAll(/`([^`]+)`/g)) {
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const p = (m[1] || "").trim();
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if (p.includes("/") && !p.startsWith("http")) found.add(p);
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}
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// Loose paths (very heuristic)
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for (const m of body.matchAll(/(^|\\s)([\\w./-]+\\.[\\w]+)(\\s|$)/g)) {
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const p = (m[2] || "").trim();
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if (p.includes("/") && !p.startsWith("http")) found.add(p);
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}
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return Array.from(found).slice(0, 5);
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}
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const referencedPaths = extractLikelyPaths(issue.body || "");
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const fileSnippets = [];
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for (const p of referencedPaths) {
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const content = await tryGetRepoFile(p);
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if (!content) continue;
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const snippet = content.length > 6000 ? content.slice(0, 6000) + "\n...(truncated)..." : content;
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fileSnippets.push({ path: p, snippet });
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}
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const automationTxt = await tryGetRepoFile("AUTOMATION.txt");
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const systemPrompt = [
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"You are an expert software engineer.",
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"You are reviewing a GitHub issue and optionally some referenced code.",
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"Be specific, actionable, and concise.",
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"Prioritize correctness, security, maintainability, and tests.",
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"If information is missing, ask short clarifying questions.",
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].join(" ");
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const promptParts = [];
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promptParts.push(`Repository: ${owner}/${repo}`);
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promptParts.push(`Issue #${issueNumber}: ${issue.title || ""}`);
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promptParts.push(`Trigger label: ${triggerLabel}`);
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promptParts.push("");
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promptParts.push("Issue body:");
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promptParts.push(issue.body || "(no body)");
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if (automationTxt) {
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promptParts.push("");
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promptParts.push("AUTOMATION.txt (guidance):");
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promptParts.push(automationTxt.length > 4000 ? automationTxt.slice(0, 4000) + "\n...(truncated)..." : automationTxt);
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}
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if (fileSnippets.length) {
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promptParts.push("");
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promptParts.push("Referenced file snippets:");
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for (const f of fileSnippets) {
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promptParts.push(`---\nFile: ${f.path}\n\n${f.snippet}`);
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}
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}
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promptParts.push("");
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promptParts.push("Output format:");
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promptParts.push("- Short summary");
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promptParts.push("- Issues and risks (High/Medium/Low)");
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promptParts.push("- Proposed plan (next steps)");
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promptParts.push("- Suggested tests");
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const userPrompt = promptParts.join("\n");
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async function callOpenAI({ apiKey, baseUrl, model, messages }) {
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if (!apiKey) throw new Error("OPENAI_API_KEY is not set.");
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const url = `${baseUrl.replace(/\\/$/, "")}/chat/completions`;
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const payload = { model, messages };
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const isGpt5ish = /gpt-?5/i.test(model) || /^o\\d/i.test(model) || /^o1/i.test(model);
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if (isGpt5ish) {
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payload.max_completion_tokens = 2048;
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} else {
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payload.max_tokens = 2048;
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payload.temperature = 0.2;
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}
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const resp = await fetch(url, {
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method: "POST",
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headers: {
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"Authorization": `Bearer ${apiKey}`,
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"Content-Type": "application/json",
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},
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body: JSON.stringify(payload),
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});
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if (!resp.ok) {
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const text = await resp.text();
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throw new Error(`OpenAI API error (${resp.status}): ${text}`);
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}
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const data = await resp.json();
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const content = data?.choices?.[0]?.message?.content;
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if (!content) throw new Error("OpenAI API returned no content.");
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return content;
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}
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async function callGemini({ apiKey, model, prompt }) {
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if (!apiKey) throw new Error("GEMINI_API_KEY is not set.");
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const geminiModel = model || "gemini-1.5-pro";
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const url = `https://generativelanguage.googleapis.com/v1beta/models/${encodeURIComponent(geminiModel)}:generateContent?key=${encodeURIComponent(apiKey)}`;
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const payload = {
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contents: [{ role: "user", parts: [{ text: prompt }] }],
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generationConfig: { temperature: 0.2, maxOutputTokens: 2048 },
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};
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const resp = await fetch(url, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify(payload),
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});
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if (!resp.ok) {
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const text = await resp.text();
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throw new Error(`Gemini API error (${resp.status}): ${text}`);
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}
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const data = await resp.json();
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const parts = data?.candidates?.[0]?.content?.parts || [];
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const text = parts.map(p => p.text || "").join("").trim();
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if (!text) throw new Error("Gemini API returned no content.");
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return text;
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}
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async function callAnthropic({ apiKey, model, system, prompt }) {
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if (!apiKey) throw new Error("ANTHROPIC_API_KEY is not set.");
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const anthropicModel = model || "claude-3-5-sonnet-latest";
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const url = "https://api.anthropic.com/v1/messages";
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const payload = {
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model: anthropicModel,
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max_tokens: 2048,
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temperature: 0.2,
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system,
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messages: [{ role: "user", content: prompt }],
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};
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const resp = await fetch(url, {
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method: "POST",
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headers: {
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"x-api-key": apiKey,
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"anthropic-version": "2023-06-01",
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"content-type": "application/json",
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},
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body: JSON.stringify(payload),
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});
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if (!resp.ok) {
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const text = await resp.text();
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throw new Error(`Anthropic API error (${resp.status}): ${text}`);
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}
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const data = await resp.json();
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const text = (data?.content || []).map(p => p.text || "").join("").trim();
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if (!text) throw new Error("Anthropic API returned no content.");
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return text;
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}
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let reviewText = "";
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try {
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if (provider === "openai") {
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reviewText = await callOpenAI({
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apiKey: process.env.OPENAI_API_KEY,
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baseUrl: process.env.OPENAI_BASE_URL,
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model,
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messages: [
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{ role: "system", content: systemPrompt },
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{ role: "user", content: userPrompt },
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],
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});
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} else if (provider === "gemini") {
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reviewText = await callGemini({
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apiKey: process.env.GEMINI_API_KEY,
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model,
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prompt: `${systemPrompt}\n\n${userPrompt}`,
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});
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} else if (provider === "anthropic") {
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reviewText = await callAnthropic({
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apiKey: process.env.ANTHROPIC_API_KEY,
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model,
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system: systemPrompt,
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prompt: userPrompt,
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});
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} else {
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throw new Error(`Unsupported provider: ${provider}`);
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}
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} catch (e) {
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core.setFailed(e.message || String(e));
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return;
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}
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const commentBody = [
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marker,
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`Provider: ${provider}`,
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`Model: ${model}`,
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"",
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reviewText,
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].join("\n");
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await github.rest.issues.createComment({
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owner,
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repo,
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issue_number: issueNumber,
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body: commentBody,
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});

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