Merge branch 'copilot/add-agents-to-review-issues' into copilot/merge-open-pull-requests

This commit is contained in:
GitHub Copilot
2026-03-03 03:51:53 +00:00
2 changed files with 298 additions and 0 deletions

112
.github/workflows/issue-agent.yml vendored Normal file
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name: Issue Agent
on:
issues:
types: [opened, edited, reopened, labeled]
permissions:
issues: write
jobs:
analyze-issue:
runs-on: ubuntu-latest
steps:
- name: Analyze issue and post comment
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const issueNumber = context.issue.number;
const issueTitle = context.payload.issue.title;
const issueBody = context.payload.issue.body || '(no description provided)';
const issueUser = context.payload.issue.user.login;
const labels = (context.payload.issue.labels || []).map(l => l.name).join(', ') || 'none';
// Call GitHub Models API for AI-powered analysis
const response = await fetch('https://models.inference.ai.azure.com/chat/completions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${process.env.GITHUB_TOKEN}`
},
body: JSON.stringify({
model: 'gpt-4o-mini',
messages: [
{
role: 'system',
content: `You are a rigorous technical agent reviewing GitHub issues for the "simulation-theory" repository — a research project on simulation theory, mathematics, quantum mechanics, and philosophy. Your job is to carefully read each issue and provide a thorough, structured analysis.
For each issue produce:
1. **Summary** — a concise one-paragraph summary of what the issue is about.
2. **Key Points** — bullet list of the most important observations or questions raised.
3. **Relevance to Simulation Theory** — how this issue connects to the project's themes.
4. **Suggested Actions** — concrete next steps or questions for the author.
Be rigorous, thoughtful, and constructive. Keep the tone academic and helpful.`
},
{
role: 'user',
content: `Please analyze this GitHub issue:\n\n**Title:** ${issueTitle}\n**Author:** ${issueUser}\n**Labels:** ${labels}\n\n**Description:**\n${issueBody}`
}
],
max_tokens: 1500,
temperature: 0.4
})
});
let analysisText;
if (response.ok) {
let data;
try {
data = await response.json();
} catch (error) {
console.log('Failed to parse JSON from GitHub Models API response:', error);
}
if (data && data.choices && data.choices.length > 0 && data.choices[0].message) {
analysisText = data.choices[0].message.content;
} else if (data) {
console.log('Unexpected response structure from GitHub Models API:', JSON.stringify(data));
}
} else {
console.log(`GitHub Models API returned ${response.status}: ${await response.text()}`);
}
// Fallback: structured analysis without AI
if (!analysisText) {
analysisText = `**Summary**\nIssue #${issueNumber} titled *"${issueTitle}"* was submitted by @${issueUser}. ${issueBody.length > 0 ? 'It contains a description that may include images or text.' : 'No description was provided.'}\n\n**Labels:** ${labels}\n\n**Suggested Actions**\n- Review the content of this issue and add appropriate labels if missing.\n- Respond to the author with any clarifying questions.\n- Link related issues or pull requests if applicable.`;
}
const marker = '*This comment was generated automatically by the Issue Agent workflow.*';
const commentBody = `## 🤖 Agent Analysis\n\n${analysisText}\n\n---\n${marker}`;
// Look for an existing Issue Agent comment and update it if found to avoid spamming
const { data: existingComments } = await github.rest.issues.listComments({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: issueNumber,
per_page: 100
});
const existingAgentComment = existingComments.find(c =>
c.user &&
c.user.type === 'Bot' &&
typeof c.body === 'string' &&
c.body.includes(marker)
);
if (existingAgentComment) {
await github.rest.issues.updateComment({
owner: context.repo.owner,
repo: context.repo.repo,
comment_id: existingAgentComment.id,
body: commentBody
});
} else {
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: issueNumber,
body: commentBody
});
}

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.github/workflows/pr-agent.yml vendored Normal file
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name: PR Agent
on:
pull_request_target:
types: [opened, reopened]
permissions:
pull-requests: write
contents: read
models: read
jobs:
analyze-pr:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Collect changed files
id: changed
run: |
# Ensure the base branch ref is available locally (important for fork-based PRs)
git fetch origin "${{ github.event.pull_request.base.ref }}" --no-tags --prune --depth=1
BASE="${{ github.event.pull_request.base.sha }}"
HEAD="${{ github.event.pull_request.head.sha }}"
# Compute the list of changed files between base and head; fail explicitly on error
if ! ALL_FILES=$(git diff --name-only "$BASE" "$HEAD"); then
echo "Error: failed to compute git diff between $BASE and $HEAD" >&2
exit 1
fi
# Count total changed files robustly, even when there are zero files
TOTAL=$(printf '%s\n' "$ALL_FILES" | sed '/^$/d' | wc -l | tr -d ' ')
FILES=$(echo "$ALL_FILES" | head -50 | tr '\n' ', ')
FILES="${FILES%, }"
if [ "$TOTAL" -gt 50 ]; then
REMAINING=$(( TOTAL - 50 ))
FILES="${FILES} (and ${REMAINING} more files)"
fi
{
echo 'files<<EOF'
echo "$FILES"
echo 'EOF'
} >> "$GITHUB_OUTPUT"
- name: Analyze PR and post comment
uses: actions/github-script@v7
env:
CHANGED_FILES: ${{ steps.changed.outputs.files }}
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const prNumber = context.payload.pull_request.number;
const prTitle = context.payload.pull_request.title;
const prBody = context.payload.pull_request.body || '(no description provided)';
const prUser = context.payload.pull_request.user.login;
const baseBranch = context.payload.pull_request.base.ref;
const headBranch = context.payload.pull_request.head.ref;
const changedFiles = process.env.CHANGED_FILES || 'unknown';
const additions = context.payload.pull_request.additions ?? '?';
const deletions = context.payload.pull_request.deletions ?? '?';
// Call GitHub Models API for AI-powered analysis
let response;
try {
response = await fetch('https://models.inference.ai.azure.com/chat/completions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${process.env.GITHUB_TOKEN}`
},
body: JSON.stringify({
model: 'gpt-4o-mini',
messages: [
{
role: 'system',
content: `You are a rigorous code and content review agent for the "simulation-theory" repository — a research project on simulation theory, mathematics, quantum mechanics, and philosophy. Your job is to carefully examine each pull request and provide a thorough, structured review.
For each PR produce:
1. **Summary** — a concise one-paragraph summary of the proposed changes.
2. **Changed Files Analysis** — observations about the files being modified and why they matter.
3. **Potential Concerns** — any risks, conflicts, or issues the reviewer should check.
4. **Relevance to Project Goals** — how these changes align with (or diverge from) simulation-theory research.
5. **Suggested Actions** — specific things the PR author or reviewers should do before merging.
Be rigorous, constructive, and precise. Keep the tone academic and professional.`
},
{
role: 'user',
content: `Please analyze this pull request:\n\n**Title:** ${prTitle}\n**Author:** ${prUser}\n**Base branch:** ${baseBranch} ← **Head branch:** ${headBranch}\n**Changes:** +${additions} / -${deletions} lines\n**Changed files:** ${changedFiles}\n\n**Description:**\n${prBody}`
}
],
max_tokens: 1500,
temperature: 0.4
})
});
let analysisText;
if (response.ok) {
try {
const data = await response.json();
if (data.choices && data.choices.length > 0 && data.choices[0].message) {
try {
if (response.ok) {
const data = await response.json();
if (data.choices && data.choices.length > 0 && data.choices[0].message) {
analysisText = data.choices[0].message.content;
} else {
console.log('Unexpected response structure from GitHub Models API:', JSON.stringify(data));
}
} else {
console.log(`GitHub Models API returned ${response.status}: ${await response.text()}`);
}
} catch (error) {
console.log('Error while calling or parsing response from GitHub Models API, falling back to templated analysis:', error);
}
// Fallback: structured analysis without AI
if (!analysisText) {
let changedFilesSection;
if (!changedFiles || changedFiles === 'unknown') {
changedFilesSection = 'No changed file list is available for this PR.';
} else {
const files = changedFiles.split(',').map(f => f.trim()).filter(f => f.length > 0);
if (files.length === 0) {
changedFilesSection = 'No files listed.';
} else {
changedFilesSection = files.map(f => `- ${f}`).join('\n');
}
}
analysisText = `**Summary**\nPR #${prNumber} titled *"${prTitle}"* was submitted by @${prUser} merging \`${headBranch}\` into \`${baseBranch}\`.\n\n**Changed Files**\n${changedFilesSection}\n\n**Stats:** +${additions} additions / -${deletions} deletions\n\n**Suggested Actions**\n- Review all changed files for correctness and consistency.\n- Ensure the description clearly explains the motivation for each change.\n- Verify no unintended files are included in this PR.`;
}
// Sanitize and limit the AI-generated analysis text before posting as a comment.
const MAX_COMMENT_LENGTH = 5000;
const sanitizeAnalysisText = (text) => {
if (typeof text !== 'string') {
return '';
}
// Remove script-like tags and generic HTML tags as a defense-in-depth measure.
let cleaned = text
.replace(/<\s*\/?\s*script[^>]*>/gi, '')
.replace(/<[^>]+>/g, '')
.trim();
if (cleaned.length > MAX_COMMENT_LENGTH) {
cleaned = cleaned.slice(0, MAX_COMMENT_LENGTH) +
'\n\n*Note: Output truncated to fit comment length limits.*';
}
return cleaned;
};
const safeAnalysisText = sanitizeAnalysisText(analysisText);
const comment = `## 🤖 Agent Review\n\n${safeAnalysisText}\n\n---\n*This comment was generated automatically by the PR Agent workflow.*`;
// Try to find an existing PR Agent comment to update, to avoid spamming the thread
const { data: existingComments } = await github.rest.issues.listComments({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: prNumber
});
const existingAgentComment = existingComments.find(c =>
c &&
c.body &&
c.body.includes('This comment was generated automatically by the PR Agent workflow.')
);
if (existingAgentComment) {
await github.rest.issues.updateComment({
owner: context.repo.owner,
repo: context.repo.repo,
comment_id: existingAgentComment.id,
body: comment
});
} else {
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: prNumber,
body: comment
});
}