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Add weekly-issue-summary workflow and fix link-checker.md heading case (#186)
- Add workflows/weekly-issue-summary.md: weekly issue activity report with trend charts - Add docs/weekly-issue-summary.md: documentation page - Update README.md: add entry under Research, Status & Planning Workflows - Fix docs/link-checker.md: normalize section headings to title case Co-authored-by: Copilot <copilot@github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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‎README.md‎

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@@ -20,6 +20,7 @@ A sample family of reusable [GitHub Agentic Workflows](https://github.github.com
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### Research, Status & Planning Workflows
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- [📚 Weekly Research](docs/weekly-research.md) - Collect research updates and industry trends
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- [📊 Weekly Issue Summary](docs/weekly-issue-summary.md) - Weekly issue activity report with trend charts and recommendations
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- [👥 Daily Repo Status](docs/daily-repo-status.md) - Assess repository activity and create status reports
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- [👥 Daily Team Status](docs/daily-team-status.md) - Create upbeat daily team activity summaries with productivity insights
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- [📋 Daily Plan](docs/daily-plan.md) - Update planning issues for team coordination

‎docs/link-checker.md‎

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**Workflow file:** [`.github/workflows/link-checker.md`](../.github/workflows/link-checker.md)
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## What it does
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## What It Does
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The Link Checker is an automated agentic workflow that:
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4. **Remembers unfixable links** using cache memory to avoid repeated attempts
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5. **Creates pull requests** with the fixed links when changes are made
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## How it works
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## How It Works
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````mermaid
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graph LR
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This prevents the workflow from repeatedly attempting to fix links that are permanently broken.
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## When it runs
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## When It Runs
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- **Daily on weekdays** (automatic fuzzy scheduling)
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- **Manually** via workflow_dispatch (automatically enabled for fuzzy schedules)

‎docs/weekly-issue-summary.md‎

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# 📊 Weekly Issue Summary
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> For an overview of all available workflows, see the [main README](../README.md).
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The [Weekly Issue Summary workflow](../workflows/weekly-issue-summary.md?plain=1) generates a comprehensive weekly report on issue activity, including trend charts, resolution time analysis, and actionable recommendations. It runs automatically every Monday, giving maintainers a clear snapshot of repository health over the past week and the past 30 days.
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## Installation
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Add the workflow to your repository:
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```bash
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gh aw add https://github.com/githubnext/agentics/blob/main/workflows/weekly-issue-summary.md
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```
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Then compile:
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```bash
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gh aw compile
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```
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## How It Works
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````mermaid
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graph LR
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A[Monday Schedule] --> B[Collect Issue Data]
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B --> C[Generate Trend Charts]
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C --> D[Upload Charts]
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D --> E[Create Weekly Discussion]
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````
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Each Monday at 3 PM UTC, the workflow:
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1. **Collects issue data** — Queries issues opened and closed over the past 30 days, computing daily counts and resolution times
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2. **Generates trend charts** — Uses Python (pandas + matplotlib + seaborn) to produce two high-quality charts:
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- **Issue Activity Trends** — Weekly opened vs. closed counts and running open total
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- **Resolution Time Trends** — Average and median days-to-close over time
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3. **Uploads charts** — Stores charts as GitHub assets and collects their URLs
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4. **Creates a discussion** — Posts a `[Weekly Summary]` discussion in the **Audits** category with embedded charts, statistics, and recommendations
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Older `[Weekly Summary]` discussions are automatically closed when a new one is created, keeping the discussions list clean.
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## What You Get
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Each weekly discussion includes:
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- **Overview paragraph** comparing this week to last week
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- **Two embedded trend charts** showing activity and resolution patterns
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- **Key trends** highlighting common issue types, label distributions, and notable patterns
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- **Summary statistics table** with week-over-week comparisons
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- **Full issue list** in a collapsible section
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- **Recommendations** for the upcoming week
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## Configuration
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The workflow runs every Monday at 3 PM UTC. To change the schedule, edit the `cron` expression in the workflow frontmatter:
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```yaml
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on:
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schedule:
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- cron: "0 15 * * 1" # Monday 3 PM UTC
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```
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## Requirements
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The workflow requires:
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- A **GitHub Discussions** category named `audits` (create it in your repository's Discussions settings)
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- Python 3 available on the Actions runner (standard on GitHub-hosted runners)
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- Network access to install Python packages (`pandas`, `matplotlib`, `seaborn`)
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## Permissions
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The workflow uses `issues: read` permission only — it reads issue data but never modifies issues.

‎workflows/weekly-issue-summary.md‎

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---
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description: Creates weekly summary of issue activity including trends, charts, and insights every Monday
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timeout-minutes: 20
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strict: true
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on:
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schedule:
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- cron: "0 15 * * 1" # Weekly on Mondays at 3 PM UTC
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workflow_dispatch:
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permissions:
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issues: read
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engine: copilot
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network:
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allowed:
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- defaults
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- python
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tools:
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edit:
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bash:
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- "*"
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github:
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lockdown: true
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toolsets:
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- issues
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safe-outputs:
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upload-asset:
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create-discussion:
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title-prefix: "[Weekly Summary] "
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category: "audits"
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close-older-discussions: true
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steps:
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- name: Setup Python environment
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run: |
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mkdir -p /tmp/charts /tmp/data
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pip install --user --quiet numpy pandas matplotlib seaborn scipy
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python3 -c "import pandas, matplotlib, seaborn; print('Python environment ready')"
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---
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# Weekly Issue Summary
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Create a comprehensive weekly summary of issue activity for repository ${{ github.repository }}.
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## Step 1: Collect Issue Data
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Use GitHub API tools to gather data for the past 30 days:
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1. **Issue Activity Data** — Count of issues opened per day, closed per day, and running open count
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2. **Issue Resolution Data** — Average time to close issues, distribution of issue lifespans, breakdown by label
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Fetch enough issues to compute weekly and daily trends over the past 30 days. Use the GitHub toolset to query issues filtered by `created` and `closed` dates.
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## Step 2: Generate Trend Charts
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Write Python scripts to create exactly 2 high-quality trend charts and execute them via bash.
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### Chart 1: Issue Activity Trends
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Save data to `/tmp/data/issue_activity.csv` with columns: `date,opened,closed,open_total`
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Generate a multi-line chart:
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- Issues opened per week (bar or line)
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- Issues closed per week (bar or line)
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- Running total of open issues (secondary line)
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- X-axis: last 12 weeks, Y-axis: count
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- Save as `/tmp/charts/issue_activity_trends.png` at 300 DPI, 12×7 inches
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- Use seaborn whitegrid style with a professional color palette
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### Chart 2: Issue Resolution Time Trends
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Save data to `/tmp/data/issue_resolution.csv` with columns: `date,avg_days,median_days`
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Generate a line chart with moving average overlay:
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- Average time to close (7-day moving average line)
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- Median time to close
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- Shaded variance band
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- X-axis: last 30 days, Y-axis: days to resolution
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- Save as `/tmp/charts/issue_resolution_trends.png` at 300 DPI, 12×7 inches
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Run your Python scripts via bash and verify the charts exist before proceeding.
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### Python Notes
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- Use pandas for data manipulation and datetime handling
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- Use `matplotlib.pyplot` and `seaborn` for visualization
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- Apply `plt.tight_layout()` before saving
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- Handle sparse data gracefully (use bar charts if fewer than 7 data points)
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- Set `matplotlib.use('Agg')` to avoid display errors in headless environments
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## Step 3: Upload Charts
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Upload both chart images using the `upload-asset` safe output tool. Collect the returned URLs to embed in the discussion.
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## Step 4: Create Weekly Discussion
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Create a discussion with the title format: `Weekly Summary - [YYYY-MM-DD]`
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### Formatting Guidelines
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- Use `###` for main sections, `####` for subsections (discussion title is the h1)
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- Wrap long lists in `<details><summary>` collapsible sections
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- Keep critical information (overview, trends, statistics, recommendations) always visible
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- Keep optional detail (full issue lists, verbose breakdowns) in collapsible sections
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### Discussion Structure
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```markdown
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### 📊 Weekly Overview
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[1–2 paragraphs: total issues opened and closed this week, how that compares to the previous week, key theme or pattern in the issues]
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### 📈 Issue Activity Trends
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#### Weekly Activity Patterns
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![Issue Activity Trends]({chart_1_url})
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[2–3 sentences: describe the trend — are issues accumulating, being resolved quickly, or holding steady?]
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#### Resolution Time Analysis
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![Issue Resolution Trends]({chart_2_url})
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[2–3 sentences: how quickly are issues being resolved? improving or slowing down?]
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### 🔑 Key Trends
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[Bullet list of 3–5 notable patterns: common issue types, label distribution, new contributors filing issues, recurring topics, etc.]
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### 📋 Summary Statistics
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| Metric | This Week | Last Week | Trend |
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|--------|-----------|-----------|-------|
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| Issues Opened | X | X | ↑/↓/→ |
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| Issues Closed | X | X | ↑/↓/→ |
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| Currently Open | X | X | ↑/↓/→ |
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| Avg Close Time | X days | X days | ↑/↓/→ |
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<details>
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<summary><b>Full Issue List (This Week)</b></summary>
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[Numbered list of all issues opened this week with title, number, author, labels]
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</details>
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### 💡 Recommendations for Upcoming Week
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[3–5 actionable suggestions: which issues to prioritize, patterns that suggest backlog growth, labels that need attention, etc.]
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```
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## Step 5: Notes
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- If fewer than 7 days of data are available, generate charts with available data and note the limited range
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- If no issues exist this week, still create a discussion noting the quiet week
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- Always create the discussion even if charts fail to generate (omit chart sections and explain)

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