[pull] main from github:main

.github/aw/actions-lock.json

@@ -38,11 +38,6 @@
       "version": "v5.0.5",
       "sha": "27d5ce7f107fe9357f9df03efb73ab90386fccae"
     },
-    "actions/cache/save@v4": {
-      "repo": "actions/cache/save",
-      "version": "v4",
-      "sha": "0057852bfaa89a56745cba8c7296529d2fc39830"
-    },
     "actions/cache/save@v5.0.5": {
       "repo": "actions/cache/save",
       "version": "v5.0.5",
@@ -158,10 +153,10 @@
       "version": "v1.24.0",
       "sha": "fc68ffb90438ef2936bbb3251622353b3dcb2f93"
     },
-    "github/codeql-action/upload-sarif@v4.35.3": {
+    "github/codeql-action/upload-sarif@v4.35.4": {
       "repo": "github/codeql-action/upload-sarif",
-      "version": "v4.35.3",
-      "sha": "e46ed2cbd01164d986452f91f178727624ae40d7"
+      "version": "v4.35.4",
+      "sha": "68bde559dea0fdcac2102bfdf6230c5f70eb485e"
     },
     "github/stale-repos@v9.0.8": {
       "repo": "github/stale-repos",
@@ -178,15 +173,16 @@
       "version": "v2.11.0",
       "sha": "cd0d9bdd65b20557f41bea4dbe43d0b5fbbfe553"
     },
-    "microsoft/apm-action@v1.5.0": {
+    "microsoft/apm-action@v1.6.0": {
       "repo": "microsoft/apm-action",
-      "version": "v1.5.0",
-      "sha": "4489b4be0069e2c8c4d44305c3d273fb9d50e647"
+      "version": "v1.6.0",
+      "sha": "275e67418e97c26025852c7e91730cf4c11baf30"
     },
-    "microsoft/apm-action@v1.5.1": {
+    "microsoft/apm-action@v1.7.2": {
       "repo": "microsoft/apm-action",
-      "version": "v1.5.1",
-      "sha": "f67b07a08b6be27a2dfdd3912aeda24af365db8e"
+      "version": "v1.7.2",
+      "sha": "",
+      "released_at": "2026-05-07T11:38:31Z"
     },
     "oven-sh/setup-bun@v2.2.0": {
       "repo": "oven-sh/setup-bun",
@@ -245,6 +241,11 @@
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       "pinned_image": "ghcr.io/github/gh-aw-firewall/agent:0.25.40@sha256:14ff567e8d9d4c2fbc5e55c973488381c71d7e0fdbe72d30ee7b8a738fd86504"
     },
+    "ghcr.io/github/gh-aw-firewall/agent:0.25.41": {
+      "image": "ghcr.io/github/gh-aw-firewall/agent:0.25.41",
+      "digest": "sha256:cb2b565d070116d4b67e355775340528b5a2c3cb18b2c9049638bcc2df681770",
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+    },
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       "image": "ghcr.io/github/gh-aw-firewall/api-proxy:0.25.18",
       "digest": "sha256:d16a40a3ca6e989896d0cef9f31b9412bb1fcc8755bafcafb95012ae1078539b",
@@ -270,6 +271,11 @@
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+    "ghcr.io/github/gh-aw-firewall/api-proxy:0.25.41": {
+      "image": "ghcr.io/github/gh-aw-firewall/api-proxy:0.25.41",
+      "digest": "sha256:fadd0de387209f69a9a7a1b8722bb5e7fdfb80ba9749a5c60f0e4cd7582a74d0",
+      "pinned_image": "ghcr.io/github/gh-aw-firewall/api-proxy:0.25.41@sha256:fadd0de387209f69a9a7a1b8722bb5e7fdfb80ba9749a5c60f0e4cd7582a74d0"
+    },
     "ghcr.io/github/gh-aw-firewall/cli-proxy:0.25.28": {
       "image": "ghcr.io/github/gh-aw-firewall/cli-proxy:0.25.28",
       "digest": "sha256:fdf310e4678ce58d248c466b89399e9680a3003038fd19322c388559016aaac7",
@@ -285,6 +291,11 @@
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+    "ghcr.io/github/gh-aw-firewall/cli-proxy:0.25.41": {
+      "image": "ghcr.io/github/gh-aw-firewall/cli-proxy:0.25.41",
+      "digest": "sha256:62171f2fa508667b8b0a9e096f826983f312e3da0ce894f80c0f83a875af60fe",
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+    },
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@@ -310,6 +321,11 @@
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+    "ghcr.io/github/gh-aw-firewall/squid:0.25.41": {
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+    },
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@@ -355,6 +371,11 @@
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       "pinned_image": "ghcr.io/github/serena-mcp-server:latest@sha256:bf343399e3725c45528f531a230f3a04521d4cdef29f9a5af6282ff0d3c393c5"
     },
+    "grafana/mcp-grafana": {
+      "image": "grafana/mcp-grafana",
+      "digest": "sha256:60a4e3a417a69eeb864a72052c53b4aa4466ff3577d6ef9bacc671f4b77d7090",
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+    },
     "mcp/arxiv-mcp-server": {
       "image": "mcp/arxiv-mcp-server",
       "digest": "sha256:6dc6bba6dfed97f4ad6eb8d23a5c98ef5b7fa6184937d54b2d675801cd9dd29e",

.github/aw/charts.md

@@ -4,8 +4,6 @@ description: Guidance for adding Python data visualization to agentic workflows
 
 # Python Data Visualization in Agentic Workflows
 
-Consult this file when creating or updating a workflow that generates charts, trend graphs, or any Python-based data visualization.
-
 ## Choosing a Shared Workflow
 
 Three shared workflows provide Python charting capabilities. Choose based on your needs:
@@ -180,24 +178,8 @@ steps:
 
 ### Agent Instructions
 
-Python scientific libraries have been installed. A temporary folder structure has been created at `/tmp/gh-aw/python/` for organizing scripts, data, and outputs.
-
-**Installed Libraries**:
-- **NumPy**: Array processing and numerical operations
-- **Pandas**: Data manipulation and analysis
-- **Matplotlib**: Chart generation and plotting
-- **Seaborn**: Statistical data visualization
-- **SciPy**: Scientific computing utilities
-
-**Directory Structure**:
-
-```
-/tmp/gh-aw/python/
-├── data/          # Store all data files here (CSV, JSON, etc.)
-├── charts/        # Generated chart images (PNG)
-├── artifacts/     # Additional output files
-└── *.py           # Python scripts
-```
+Libraries: NumPy, Pandas, Matplotlib, Seaborn, SciPy
+Directories: `/tmp/gh-aw/python/{data,charts,artifacts}`
 
 **Data Separation Requirement**
 
@@ -235,14 +217,6 @@ plt.savefig('/tmp/gh-aw/python/charts/chart.png',
             edgecolor='none')
 ```
 
-**Chart Quality Guidelines**:
-- **DPI**: Use 300 or higher for publication quality
-- **Figure Size**: Standard is 10×6 inches (adjustable based on needs)
-- **Labels**: Always include clear axis labels and titles
-- **Legend**: Add legends when plotting multiple series
-- **Grid**: Enable grid lines for easier reading
-- **Colors**: Use colorblind-friendly palettes (seaborn defaults are good)
-
 **Cache Memory for Reusable Helpers**:
 
 ```bash
@@ -283,21 +257,6 @@ plt.savefig('/tmp/gh-aw/python/charts/chart.png',
 print("Chart saved to /tmp/gh-aw/python/charts/chart.png")
 ```
 
-**Error Handling**:
-
-```python
-import os
-
-data_file = '/tmp/gh-aw/python/data/data.csv'
-if not os.path.exists(data_file):
-    raise FileNotFoundError(f"Data file not found: {data_file}")
-
-required_cols = ['category', 'value']
-missing = set(required_cols) - set(data.columns)
-if missing:
-    raise ValueError(f"Missing columns: {missing}")
-```
-
 ---
 
 ## Option C: Charts with Trending (Full Guide)
@@ -323,8 +282,6 @@ safe-outputs:
 
 ### Agent Instructions
 
-You are an expert at creating compelling trend visualizations with persistent data storage across workflow runs.
-
 **Cache-Memory Organization**:
 
 ```
@@ -513,25 +470,6 @@ print(f"✅ Trend chart generated with {len(df)} data points")
 
 ## Trends Visualization Best Practices
 
-When generating trending charts, focus on:
-
-### Time Series Excellence
-- Use line charts for continuous trends over time
-- Add trend lines or moving averages to highlight patterns
-- Include clear date/time labels on the x-axis
-- Show confidence intervals or error bands when relevant
-
-### Comparative Trends
-- Use multi-line charts to compare multiple trends
-- Apply distinct colors for each series with a clear legend
-- Consider using area charts for stacked trends
-- Highlight key inflection points or anomalies
-
-### Contextual Information
-- Show percentage changes or growth rates
-- Include baseline comparisons (year-over-year, month-over-month)
-- Add summary statistics (min, max, average, median)
-
 ### Example Chart Types
 
 **Temporal Trends**:
@@ -652,12 +590,3 @@ For workflows tracking Copilot coding agent session data:
 
 ---
 
-## Tips for Success
-
-1. **Consistency**: Use the same metric names and file paths across runs
-2. **Timestamps**: Always use ISO 8601 format in data points
-3. **Data first**: Write data to a CSV/JSON file before plotting — never inline
-4. **Quality**: DPI 300+, clear axis labels, seaborn whitegrid, white background
-5. **Retention**: Prune cache-memory data to 90 days to prevent unbounded growth
-6. **Upload**: Use `upload asset` for every chart to get embeddable URLs
-7. **Story**: Annotate significant events; add context with moving averages