Skills

Skill 5 of 54

Debugging Workflows

Debug gh-aw workflows using run logs, audits, and failure triage.

3 minutes · 673 words · 44 sections

Install

$ npx skills add github/gh-aw --skill debugging-workflows

$ npx skills add github/gh-aw

The first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.

Debugging GitHub Agentic Workflows

Use this guide to debug GitHub Agentic Workflows: download and analyze logs, audit runs, and trace workflow behavior.

Quick Start
Downloading Workflow Logs
Auditing Specific Runs
How Agentic Workflows Work
Common Issues and Solutions
Advanced Debugging Techniques
Reference Commands

Quick Start

Download Logs from Recent Runs

# Download logs from the last 24 hours
gh aw logs --start-date -1d -o /tmp/workflow-logs

# Download logs for a specific workflow
gh aw logs weekly-research --start-date -1d

# Download logs with JSON output for programmatic analysis
gh aw logs --json

Audit a Specific Run

# Audit by run ID
gh aw audit 1234567890

# Audit from a GitHub Actions URL
gh aw audit https://github.com/owner/repo/actions/runs/1234567890

# Audit with JSON output
gh aw audit 1234567890 --json

Downloading Workflow Logs

The gh aw logs command downloads workflow run artifacts and logs from GitHub Actions for analysis.

Basic Usage

# Download logs for all workflows (last 10 runs)
gh aw logs

# Download logs for a specific workflow
gh aw logs <workflow-name>

# Download with custom output directory
gh aw logs -o ./my-logs

# Filter by date range
gh aw logs --start-date 2024-01-01 --end-date 2024-01-31
gh aw logs --start-date -1w                    # Last week
gh aw logs --start-date -1mo                   # Last month

# Filter by AI engine
gh aw logs --engine copilot
gh aw logs --engine claude
gh aw logs --engine codex

# Filter by count
gh aw logs -c 5                                # Last 5 runs

# Filter by branch/tag
gh aw logs --ref main
gh aw logs --ref feature-xyz

# Filter by run ID range
gh aw logs --after-run-id 1000 --before-run-id 2000

# Filter firewall-enabled runs
gh aw logs --firewall                          # Only firewall-enabled
gh aw logs --no-firewall                       # Only non-firewall

Output Options

# Generate JSON summary
gh aw logs --json

# Parse agent logs and generate Markdown reports
gh aw logs --parse

# Generate Mermaid tool sequence graph
gh aw logs --tool-graph

# Set download timeout
gh aw logs --timeout 300                       # 5 minute timeout

Downloaded Artifacts

When you run gh aw logs, the following artifacts are downloaded for each run:

File	Description
`aw_info.json`	Engine configuration and workflow metadata
`safe_output.jsonl`	Agent’s final output content (when non-empty)
`agent_output/`	Agent logs directory
`agent-stdio.log`	Agent standard output/error logs
`aw.patch`	Git patch of changes made during execution
`workflow-logs/`	GitHub Actions job logs (organized by job)
`summary.json`	Complete metrics and run data for all runs

Example: Analyze Recent Failures

# Download failed runs from last week
gh aw logs --start-date -1w -o /tmp/debug-logs

# Check the summary for patterns
cat /tmp/debug-logs/summary.json | jq '.runs[] | select(.conclusion == "failure")'

Auditing Specific Runs

The gh aw audit command investigates a single workflow run in detail, downloading artifacts, detecting errors, and generating a report.

Basic Usage

# Audit by numeric run ID
gh aw audit 1234567890

# Audit from GitHub Actions URL
gh aw audit https://github.com/owner/repo/actions/runs/1234567890

# Audit from job URL (extracts first failing step)
gh aw audit https://github.com/owner/repo/actions/runs/1234567890/job/9876543210

# Audit from job URL with specific step
gh aw audit https://github.com/owner/repo/actions/runs/1234567890/job/9876543210#step:7:1

Output Options

# JSON output for programmatic analysis
gh aw audit 1234567890 --json

# Custom output directory
gh aw audit 1234567890 -o ./audit-reports

# Parse agent logs and firewall logs
gh aw audit 1234567890 --parse

# Verbose output
gh aw audit 1234567890 -v

Audit Report Contents

The audit command provides:

Error Detection: Errors and warnings from workflow logs
MCP Tool Usage: Statistics on tool calls by the AI agent
Missing Tools: Tools the agent tried to use but weren’t available
Execution Metrics: Duration, token usage, and cost information
Safe Output Analysis: What GitHub operations were attempted

Example: Investigate a Failed Run

# Get detailed audit report
gh aw audit 1234567890 --json > audit.json

# Extract key information
cat audit.json | jq '{
  status: .status,
  conclusion: .conclusion,
  errors: .errors,
  missing_tools: .missing_tools,
  tool_usage: .tool_usage
}'

How Agentic Workflows Work

Understanding the workflow architecture helps in debugging.

Workflow Structure

Agentic workflows use a markdown + YAML frontmatter format:

---
on:
  issues:
    types: [opened]
permissions:
  issues: write
timeout-minutes: 10
engine: copilot
tools:
  github:
    mode: remote
    toolsets: [default]
safe-outputs:
  create-issue:
    labels: [ai-generated]
---

# Workflow Title

Natural language instructions for the AI agent.

Use GitHub context like ${{ github.event.issue.number }}.

Execution Flow

1. Trigger Event (issue opened, PR created, schedule, etc.)
     ↓
2. Activation Job
   - Validates permissions
   - Processes mcp-scripts
   - Sanitizes context
     ↓
3. AI Agent Job
   - Loads MCP servers and tools
   - Executes AI agent with prompt
   - Agent makes tool calls
   - Agent produces output
     ↓
4. Safe Outputs Job
   - Processes agent output
   - Creates GitHub resources (issues, PRs, etc.)
   - Applies labels, comments
     ↓
5. Completion
   - Workflow summary generated
   - Artifacts uploaded

Key Components

Component	Purpose	Configuration
Engine	AI model to use	`engine: copilot`, `claude`, `codex`
Tools	APIs available to agent	`tools:` section with MCP servers
MCP Scripts	Context passed to agent	`mcp-scripts:` with GitHub expressions
Safe-Outputs	Resources agent can create	`safe-outputs:` with allowed operations
Permissions	GitHub token permissions	`permissions:` block
Network	Allowed network access	`network:` with domain/ecosystem lists

Compilation Process

# Compile workflow to GitHub Actions YAML
gh aw compile <workflow-name>

# Result: .github/workflows/<name>.md → .github/workflows/<name>.lock.yml

The .lock.yml file is the actual GitHub Actions workflow that runs.

Common Issues and Solutions

Missing Tool Errors

Symptoms:

Error: “Tool ‘github:read_issue’ not found”
Agent cannot access GitHub APIs

Solution: Add GitHub MCP server configuration:

tools:
  github:
    mode: remote
    toolsets: [default]

Permission Errors

Symptoms:

HTTP 403 (Forbidden) errors
“Resource not accessible” errors

Solution: Add required permissions:

permissions:
  contents: read
  issues: write
  pull-requests: write

Safe-Input Errors

Symptoms:

“missing tool configuration for mcpscripts-gh”
Environment variable not available

Solution: Configure mcp-scripts:

mcp-scripts:
  issue:
    script: |
      return { title: process.env.ISSUE_TITLE, body: process.env.ISSUE_BODY };
    env:
      ISSUE_TITLE: ${{ github.event.issue.title }}
      ISSUE_BODY: ${{ github.event.issue.body }}

Safe-Output Errors

Symptoms:

Agent tries to create resources but fails
“Safe output not enabled” errors

Solution: Enable safe-outputs:

safe-outputs:
  staged: false  # Set to false to actually create resources
  create-issue:
    labels: [ai-generated]

Cascading Safe-Output Message Failures (Process Safe Outputs step)

Symptoms:

Process Safe Outputs reports multiple failed messages in one run
One failed update_pull_request message includes a 403 workflows-permission warning
Other failed messages (for example add_comment) include Bad credentials

What this means:

Do not assume all safe-output failures share one root cause.
A 403 workflows-permission error on update_pull_request can be expected/non-fatal in some workflows.
A 401-style Bad credentials error on other messages is a separate authentication failure that needs its own fix.

Diagnostic steps:

# Summarize failed safe-output messages and types
gh aw audit <run-id>

# Include additional artifacts when diagnosis needs more context
gh aw audit <run-id> --artifacts usage,github-api,mcp,agent

# Escalate to full artifact collection for hard-to-classify failures
gh aw audit <run-id> --artifacts all

# Inspect full failing job logs to classify each message failure
gh run view <run-id> --job=<job-id> --log

Triage each failed message by its own HTTP status code and tool/action name.
Check permissions: for missing scopes when 403 errors appear.
Compare the “failed message count” against the individual failed message lines to confirm whether there are multiple independent failures.
If several credential failures cluster together in time, investigate token freshness/expiry and token source for the run.

Network Access Errors

Symptoms:

Firewall denials
URLs appearing as “(redacted)”

Solution: Configure network access:

network:
  allowed:
    - defaults
    - python    # For PyPI
    - node      # For npm
    - "api.example.com"  # Custom domains

Timeout Errors

Symptoms:

Workflow exceeds time limit
Agent loops or hangs

Solution: Increase timeout or optimize prompt:

timeout-minutes: 30  # Increase from default

Advanced Debugging Techniques

Polling In-Progress Runs

When a run is still executing:

# Poll until completion
while true; do
  output=$(gh aw audit <run-id> --json 2>&1)
  if echo "$output" | grep -q '"status":.*"\(completed\|failure\|cancelled\)"'; then
    echo "$output"
    break
  fi
  echo "⏳ Run still in progress. Waiting 45 seconds..."
  sleep 45
done

Inspecting MCP Configuration

# Inspect MCP servers for a workflow
gh aw mcp inspect <workflow-name>

# List all workflows with MCP servers
gh aw mcp list

Checking Workflow Status

# Show status of all agentic workflows
gh aw status

Downloading Specific Artifacts

# Download only the agent log artifact
GH_REPO=owner/repo gh run download <run-id> -n agent-stdio.log

Inspecting Job Logs

# View specific job logs
gh run view <run-id>
gh run view --job <job-id> --log

Analyzing Firewall Logs

# Parse firewall logs for network issues
gh aw logs --parse

# Check firewall-enabled runs
gh aw logs --firewall

Debug Mode Compilation

# Compile with verbose output
gh aw compile --verbose

# Compile with strict security checks
gh aw compile --strict

# Run security scanners
gh aw compile --actionlint --zizmor --poutine

Reference Commands

Log Analysis Commands

Command	Description
`gh aw logs`	Download logs for all workflows
`gh aw logs <workflow>`	Download logs for specific workflow
`gh aw logs --json`	Output as JSON
`gh aw logs --start-date -1d`	Filter by date
`gh aw logs --engine copilot`	Filter by engine
`gh aw logs --parse`	Generate Markdown reports

Audit Commands

Command	Description
`gh aw audit <run-id>`	Audit specific run
`gh aw audit <url>`	Audit from GitHub URL
`gh aw audit <run-id> --json`	Output as JSON
`gh aw audit <run-id> --parse`	Parse logs to Markdown

MCP Commands

Command	Description
`gh aw mcp list`	List workflows with MCP servers
`gh aw mcp inspect <workflow>`	Inspect MCP configuration

Status Commands

Command	Description
`gh aw status`	Show all workflow status
`gh aw compile`	Compile all workflows
`gh aw compile <workflow>`	Compile specific workflow
`gh aw compile --strict`	Compile with security checks

Workflow Execution Commands

Command	Description
`gh aw run <workflow>`	Trigger workflow manually
`gh workflow run <name>.lock.yml`	Alternative trigger method
`gh run watch <run-id>`	Monitor running workflow

Additional Resources

Workflow Health Monitoring Runbook (opens in a new tab) - Step-by-step investigation procedures
Common Issues Reference (opens in a new tab) - Frequently encountered issues
Error Reference (opens in a new tab) - Error codes and solutions
GitHub MCP Server Documentation - Tool configuration reference

Colophon for this issue

Colophon

How this issue was made

Source

github.com/github/gh-aw, branch main, last pushed 24 September 2026.

Licence

MIT — the text of every skill is reproduced unmodified, frontmatter included, under the upstream licence.

Discovery

54 skills found by walking the repository tree for SKILL.md, not by matching a directory convention. 4 distinct layouts observed: .claude/skills/*/SKILL.md, .github/skills/*/SKILL.md, .squad/*/skill.md, SKILL.md (repo root).

Authorship

27 of 54 skills are installed into the repository rather than published from it. They are shelved in the closing “Credited skills” part and remain the work of their own authors. A skill counts as credited when its only copy lives under one agent’s dot directory; visible directories, per-agent mirror sets, and skills installable from this repository on skills.sh all count as published.

Issue colours

Resolved from a curated brand profile — hue 295°, chroma 0.190. Two accent tones are generated per issue and each is proven against its own ground before it ships: a single accent that passes AA on both light and dark paper is arithmetically impossible.

Heading repairs

1 repair applied to this skill so the document has one h1 and no skipped levels:

Shifted “44 headings” from h1 to h2 so the skill title is the only h1.

Spec compliance

4 editorial notes across 3 of 54 skills. They are printed in the margin of each skill rather than as errors here.

Images

Images inside a skill come from the upstream repository. Where the author gave no alternative text we mark the image decorative rather than inventing a description — a plausible caption we made up is worse than none for the reader who depends on it.

Signal

Install counts come from skills.sh. They measure downloads, not quality, and an unranked repository is not an unread one.

Agent surfaces

The whole issue is available as one markdown document at /github/gh-aw.md, and each skill at its own .md URL.

Publication

Set by Skills Docs from the source repository. Body text is Literata at the reader’s chosen size and measure; code is Geist Mono. Nothing on this page was written by us except this paragraph.

Debugging GitHub Agentic Workflows

Use this guide to debug GitHub Agentic Workflows: download and analyze logs, audit runs, and trace workflow behavior.

Quick Start
Downloading Workflow Logs
Auditing Specific Runs
How Agentic Workflows Work
Common Issues and Solutions
Advanced Debugging Techniques
Reference Commands

Quick Start

Download Logs from Recent Runs

# Download logs from the last 24 hours
gh aw logs --start-date -1d -o /tmp/workflow-logs

# Download logs for a specific workflow
gh aw logs weekly-research --start-date -1d

# Download logs with JSON output for programmatic analysis
gh aw logs --json

Audit a Specific Run

# Audit by run ID
gh aw audit 1234567890

# Audit from a GitHub Actions URL
gh aw audit https://github.com/owner/repo/actions/runs/1234567890

# Audit with JSON output
gh aw audit 1234567890 --json

Downloading Workflow Logs

The gh aw logs command downloads workflow run artifacts and logs from GitHub Actions for analysis.

Basic Usage

# Download logs for all workflows (last 10 runs)
gh aw logs

# Download logs for a specific workflow
gh aw logs <workflow-name>

# Download with custom output directory
gh aw logs -o ./my-logs

# Filter by date range
gh aw logs --start-date 2024-01-01 --end-date 2024-01-31
gh aw logs --start-date -1w                    # Last week
gh aw logs --start-date -1mo                   # Last month

# Filter by AI engine
gh aw logs --engine copilot
gh aw logs --engine claude
gh aw logs --engine codex

# Filter by count
gh aw logs -c 5                                # Last 5 runs

# Filter by branch/tag
gh aw logs --ref main
gh aw logs --ref feature-xyz

# Filter by run ID range
gh aw logs --after-run-id 1000 --before-run-id 2000

# Filter firewall-enabled runs
gh aw logs --firewall                          # Only firewall-enabled
gh aw logs --no-firewall                       # Only non-firewall

Output Options

# Generate JSON summary
gh aw logs --json

# Parse agent logs and generate Markdown reports
gh aw logs --parse

# Generate Mermaid tool sequence graph
gh aw logs --tool-graph

# Set download timeout
gh aw logs --timeout 300                       # 5 minute timeout

Downloaded Artifacts

When you run gh aw logs, the following artifacts are downloaded for each run:

File	Description
`aw_info.json`	Engine configuration and workflow metadata
`safe_output.jsonl`	Agent’s final output content (when non-empty)
`agent_output/`	Agent logs directory
`agent-stdio.log`	Agent standard output/error logs
`aw.patch`	Git patch of changes made during execution
`workflow-logs/`	GitHub Actions job logs (organized by job)
`summary.json`	Complete metrics and run data for all runs

Example: Analyze Recent Failures

# Download failed runs from last week
gh aw logs --start-date -1w -o /tmp/debug-logs

# Check the summary for patterns
cat /tmp/debug-logs/summary.json | jq '.runs[] | select(.conclusion == "failure")'

Auditing Specific Runs

The gh aw audit command investigates a single workflow run in detail, downloading artifacts, detecting errors, and generating a report.

Basic Usage

# Audit by numeric run ID
gh aw audit 1234567890

# Audit from GitHub Actions URL
gh aw audit https://github.com/owner/repo/actions/runs/1234567890

# Audit from job URL (extracts first failing step)
gh aw audit https://github.com/owner/repo/actions/runs/1234567890/job/9876543210

# Audit from job URL with specific step
gh aw audit https://github.com/owner/repo/actions/runs/1234567890/job/9876543210#step:7:1

Output Options

# JSON output for programmatic analysis
gh aw audit 1234567890 --json

# Custom output directory
gh aw audit 1234567890 -o ./audit-reports

# Parse agent logs and firewall logs
gh aw audit 1234567890 --parse

# Verbose output
gh aw audit 1234567890 -v

Audit Report Contents

The audit command provides:

Error Detection: Errors and warnings from workflow logs
MCP Tool Usage: Statistics on tool calls by the AI agent
Missing Tools: Tools the agent tried to use but weren’t available
Execution Metrics: Duration, token usage, and cost information
Safe Output Analysis: What GitHub operations were attempted

Example: Investigate a Failed Run

# Get detailed audit report
gh aw audit 1234567890 --json > audit.json

# Extract key information
cat audit.json | jq '{
  status: .status,
  conclusion: .conclusion,
  errors: .errors,
  missing_tools: .missing_tools,
  tool_usage: .tool_usage
}'

How Agentic Workflows Work

Understanding the workflow architecture helps in debugging.

Workflow Structure

Agentic workflows use a markdown + YAML frontmatter format:

---
on:
  issues:
    types: [opened]
permissions:
  issues: write
timeout-minutes: 10
engine: copilot
tools:
  github:
    mode: remote
    toolsets: [default]
safe-outputs:
  create-issue:
    labels: [ai-generated]
---

# Workflow Title

Natural language instructions for the AI agent.

Use GitHub context like ${{ github.event.issue.number }}.

Execution Flow

1. Trigger Event (issue opened, PR created, schedule, etc.)
     ↓
2. Activation Job
   - Validates permissions
   - Processes mcp-scripts
   - Sanitizes context
     ↓
3. AI Agent Job
   - Loads MCP servers and tools
   - Executes AI agent with prompt
   - Agent makes tool calls
   - Agent produces output
     ↓
4. Safe Outputs Job
   - Processes agent output
   - Creates GitHub resources (issues, PRs, etc.)
   - Applies labels, comments
     ↓
5. Completion
   - Workflow summary generated
   - Artifacts uploaded

Key Components

Component	Purpose	Configuration
Engine	AI model to use	`engine: copilot`, `claude`, `codex`
Tools	APIs available to agent	`tools:` section with MCP servers
MCP Scripts	Context passed to agent	`mcp-scripts:` with GitHub expressions
Safe-Outputs	Resources agent can create	`safe-outputs:` with allowed operations
Permissions	GitHub token permissions	`permissions:` block
Network	Allowed network access	`network:` with domain/ecosystem lists

Compilation Process

# Compile workflow to GitHub Actions YAML
gh aw compile <workflow-name>

# Result: .github/workflows/<name>.md → .github/workflows/<name>.lock.yml

The .lock.yml file is the actual GitHub Actions workflow that runs.

Common Issues and Solutions

Missing Tool Errors

Symptoms:

Error: “Tool ‘github:read_issue’ not found”
Agent cannot access GitHub APIs

Solution: Add GitHub MCP server configuration:

tools:
  github:
    mode: remote
    toolsets: [default]

Permission Errors

Symptoms:

HTTP 403 (Forbidden) errors
“Resource not accessible” errors

Solution: Add required permissions:

permissions:
  contents: read
  issues: write
  pull-requests: write

Safe-Input Errors

Symptoms:

“missing tool configuration for mcpscripts-gh”
Environment variable not available

Solution: Configure mcp-scripts:

mcp-scripts:
  issue:
    script: |
      return { title: process.env.ISSUE_TITLE, body: process.env.ISSUE_BODY };
    env:
      ISSUE_TITLE: ${{ github.event.issue.title }}
      ISSUE_BODY: ${{ github.event.issue.body }}

Safe-Output Errors

Symptoms:

Agent tries to create resources but fails
“Safe output not enabled” errors

Solution: Enable safe-outputs:

safe-outputs:
  staged: false  # Set to false to actually create resources
  create-issue:
    labels: [ai-generated]

Cascading Safe-Output Message Failures (Process Safe Outputs step)

Symptoms:

Process Safe Outputs reports multiple failed messages in one run
One failed update_pull_request message includes a 403 workflows-permission warning
Other failed messages (for example add_comment) include Bad credentials

What this means:

Do not assume all safe-output failures share one root cause.
A 403 workflows-permission error on update_pull_request can be expected/non-fatal in some workflows.
A 401-style Bad credentials error on other messages is a separate authentication failure that needs its own fix.

Diagnostic steps:

# Summarize failed safe-output messages and types
gh aw audit <run-id>

# Include additional artifacts when diagnosis needs more context
gh aw audit <run-id> --artifacts usage,github-api,mcp,agent

# Escalate to full artifact collection for hard-to-classify failures
gh aw audit <run-id> --artifacts all

# Inspect full failing job logs to classify each message failure
gh run view <run-id> --job=<job-id> --log

Triage each failed message by its own HTTP status code and tool/action name.
Check permissions: for missing scopes when 403 errors appear.
Compare the “failed message count” against the individual failed message lines to confirm whether there are multiple independent failures.
If several credential failures cluster together in time, investigate token freshness/expiry and token source for the run.

Network Access Errors

Symptoms:

Firewall denials
URLs appearing as “(redacted)”

Solution: Configure network access:

network:
  allowed:
    - defaults
    - python    # For PyPI
    - node      # For npm
    - "api.example.com"  # Custom domains

Timeout Errors

Symptoms:

Workflow exceeds time limit
Agent loops or hangs

Solution: Increase timeout or optimize prompt:

timeout-minutes: 30  # Increase from default

Advanced Debugging Techniques

Polling In-Progress Runs

When a run is still executing:

# Poll until completion
while true; do
  output=$(gh aw audit <run-id> --json 2>&1)
  if echo "$output" | grep -q '"status":.*"\(completed\|failure\|cancelled\)"'; then
    echo "$output"
    break
  fi
  echo "⏳ Run still in progress. Waiting 45 seconds..."
  sleep 45
done

Inspecting MCP Configuration

# Inspect MCP servers for a workflow
gh aw mcp inspect <workflow-name>

# List all workflows with MCP servers
gh aw mcp list

Checking Workflow Status

# Show status of all agentic workflows
gh aw status

Downloading Specific Artifacts

# Download only the agent log artifact
GH_REPO=owner/repo gh run download <run-id> -n agent-stdio.log

Inspecting Job Logs

# View specific job logs
gh run view <run-id>
gh run view --job <job-id> --log

Analyzing Firewall Logs

# Parse firewall logs for network issues
gh aw logs --parse

# Check firewall-enabled runs
gh aw logs --firewall

Debug Mode Compilation

# Compile with verbose output
gh aw compile --verbose

# Compile with strict security checks
gh aw compile --strict

# Run security scanners
gh aw compile --actionlint --zizmor --poutine

Reference Commands

Log Analysis Commands

Command	Description
`gh aw logs`	Download logs for all workflows
`gh aw logs <workflow>`	Download logs for specific workflow
`gh aw logs --json`	Output as JSON
`gh aw logs --start-date -1d`	Filter by date
`gh aw logs --engine copilot`	Filter by engine
`gh aw logs --parse`	Generate Markdown reports

Audit Commands

Command	Description
`gh aw audit <run-id>`	Audit specific run
`gh aw audit <url>`	Audit from GitHub URL
`gh aw audit <run-id> --json`	Output as JSON
`gh aw audit <run-id> --parse`	Parse logs to Markdown

MCP Commands

Command	Description
`gh aw mcp list`	List workflows with MCP servers
`gh aw mcp inspect <workflow>`	Inspect MCP configuration

Status Commands

Command	Description
`gh aw status`	Show all workflow status
`gh aw compile`	Compile all workflows
`gh aw compile <workflow>`	Compile specific workflow
`gh aw compile --strict`	Compile with security checks

Workflow Execution Commands

Command	Description
`gh aw run <workflow>`	Trigger workflow manually
`gh workflow run <name>.lock.yml`	Alternative trigger method
`gh run watch <run-id>`	Monitor running workflow

Additional Resources

Workflow Health Monitoring Runbook (opens in a new tab) - Step-by-step investigation procedures
Common Issues Reference (opens in a new tab) - Frequently encountered issues
Error Reference (opens in a new tab) - Error codes and solutions
GitHub MCP Server Documentation - Tool configuration reference