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Publish gh-aw changes as a draft pull request from a GitHub Copilot cloud agent. The workflow checks repository templates, runs required local validation, commits and pushes through runtime tools, and records the validation results in the PR.

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• Publishing validated gh-aw changes as a draft pull request
• Preparing a PR body using the repository template
• Recording passed validation commands in a pull request

Improve validation errors in gh-aw so users can see what went wrong, what input is expected, and how to fix it. Provides writing patterns, valid YAML examples, Go error-handling guidance, and checks for message quality.

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• Rewrite vague validation errors with concrete fixes
• Add valid YAML examples to configuration errors
• Standardize messages for format, type, and choice errors

Add a new safe-output message type to gh-aw, connecting configuration validation, Go parsing, and JavaScript rendering. Follow the required updates through tests, exports, documentation, and generated action bundles so the message reaches workflow output.

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• Add a new safe-output message type to gh-aw
• Support custom templates with placeholders
• Test default and customized message output

Query GitHub pull requests by author, status, or review needs, and extract selected fields as JSON. Inspect the data structure and size first, then use jq, a JSON filtering tool, to refine results.

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• Find pull requests awaiting review
• List pull request numbers and titles by author
• Identify pull requests with many changed files

Prepare an open pull request for human merging from a GitHub Copilot cloud agent. Address review threads, validate changes locally, resolve branch conflicts, and report remaining blockers, including the need for a human to rerun CI.

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• Addressing unresolved pull request review threads
• Fixing prior CI failures with local validation
• Resolving conflicts and preparing a merge handoff

Use concrete JSON and Markdown examples to structure Sergo tool caches, strategy histories, improvement tasks, and reports. The templates show which fields to record and how to organize findings for review.

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• Formatting a cached snapshot of available tools
• Recording analysis strategies and their results
• Structuring improvement tasks and analysis reports

Routes requests to design, create, update, debug, or upgrade GitHub Agentic Workflows to the appropriate instructions. It helps the agent select a workflow-specific procedure and apply any additional rules defined for the repository.

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• Designing a new workflow through questions
• Investigating failed runs and recurring errors
• Upgrading workflows and addressing deprecations

Update the gh-aw-firewall version used by gh-aw, rebuild generated files, and review release notes and configuration schemas, which define valid settings. It guides related code and test updates while identifying follow-up work that remains.

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• Updating the firewall version used by gh-aw
• Reviewing release notes for integration changes
• Checking configuration schema differences

Format gh-aw terminal output using Go struct tags, annotations attached to data fields. Set labels and section titles, format numbers and dollar amounts, omit empty fields, and render lists of records as tables.

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• Display workflow details as aligned fields
• Show job lists as terminal tables
• Format token counts and estimated costs

Build and troubleshoot canvas extensions displayed in the GitHub Copilot app's side panel. Follow guidance for scaffolding, implementing actions and inputs, preserving state, matching app themes, and checking behavior through canvas tools and extension logs.

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• Create a canvas extension for the side panel
• Check actions and input validation
• Investigate extension loading failures

Define and check GitHub Copilot instructions and specialized agents using Markdown files. It explains file locations, configuration fields, tool access, path-specific rules, and workflow integration, with examples for documentation, testing, reviews, and other development tasks.

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• Defining repository-wide coding standards
• Applying instructions to specific directories
• Creating agents for README and test writing

Add an agent execution engine to gh-aw using a shared Markdown definition where possible. The skill guides installation, authentication, configuration, and testing, with Go infrastructure changes reserved for capabilities that existing settings cannot express.

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• Adding an agent execution engine to gh-aw
• Defining CLI installation, authentication, and MCP settings
• Extending shared behavior settings when existing options fall short

Guide changes to gh-aw internals, including workflow validation, session scoring, and GitHub output handling. Find implementation locations and design rules for configuration checks, YAML compatibility, and limits on log responses returned to agents.

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• Choose where to add workflow validation
• Review changes to session scoring
• Investigate YAML parsing differences

Guide gh-aw release preparation and assess whether command-line changes break existing usage. The reference covers changesets, which record release changes, version previews, release steps, firewall log classification, and summaries of Go module usage.

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• Preview a release version and changelog
• Assess command-line compatibility and changeset types
• Interpret allowed and blocked traffic in firewall logs

Review and implement gh-aw workflows and Go code with security checks for untrusted input, shell commands, dependency versions, and permissions. The skill also covers event-specific conditions and tools that inspect workflows for errors and security risks without running them.

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• Reviewing issue and PR input handling for injection risks
• Checking conditions in workflows with multiple event triggers
• Reviewing action version pins and job permissions

Write and edit gh-aw documentation using Diátaxis, a framework that separates learning, task instructions, reference, and explanations. Apply Astro Starlight Markdown conventions while keeping examples focused and removing repeated or unnecessary content.

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• Write a beginner tutorial with a working result
• Create a guide for a specific task
• Standardize CLI and API reference pages

Create and manage coding tasks for GitHub Copilot from the command line. The skill covers writing clear implementation requests, listing and updating sessions, and connecting task creation to automated GitHub workflows that produce pull requests for review.

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• Request bug fixes from GitHub Copilot
• List and update existing coding tasks
• Create implementation tasks from labeled issues

Create GitHub Copilot coding tasks from plain-language instructions using the GitHub CLI extension. Tasks become issues that trigger code changes and pull requests for review, with commands to list tasks, inspect details, and update instructions.

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• Delegate a bug fix to GitHub Copilot
• Create a task from a written specification
• List tasks and inspect their details

Find and review pull requests created with GitHub Copilot using author searches, branch names, and Git history. Follow practical checks for changes and tests, and export request lists to CSV to track contributions.

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• Finding pull requests created by Copilot
• Checking changes, commit history, and tests before merging
• Exporting Copilot contribution lists to CSV

Query GitHub issues and extract or summarize selected fields using jq, a JSON filtering tool. Inspect data structure and size before retrieving detailed results, then filter by status, author, labels, comments, or creation date.

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• Finding issues with a specific label or author
• Counting retrieved issues by status
• Identifying old issues or active discussions

Use this reference to configure GitHub access for AI agents through MCP, a protocol for connecting agents to tools. Covers tool selection, authentication, read-only access, and migration of existing workflow settings.

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• Configuring GitHub access for an AI agent
• Selecting tools for issues and pull requests
• Migrating allowed lists to toolsets

Add custom Go code checkers to gh-aw, with guidance on tests, registration, and running all checks. Handle equivalent syntax consistently and optionally limit performance rules using test coverage, which records which code tests execute.

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• Adding a custom Go code checking rule
• Testing rules against equivalent syntax forms
• Registering a checker in the shared runner

Guides the refactoring of large JavaScript files into smaller CommonJS modules within gh-aw. It covers source placement, function exports, tests, and action builds so extracted code remains testable and is included in generated actions.

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• Splitting a large JavaScript file by responsibility
• Extracting utilities shared across workflows
• Adding tests for extracted modules

Turn a code pattern fixed in a pull request into a custom Go linter, a tool that checks code without running it. Covers a focused detection rule, tests, and registration in the linter runner.

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• Turning a PR fix into a repeatable code check
• Adding a custom Go linter
• Testing cases that should and should not be flagged