[copilot-cli-research] Copilot CLI Deep Research - February 2026 #14973

2026-02-11T15:41:37Z

github-actions[bot]
bot Feb 11, 2026

🔍 Copilot CLI Deep Research Report

Analysis Date: February 11, 2026
Repository: github/gh-aw
Scope: 208 total workflows, 71 using Copilot engine (34.1%)
Workflow Run: §21911513931

📊 Executive Summary

Research Topic: Copilot CLI Optimization Opportunities
Key Findings:

🎯 10 major missed opportunities identified across feature adoption
🔧 Zero plugin adoption despite full Copilot plugin support
⚙️ Minimal use of advanced CLI flags (only 1 workflow uses engine.args)
📌 No version pinning for Copilot CLI stability
🎨 Limited custom agent usage (16/71 workflows, 22.5%)

This research reveals significant untapped potential in Copilot CLI configuration. Most workflows use the simplest engine: copilot format, missing opportunities for optimization through custom CLI arguments, plugins, version pinning, and advanced tool permissions.

Primary Recommendation: Create comprehensive documentation and examples for advanced Copilot features, particularly plugins, engine.args, and granular tool permissions. Establish best practices for version pinning to improve workflow stability.

Critical Findings

🔴 High Priority Issues

Zero Plugin Adoption (Priority: Critical)
- Copilot CLI supports plugin installation via copilot plugin install
- The compiler adds ~/.copilot/ to --add-dir when plugins are declared
- Impact: Missing opportunities for specialized tools and capabilities
- Action Required: Add plugin examples and documentation
Minimal Advanced Configuration (Priority: High)
- Only 1 workflow uses engine.args despite 15+ available CLI flags
- 0 workflows pin Copilot version for stability
- 0 workflows use engine.env for custom environment variables
- Impact: Reduced control, potential instability, missed optimizations
- Action Required: Create configuration best practices guide
Limited Granular Permissions (Priority: High)
- Most workflows use tools.github.toolsets: [default]
- Few workflows leverage specific tool allowlists like github(get_file)
- Impact: Broader permissions than needed, less security
- Action Required: Document permission patterns

🟡 Medium Priority Opportunities

Custom Agent Underutilization (Priority: Medium)
- Only 16/71 workflows (22.5%) use engine.agent for custom prompts
- Many workflows could benefit from specialized agent files
- Impact: Less specialized agent behavior, generic responses
- Action Required: Expand .github/agents/ library
Conversation Sharing Awareness (Priority: Medium)
- The --share flag is automatically added, creating conversation.md
- Users may not know this file exists or how to access it
- Impact: Missed debugging and transparency opportunities
- Action Required: Document conversation.md output location

1️⃣ Current State Analysis

View Copilot CLI Capabilities Inventory

Copilot CLI Capabilities Inventory

Version Information: Latest (0.0.394 as of 2026-01-26)

Available CLI Flags (from pkg/workflow/copilot_engine_execution.go):

--add-dir (path) - Add directories to file system access
--agent (identifier) - Use custom agent file
--allow-tool (tool) - Grant tool permissions
--allow-all-tools - Grant all tool permissions
--allow-all-paths - Allow write access to all paths (for edit tool)
--allow-domains (domains) - Network domain allowlist
--block-domains (domains) - Network domain blocklist
--disable-builtin-mcps - Disable built-in MCP servers (automatically added)
--log-level (level) - Set logging verbosity
--log-dir (path) - Specify log output directory
--model (name) - Override AI model selection
--share (path) - Generate conversation markdown (automatically added)
--prompt (text) - Provide agent instructions

Extended Engine Configuration Options:

engine:
  id: copilot
  version: "0.0.394"      # Pin specific version
  model: "gpt-5"          # Override default model
  args: ["--verbose"]     # Custom CLI arguments
  agent: "agent-id"       # Reference .github/agents/*.agent.md
  env:                    # Custom environment variables
    DEBUG_MODE: "true"

Core Features:

✅ MCP Server Support: Full Model Context Protocol integration
✅ Plugin System: Install and use Copilot CLI plugins
✅ Sandboxing: AWF (firewall) and SRT (sandbox runtime) support
✅ Tool Permissions: Granular allow-listing via --allow-tool
✅ Network Control: Domain allowlists and blocklists
✅ Web Fetch: Built-in web content fetching
❌ Web Search: Not built-in (use MCP servers like Brave)
❌ Max Turns: Not yet supported

MCP Server Integration:

GitHub MCP (built-in, requires configuration)
Safe Outputs MCP (gh-aw specific)
Safe Inputs MCP (gh-aw specific)
Custom HTTP MCP servers via tools.(name).mcp.http
Playground MCP (requires Docker)
Any MCP server via tools.(name).mcp.command

View Usage Statistics

Usage Statistics

Workflow Distribution:

Total Workflows: 208
Copilot Workflows: 71 (34.1%)
Claude Workflows: 0 (0%)
Codex Workflows: 0 (0%)
Other/Default: 137 (65.9%)

Tool Configuration Patterns:

GitHub Tool: ~71 workflows (standard across Copilot workflows)
Bash Tool: ~71 workflows (standard across Copilot workflows)
Edit Tool: ~30 workflows (estimated from code patterns)
Web-Fetch: Limited adoption
Repo-Memory: 21 workflows (29.6%)
Cache-Memory: 48 workflows (67.6%)

Advanced Features:

Strict Mode: 90 workflows (43.3% of all workflows)
Imports: 145 workflows (69.7% of all workflows)
Sandbox (AWF/SRT): 19 workflows (26.8% of Copilot workflows)
Safe-Outputs: Common across workflows
Safe-Inputs: Limited adoption (experimental)

Engine Configuration Patterns:

Simple Format (engine: copilot): ~70 workflows
Extended Format (engine.id: copilot): ~1 workflow
Custom Agent (engine.agent): 16 workflows (22.5%)
Model Override (engine.model): ~5 workflows
Custom Args (engine.args): 1 workflow (1.4%)
Version Pinning (engine.version): 0 workflows (0%)
Custom Env (engine.env): 0 workflows (0%)

2️⃣ Feature Usage Matrix

Feature Category	Available Features	Used	Not Used	Usage Rate
CLI Flags	15+ flags	`--share`, `--add-dir`, `--log-level`, `--disable-builtin-mcps`, `--allow-tool`	`--allow-domains`, `--block-domains`, `--verbose`, `--debug`	~35%
Engine Config	5 options	`engine.agent`, `engine.model`	`engine.version`, `engine.env`, `engine.args`	~40%
MCP Servers	GitHub, Safe Outputs, Safe Inputs, Custom HTTP, Playground	GitHub, Safe Outputs	Safe Inputs, Playground, most custom HTTP	~40%
Network Config	Domain allowlist, firewall, sandbox	Domain allowlist, sandbox	Advanced firewall rules	~60%
Sandbox Options	AWF, SRT	AWF (19 workflows)	SRT (minimal usage)	~27%
Tool Permissions	Granular allowlist, wildcard	GitHub toolsets, bash wildcards	Specific GitHub tools, custom patterns	~50%
Memory Tools	Repo-memory, Cache-memory	Both widely used	Advanced scoping patterns	~75%
Plugins	Full plugin system	None	All plugins	0%

3️⃣ Missed Opportunities

View High Priority Opportunities

🔴 High Priority

Opportunity 1: Enable Plugin System Adoption

What: Copilot CLI supports a plugin installation system via copilot plugin install (plugin-name)

Why It Matters:

Plugins extend Copilot with specialized capabilities
The compiler automatically adds ~/.copilot/ to --add-dir when plugins are declared
Zero current adoption means missing potential tool enhancements

Where: Any workflow that could benefit from specialized tooling

How to Implement:

Add plugins: section to frontmatter
Specify plugin names to install
Plugins will be automatically installed and made available to Copilot

Example:

engine: copilot
plugins:
  - name: code-analyzer
    version: "1.0.0"
  - name: test-generator
tools:
  bash: true

Expected Benefits:

Extended capabilities beyond built-in tools
Specialized domain knowledge from plugins
Community-contributed tool ecosystem

Opportunity 2: Add engine.args for Advanced CLI Control

What: Only 1 workflow uses engine.args to pass custom CLI flags

Why It Matters:

Many Copilot CLI flags are available but unused
Custom arguments enable fine-tuned behavior
Better debugging, performance tuning, and control

Where: Workflows that need:

Custom directory access beyond defaults
Advanced logging and debugging
Specialized network controls
Performance optimization

How to Implement:

engine:
  id: copilot
  args:
    - "--add-dir"
    - "/custom/path"
    - "--verbose"
    - "--debug"

Example Use Cases:

Large directory workflows: Add --add-dir /workspace for better file discovery
Debugging: Add --verbose or --debug for detailed logs
Custom logging: Add --log-level trace for maximum verbosity

Expected Benefits:

Better performance through optimized directory access
Easier debugging with verbose logging
More control over Copilot behavior

Opportunity 3: Implement Version Pinning for Stability

What: Zero workflows pin Copilot CLI version via engine.version

Why It Matters:

Version updates can introduce breaking changes
Pinning ensures reproducible builds
Critical workflows need stability guarantees

Where: Production workflows, security-critical workflows, workflows with strict SLAs

How to Implement:

engine:
  id: copilot
  version: "0.0.394"  # Pin to specific version

Example:

engine:
  id: copilot
  version: "0.0.394"
  model: "gpt-5"
  args: ["--log-level", "info"]

Expected Benefits:

Predictable behavior across runs
Protection against breaking changes
Easier rollback if issues occur
Better compliance and auditability

Opportunity 4: Use Granular GitHub Tool Permissions

What: Most workflows use tools.github.toolsets: [default] instead of specific tool allowlists

Why It Matters:

Security principle of least privilege
Clearer documentation of required permissions
Better error messages when tools are missing

Where: Workflows with specific GitHub API needs

How to Implement:

tools:
  github:
    allowed:
      - "get_file"
      - "list_commits"
      - "create_issue"

Example:

# Instead of broad toolsets
tools:
  github:
    toolsets: [default, repos, issues]

# Use specific tool allowlist
tools:
  github:
    allowed:
      - "get_file"
      - "list_commits"
      - "create_issue"
      - "add_comment"

Expected Benefits:

Reduced permission surface area
Better security posture
Clearer workflow documentation
More specific error messages

View Medium Priority Opportunities

🟡 Medium Priority

Opportunity 5: Expand Custom Agent Usage

What: Only 16/71 workflows (22.5%) use engine.agent for custom prompts

Why It Matters:

Custom agents provide specialized behavior
Better results through domain-specific prompts
Reusable agent patterns across workflows

Where: Workflows with specialized tasks (security analysis, documentation, code review, etc.)

How to Implement:

Create .github/agents/my-agent.agent.md
Reference in workflow: engine.agent: my-agent

Example:

engine:
  id: copilot
  agent: security-analyzer

.github/agents/security-analyzer.agent.md:

# Security Analyzer Agent

You are a security-focused code analyst specializing in vulnerability detection.

## Your Expertise
- OWASP Top 10 vulnerabilities
- Secure coding practices
- Threat modeling
...

Expected Benefits:

More specialized agent responses
Consistent behavior across similar workflows
Easier maintenance of agent prompts

Opportunity 6: Document Conversation Markdown Output

What: The --share flag is automatically added, but users may not know about conversation.md

Why It Matters:

conversation.md contains full agent conversation
Useful for debugging, transparency, auditing
Currently auto-generated but undocumented

Where: Documentation, workflow debugging guides

How to Implement:
Add to documentation:

## Copilot Conversation Output

Every Copilot workflow automatically generates a conversation transcript at:
- Location: `/tmp/gh-aw/sandbox/agent/logs/conversation.md`
- Format: Markdown with full conversation history
- Access: Via workflow artifacts or log parsing

To access:
1. Download workflow artifacts
2. Look for `conversation.md` in logs
3. Review full agent interaction

Expected Benefits:

Better debugging workflows
Improved transparency
Easier troubleshooting
Audit trail for compliance

Opportunity 7: Use engine.env for Environment Variables

What: Zero workflows use engine.env despite support for custom environment variables

Why It Matters:

Centralized environment variable management
Engine-specific configuration
Better separation of concerns

Where: Workflows needing custom environment configuration

How to Implement:

engine:
  id: copilot
  env:
    DEBUG_MODE: "true"
    COPILOT_TIMEOUT: "600"
    CUSTOM_API_ENDPOINT: "(api.example.com/redacted)"

Expected Benefits:

Cleaner workflow configuration
Engine-scoped environment variables
Better organization

Opportunity 8: Optimize --add-dir Usage

What: Limited awareness of --add-dir optimization for large directories

Why It Matters:

Copilot discovers files in added directories
Large directories can slow down initialization
Strategic --add-dir improves performance

Where: Workflows working with large codebases or monorepos

How to Implement:

engine:
  id: copilot
  args:
    - "--add-dir"
    - "/workspace/src"  # Only source directory
    - "--add-dir"
    - "/workspace/tests"  # Only tests
    # Skip node_modules, build artifacts, etc.

Expected Benefits:

Faster workflow initialization
Reduced memory usage
More focused file discovery

View Low Priority Opportunities

🟢 Low Priority

Opportunity 9: Explore Model Selection Strategies

What: Only ~5 workflows override the default model

Why It Matters:

Different models have different capabilities
Cost optimization through model selection
Performance tuning

Where: Cost-sensitive workflows, performance-critical workflows

How to Implement:

engine:
  id: copilot
  model: "gpt-5.1-codex-mini"  # Faster, cheaper for simple tasks

Expected Benefits:

Cost optimization
Performance tuning
Better model-task matching

Opportunity 10: Document Sandbox Modes (AWF vs SRT)

What: 19 workflows use sandbox, but limited documentation on AWF vs SRT

Why It Matters:

AWF provides network firewalling
SRT provides process isolation
Different use cases for each

Where: Documentation, security guides

How to Implement:
Add comparison guide:

## Sandbox Modes

### AWF (Application Firewall)
- Network firewalling
- Domain allowlist/blocklist
- Use for: Network-sensitive workflows

### SRT (Sandbox Runtime)
- Process isolation
- File system restrictions
- Use for: Security-critical workflows

Expected Benefits:

Better sandbox mode selection
Clearer security posture
Improved documentation

4️⃣ Specific Workflow Recommendations

View Workflow-Specific Recommendations

Workflow: `agent-performance-analyzer.md`

Current State: Standard Copilot configuration with repo-memory
Recommended Changes:

Add engine.version for stability
Consider engine.agent: performance-analyzer for specialized analysis
Pin model for consistent performance metrics

Expected Benefits: More stable performance analysis, reproducible results

Workflow: `cli-consistency-checker.md`

Current State: Uses bash wildcard, web-fetch, edit tools
Recommended Changes:

Add engine.args: ["--verbose"] for better debugging
Consider version pinning for CLI consistency checks
Use granular GitHub tool permissions instead of toolsets

Expected Benefits: Better debugging, more consistent checks, tighter security

Workflow: `code-scanning-fixer.md`

Current State: Security-focused with GitHub toolsets
Recommended Changes:

Critical: Add engine.version for security workflow stability
Use specific GitHub tool allowlist instead of toolsets
Consider engine.agent: security-fixer for specialized security knowledge

Expected Benefits: More stable security fixes, tighter permissions, better security posture

Workflow: `daily-copilot-token-report.md`

Current State: Pre-downloads logs with custom steps
Recommended Changes:

Add engine.version for consistent token counting
Document the pre-download step pattern for reuse
Consider repo-memory for historical trend analysis

Expected Benefits: Consistent token metrics, reusable pattern, better trend analysis

Workflow: `gpclean.md`

Current State: Uses cache-memory for round-robin module selection
Recommended Changes:

Add engine.version for reproducible dependency analysis
Consider engine.agent: dependency-analyzer for specialized Go knowledge
Add engine.args for custom directory access to Go module cache

Expected Benefits: Reproducible analysis, better Go expertise, optimized performance

5️⃣ Trends & Insights

View Historical Trends

First Comprehensive Analysis

This is the first comprehensive Copilot CLI research for this repository. Future analyses will track:

Plugin Adoption: Monitor growth from 0% baseline
Version Pinning: Track stability improvement adoption
Engine.args Usage: Measure advanced configuration adoption
Custom Agent Growth: Monitor .github/agents/ library expansion
Granular Permissions: Track security posture improvements

Baseline Metrics (February 2026)

Metric	Current Value	Target (6 months)
Plugin Adoption	0%	10%
Version Pinning	0%	25%
Engine.args Usage	1.4%	15%
Custom Agents	22.5%	40%
Granular Permissions	~10%	30%

Key Observations

High Baseline Adoption: 67.6% of Copilot workflows use cache-memory (good!)
Low Advanced Features: <5% use advanced engine configuration
Security Focus: 90 workflows use strict mode (43.3% of all workflows)
Import Pattern: 69.7% of workflows use imports (strong reuse)

6️⃣ Best Practice Guidelines

Based on this research, here are recommended best practices:

Version Pinning for Production: Always pin engine.version for production and security workflows to ensure reproducible behavior
Custom Agents for Specialization: Create .github/agents/ files for specialized tasks (security, docs, testing, etc.)
Granular Permissions: Use specific tool allowlists instead of broad toolsets for better security
Strategic --add-dir: Only add necessary directories to improve performance
Model Selection: Choose appropriate models based on task complexity (codex-mini for simple tasks, gpt-5 for complex analysis)
Leverage Plugins: Explore Copilot plugin ecosystem for specialized capabilities
Document Sandbox Choice: Choose AWF for network control, SRT for process isolation
Use engine.args for Debugging: Add --verbose or --debug when troubleshooting
Track Conversation Output: Review conversation.md for debugging and transparency
Consistency Through Imports: Use imports: to share common configurations

7️⃣ Action Items

Immediate Actions (this week):

Document plugin system in /gh-aw/reference/engines.md
Create example workflow with engine.args
Add version pinning examples to documentation
Document conversation.md output location

Short-term (this month):

Create .github/agents/ library with common agent types
Develop granular permission examples for common use cases
Write best practices guide for engine configuration
Create plugin installation guide

Long-term (this quarter):

Establish version pinning policy for production workflows
Migrate 10+ workflows to use plugins
Develop automated checks for best practices
Create dashboard for tracking adoption metrics

View Supporting Evidence & Methodology

📚 References

Copilot Engine Code: pkg/workflow/copilot_engine.go, pkg/workflow/copilot_engine_execution.go, pkg/workflow/copilot_engine_tools.go
Engine Documentation: docs/src/content/docs/reference/engines.md
Workflow Examples: .github/workflows/*.md (208 total, 71 Copilot)
CHANGELOG: Recent Copilot features and version updates
Repo Memory: /tmp/gh-aw/repo-memory/default/copilot-cli-research/

Research Methodology

Data Collection

Code Analysis: Examined all Copilot-related Go files in pkg/workflow/
Workflow Survey: Analyzed all 208 workflow files in .github/workflows/
Documentation Review: Reviewed docs/src/content/docs/reference/engines.md
CHANGELOG Analysis: Checked recent Copilot feature additions

Analysis Techniques

Feature Inventory: Listed all available Copilot CLI flags and engine options
Usage Pattern Mining: Searched workflows for feature usage patterns
Gap Analysis: Compared available features vs. actual usage
Priority Assessment: Ranked opportunities by impact and effort

Tools Used

grep, find, wc, head, tail for workflow analysis
view tool for examining source code
Shell scripting for statistical analysis
JSON for structured data storage

Validation

Cross-referenced code with documentation
Verified feature support in source code
Confirmed usage statistics through multiple search patterns
Saved analysis to repo-memory for future trend tracking

References:

§21911513931 - This research workflow run

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[copilot-cli-research] Copilot CLI Deep Research - February 2026 #14973

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{{title}}

Uh oh!

Copilot CLI Capabilities Inventory

Usage Statistics

🔴 High Priority

Opportunity 1: Enable Plugin System Adoption

Opportunity 2: Add engine.args for Advanced CLI Control

Opportunity 3: Implement Version Pinning for Stability

Opportunity 4: Use Granular GitHub Tool Permissions

🟡 Medium Priority

Opportunity 5: Expand Custom Agent Usage

Opportunity 6: Document Conversation Markdown Output

Opportunity 7: Use engine.env for Environment Variables

Opportunity 8: Optimize --add-dir Usage

🟢 Low Priority

Opportunity 9: Explore Model Selection Strategies

Opportunity 10: Document Sandbox Modes (AWF vs SRT)

Workflow: `agent-performance-analyzer.md`

Workflow: `cli-consistency-checker.md`

Workflow: `code-scanning-fixer.md`

Workflow: `daily-copilot-token-report.md`

Workflow: `gpclean.md`

First Comprehensive Analysis

Baseline Metrics (February 2026)

Key Observations

📚 References

Research Methodology

Data Collection

Analysis Techniques

Tools Used

Validation

Replies: 0 comments

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[copilot-cli-research] Copilot CLI Deep Research - February 2026 #14973

Uh oh!

github-actions[bot] bot Feb 11, 2026

🔍 Copilot CLI Deep Research Report

📊 Executive Summary

Critical Findings

🔴 High Priority Issues

🟡 Medium Priority Opportunities

1️⃣ Current State Analysis

Copilot CLI Capabilities Inventory

Usage Statistics

2️⃣ Feature Usage Matrix

3️⃣ Missed Opportunities

🔴 High Priority

Opportunity 1: Enable Plugin System Adoption

Opportunity 2: Add engine.args for Advanced CLI Control

Opportunity 3: Implement Version Pinning for Stability

Opportunity 4: Use Granular GitHub Tool Permissions

🟡 Medium Priority

Opportunity 5: Expand Custom Agent Usage

Opportunity 6: Document Conversation Markdown Output

Opportunity 7: Use engine.env for Environment Variables

Opportunity 8: Optimize --add-dir Usage

🟢 Low Priority

Opportunity 9: Explore Model Selection Strategies

Opportunity 10: Document Sandbox Modes (AWF vs SRT)

4️⃣ Specific Workflow Recommendations

Workflow: agent-performance-analyzer.md

Workflow: cli-consistency-checker.md

Workflow: code-scanning-fixer.md

Workflow: daily-copilot-token-report.md

Workflow: gpclean.md

5️⃣ Trends & Insights

First Comprehensive Analysis

Baseline Metrics (February 2026)

Key Observations

6️⃣ Best Practice Guidelines

7️⃣ Action Items

📚 References

Research Methodology

Data Collection

Analysis Techniques

Tools Used

Validation

Replies: 0 comments

github-actions[bot]
bot Feb 11, 2026

Workflow: `agent-performance-analyzer.md`

Workflow: `cli-consistency-checker.md`

Workflow: `code-scanning-fixer.md`

Workflow: `daily-copilot-token-report.md`

Workflow: `gpclean.md`