GitHub Workflow Automation

Automate common GitHub operations with standardized templates to reduce token consumption.

Token Consumption Analysis

Without Skill (Direct GitHub MCP Tools)

Operation	MCP Tool	Avg Tokens	Notes
Create PR	`mcp__github__create_pull_request`	~2,500	Tool metadata + body generation
Create Issue	`mcp__github__create_issue`	~1,800	Template + label reasoning
Add PR Comment	`mcp__github__add_comment_to_pending_review`	~1,200	Review comment context
List Issues	`mcp__github__list_issues`	~2,000	Pagination + filtering logic
Search Code	`mcp__github__search_code`	~3,500	Query construction + results
Update Issue	`mcp__github__update_issue`	~1,500	State reasoning

Estimated Total: ~12,500 tokens for typical PR workflow

With Skill (Template-Driven)

Operation	Template	Tokens	Savings
Create PR	`liquidation_pr_template`	~600	76% ✅
Create Issue	`task_issue_template`	~500	72% ✅
Add PR Comment	`review_comment_template`	~300	75% ✅
Commit Message	`commit_msg_template`	~200	87% ✅

Estimated Total: ~1,600 tokens (87% reduction)

Quick Start

Create Pull Request

code

User: "Create PR for Task 01 implementation"

Skill: github-workflow
└─ Generates PR with LiquidationHeatmap template:
   Title: "[Task 01] Bitcoin Interface: ZMQ Listener"
   Body: Auto-generated from template
   Labels: enhancement, task-01
   Draft: true (MVP incomplete)

Token Cost: ~600 (vs ~2,500 with MCP tool)

Templates

1. Pull Request Template

yaml

# .claude/skills/github-workflow/templates/pr_template.md

Title Pattern: "[Task XX] Module Name: Brief Description"

Body:
## Summary
{description}

## Changes
- **Deliverable**: `{file_path}`
- **Tests**: `{test_file}` ({test_count} tests)
- **Coverage**: {coverage}%
- **Integration**: Task {prev} → Task {next}

## Test Plan
- [ ] Unit tests passing
- [ ] Integration tests passing
- [ ] Coverage >{threshold}%
- [ ] Manual testing completed

## Checklist
- [ ] Code follows LiquidationHeatmap style
- [ ] Black box interface maintained
- [ ] Documentation updated
- [ ] TDD cycle completed (RED-GREEN-REFACTOR)

## Related
- Closes #{issue_number}
- Part of #epic-mempool-live

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Usage:

python

skill.create_pr(
    task="01",
    module="Bitcoin Interface",
    description="ZMQ listener for mempool transactions",
    file_path="live/backend/zmq_listener.py",
    test_file="tests/test_zmq_listener.py",
    coverage=87,
    issue_number=5
)

Output: Ready-to-use PR via mcp__github__create_pull_request

2. Issue Template

yaml

# .claude/skills/github-workflow/templates/issue_task.md

Title: "[Task XX] Module Name"

Body:
## Goal
{goal}

## Deliverables
- [ ] `{deliverable_file}`
- [ ] `{test_file}`
- [ ] Coverage >{threshold}%
- [ ] Integration with Task {prev_task}

## Acceptance Criteria
{criteria_list}

## Dependencies
- Requires: Task {prev_task}
- Blocks: Task {next_task}

## Resources
- Task file: `docs/tasks/{task_number}_{module}.md`
- Agent: {agent_name}
- Estimated: {estimate}

## Labels
- `enhancement`
- `task-{task_number}`
- `{priority}`

Usage:

python

skill.create_issue(
    task="02",
    module="Transaction Processor",
    goal="Implement liquidation heatmap visualization component",
    deliverable_file="live/backend/tx_processor.py",
    agent_name="transaction-processor",
    estimate="2-3 weeks"
)

3. PR Review Comment Template

yaml

# Review comment for specific code

## Pattern: Suggest Improvement
🔍 **Code Review**: {file}:{line}

**Issue**: {issue_description}

**Suggestion**:
```{language}
{suggested_code}

Reason: {explanation}

code


**Usage**:
```python
skill.add_review_comment(
    file="live/backend/zmq_listener.py",
    line=45,
    issue="Missing error handling for connection loss",
    suggestion="Add try/except with exponential backoff",
    language="python"
)

4. Commit Message Template

yaml

# Standardized commit format

Pattern:
[Task XX] Module: Brief description

{detailed_changes}

- Deliverable: {file}
- Tests: {test_file}
- Coverage: {coverage}%
- Integration: {integration}

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Usage:

python

skill.generate_commit_msg(
    task="01",
    module="Bitcoin Interface",
    description="Implement ZMQ listener",
    file="live/backend/zmq_listener.py",
    coverage=87
)

Output:

code

[Task 01] Bitcoin Interface: Implement ZMQ listener

- Add async ZMQ subscription to Bitcoin Core
- Implement reconnection logic with exponential backoff
- Add health check monitoring

- Deliverable: live/backend/zmq_listener.py
- Tests: tests/test_zmq_listener.py (3 tests)
- Coverage: 87%
- Integration: Task 01 → Task 02

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Workflow Automation

Workflow 1: Complete PR Creation

code

User: "Create PR for Task 01"

Skill Execution:
1. Read git diff
2. Extract changes summary
3. Count tests
4. Check coverage
5. Generate PR body from template
6. Call mcp__github__create_pull_request with template
7. Add labels (enhancement, task-01)
8. Set draft=true (if MVP incomplete)

Token Cost: ~600 (vs ~2,500 manual)

Workflow 2: Issue + Branch + PR Draft

code

User: "Setup Task 02 workflow"

Skill Execution:
1. Create issue from template → mcp__github__create_issue
2. Create branch task-02/transaction-processor → mcp__github__create_branch
3. Create draft PR → mcp__github__create_pull_request
4. Link PR to issue

Token Cost: ~1,200 (vs ~4,500 manual)

Workflow 3: Review Comment Batch

code

User: "Add review comments for PR #12"

Skill Execution:
1. Read PR diff → mcp__github__pull_request_read
2. Generate 5 review comments from template
3. Batch submit → mcp__github__add_comment_to_pending_review
4. Submit review → mcp__github__submit_pending_pull_request_review

Token Cost: ~900 (vs ~3,000 manual)

Token Savings Comparison

Scenario: Full Task Implementation (Task 01)

Without Skill:

code

1. Create branch: manual                      ~500 tokens
2. Implement code: (subagent)                ~8,000 tokens
3. Create PR with reasoning:                 ~2,500 tokens
4. Add review comments:                      ~1,800 tokens
5. Update issue:                             ~1,500 tokens
6. Generate commit msg:                      ~1,500 tokens

Total: ~15,800 tokens

With Skill:

code

1. Create branch: template                     ~200 tokens
2. Implement code: (subagent)                ~8,000 tokens
3. Create PR from template:                    ~600 tokens
4. Add review comments:                        ~300 tokens
5. Update issue:                               ~400 tokens
6. Generate commit msg:                        ~200 tokens

Total: ~9,700 tokens (38% reduction)

Savings: ~6,100 tokens per task (38% reduction on GitHub ops)

Integration with Existing Workflow

Before (Subagents Only)

code

User: "Implement Task 01"
├─ Subagent: bitcoin-onchain-expert (implement)
├─ Manual: Create PR (reasoning + GitHub MCP)
├─ Manual: Write commit message
└─ Manual: Update issue

Total: ~15,800 tokens

After (Subagents + GitHub Skill)

code

User: "Implement Task 01"
├─ Subagent: bitcoin-onchain-expert (implement)
├─ Skill: github-workflow (create PR)        ✅ -76% tokens
├─ Skill: github-workflow (commit msg)       ✅ -87% tokens
└─ Skill: github-workflow (update issue)     ✅ -73% tokens

Total: ~9,700 tokens (38% reduction)

Automatic Invocation

Triggers:

•"create PR for [task/module]"
•"create issue for Task [XX]"
•"generate commit message for [changes]"
•"add review comment to PR [number]"
•"setup workflow for Task [XX]"

Does NOT trigger:

•Complex PR review reasoning (use human or subagent)
•Code conflict resolution (use subagent)
•Strategic issue prioritization (use human)

Configuration

Enable GitHub MCP

json

// .mcp.codebase.json (already configured)
{
  "github": {
    "type": "http",
    "url": "https://api.githubcopilot.com/mcp",
    "headers": {
      "Authorization": "Bearer ${GITHUB_PAT}"
    }
  }
}

Environment Variable

bash

export GITHUB_PAT="your_github_pat_token"

Project Config

yaml

# .claude/skills/github-workflow/config.yaml
repository: "gptprojectmanager/LiquidationHeatmap"
default_branch: "main"
labels:
  - "enhancement"
  - "task-01"
  - "task-02"
  - "task-03"
  - "task-04"
  - "task-05"
reviewers:
  - "gptprojectmanager"

Best Practices

PR Best Practices

•Always use draft PRs for incomplete work
•Link to related issue (#XX)
•Include test coverage in description
•Use conventional commit style in title

Issue Best Practices

•One task per issue
•Clear acceptance criteria
•Link to task file in docs/
•Assign appropriate labels

Commit Message Best Practices

•Follow [Task XX] prefix
•Be concise but descriptive
•Include deliverable file paths
•Always add Claude attribution

Example Output

Generated PR Description

markdown

## Summary
Implement ZMQ listener for real-time mempool transaction streaming from Bitcoin Core.

## Changes
- **Deliverable**: `live/backend/zmq_listener.py`
- **Tests**: `tests/test_zmq_listener.py` (3 tests)
- **Coverage**: 87%
- **Integration**: Task 01 → Task 02 (raw tx bytes output)

## Test Plan
- [x] Unit tests passing
- [x] Integration tests passing
- [x] Coverage >80% (87%)
- [x] Manual testing with Bitcoin Core v27

## Checklist
- [x] Code follows LiquidationHeatmap style
- [x] Black box interface maintained
- [x] Documentation updated (CLAUDE.md)
- [x] TDD cycle completed (RED-GREEN-REFACTOR)

## Related
- Closes #5
- Part of #epic-mempool-live

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Token Economics Summary

Operation	Without Skill	With Skill	Savings
Create PR	2,500	600	76%
Create Issue	1,800	500	72%
PR Comments	1,200	300	75%
Commit Msg	1,500	200	87%
Total/Task	7,000	1,600	77%

Full Pipeline (5 Tasks):

•Without: 35,000 tokens
•With: 8,000 tokens
•Savings: 27,000 tokens (77%)

Limitations

What Skill Does NOT Do

•❌ Complex code review reasoning (use subagent)
•❌ Merge conflict resolution (manual)
•❌ Strategic issue prioritization (human decision)
•❌ Security vulnerability analysis (specialized tool)

What Skill DOES Do

•✅ Generate standardized PR/issue templates
•✅ Auto-fill metadata (labels, assignees, milestones)
•✅ Batch GitHub operations
•✅ Reduce token consumption on repetitive ops