Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add llama-farm/llamafarm --skill "fix-ci"
Install specific skill from multi-skill repository
# Description
Fetch GitHub CI failure information, analyze root causes, reproduce locally, and propose a fix plan. Use `/fix-ci` for current branch or `/fix-ci <run-id>` for a specific run.
# SKILL.md
name: fix-ci
description: Fetch GitHub CI failure information, analyze root causes, reproduce locally, and propose a fix plan. Use /fix-ci for current branch or /fix-ci <run-id> for a specific run.
allowed-tools: Bash, Read, Grep, Glob, Task, AskUserQuestion, EnterPlanMode
Fix CI Skill
Automates CI troubleshooting by fetching GitHub Actions failures, analyzing logs, reproducing issues locally, and creating a fix plan for user approval.
Execution Workflow
Step 1: Prerequisites Check
Verify the GitHub CLI is installed and authenticated:
gh --version && gh auth status
If gh is not installed:
- Inform user: "GitHub CLI is required. Install with: brew install gh"
- Exit gracefully
If not authenticated:
- Inform user: "Please authenticate with: gh auth login"
- Exit gracefully
Step 2: Parse Arguments
Determine the mode based on arguments:
- No arguments (
/fix-ci): Fetch failures for the current branch only - With run-id (
/fix-ci <run-id>): Fetch specific run (bypasses branch scoping)
Step 3: Fetch Failed Run
Default mode (current branch):
BRANCH=$(git branch --show-current)
gh run list --branch "$BRANCH" --status failure --limit 1 --json databaseId,name,headBranch,workflowName,createdAt
Specific run mode:
gh run view <run-id> --json databaseId,name,headBranch,workflowName,jobs,conclusion
If no failures found:
- Report: "No failed runs found for branch $BRANCH. CI is green!"
- Optionally show recent successful runs:
gh run list --branch "$BRANCH" --limit 3 --json databaseId,conclusion,workflowName,createdAt
- Exit gracefully
Step 4: Get Failure Details
Once a failed run is identified, gather comprehensive details:
RUN_ID=<the-run-id>
# Get failed jobs with their steps
gh run view $RUN_ID --json jobs --jq '.jobs[] | select(.conclusion == "failure") | {name, conclusion, steps: [.steps[] | select(.conclusion == "failure")]}'
# Get failed step logs (critical for debugging)
gh run view $RUN_ID --log-failed 2>&1 | head -500
# Get verbose run info
gh run view $RUN_ID --verbose
Log handling:
- Truncate logs to 500 lines to avoid context overflow
- Note to user: "Showing first 500 lines of failed logs. Full logs available on GitHub."
Step 5: Download Artifacts (if available)
Attempt to download any debug artifacts:
# Try common artifact names - failures are OK (not all runs have artifacts)
gh run download $RUN_ID -n "coverage" -D /tmp/ci-debug/ 2>/dev/null || true
gh run download $RUN_ID -n "test-results" -D /tmp/ci-debug/ 2>/dev/null || true
gh run download $RUN_ID -n "logs" -D /tmp/ci-debug/ 2>/dev/null || true
If artifacts downloaded, read them for additional context.
Step 6: Analyze Failure Type
Categorize the failure based on log patterns:
| Pattern | Failure Type | Root Cause Area |
|---|---|---|
FAIL:, --- FAIL, FAILED |
Test Failure | Specific test case |
ruff check, ruff format |
Lint Error | Code style/formatting |
ModuleNotFoundError, ImportError |
Import Error | Missing dependency |
TypeError, AttributeError |
Runtime Error | Type mismatch |
SyntaxError |
Syntax Error | Invalid code |
AssertionError |
Assertion Failure | Test expectation mismatch |
TimeoutError, timed out |
Timeout | Performance/hang |
PermissionError, EACCES |
Permission Error | File/resource access |
ConnectionError, ECONNREFUSED |
Network Error | External service |
Extract key information:
- Failed test name/file (if applicable)
- Error message
- Stack trace location (file:line)
- Environment variables or config issues
Step 7: Map to Local Test Commands
Determine the appropriate local command based on the CI job:
| CI Workflow/Job | Local Command |
|---|---|
test-cli |
cd cli && go test ./... |
test-python (server) |
cd server && uv run pytest -v |
test-python (rag) |
cd rag && uv run pytest -v |
test-python (config) |
cd config && uv run pytest -v |
test-python (runtime) |
cd runtimes/universal && uv run pytest -v |
lint (python) |
uv run ruff check . |
lint (go) |
cd cli && golangci-lint run |
type-check |
uv run mypy . |
build-cli |
nx build cli |
build-designer |
cd designer && npm run build |
For specific test failures, narrow down the command:
- Python: cd <dir> && uv run pytest -v <test_file>::<test_name>
- Go: cd cli && go test -v -run <TestName> ./...
Step 8: Reproduce Locally
Run the mapped local command to confirm the failure reproduces:
# Example for Python test
cd server && uv run pytest -v tests/test_api.py::test_health_check
Outcome A - Failure reproduces locally:
- Good! Continue to fix plan
- Report: "Successfully reproduced failure locally"
Outcome B - Failure does NOT reproduce locally:
- Note: "Could not reproduce locally. Possible causes:"
- Flaky test (timing-dependent)
- Environment difference (CI has different deps/config)
- Race condition
- Suggest: "Consider re-running CI with gh run rerun $RUN_ID"
- Ask user how to proceed (investigate further or skip)
Step 9: Analyze Root Cause
Based on the failure type and logs, identify:
- What failed: Specific test, lint rule, or build step
- Why it failed: The actual error condition
- Where to fix: File(s) and line(s) that need changes
- How to fix: Proposed changes
Use available tools to explore:
- Read the failing test file
- Read the code being tested
- Search for related patterns in the codebase
- Check recent changes that might have caused the failure
Step 10: Enter Plan Mode
Use EnterPlanMode to create a formal fix plan. The plan should include:
# CI Fix Plan
## Problem Statement
[Summary of the CI failure from logs]
## Failure Details
- **Run ID**: <run-id>
- **Workflow**: <workflow-name>
- **Job**: <job-name>
- **Error Type**: <categorized-type>
## Root Cause Analysis
[Explanation of why the failure occurred]
## Affected Files
- `path/to/file1.py` (line X)
- `path/to/file2.py` (line Y)
## Proposed Changes
### Change 1: [Brief description]
[Specific edit to make]
### Change 2: [Brief description]
[Specific edit to make]
## Verification Steps
1. Run: `<local-test-command>`
2. Expected: All tests pass
3. Optional: Run full test suite with `<full-suite-command>`
## Notes
- [Any caveats or considerations]
Step 11: User Approval Gate
Present the plan and wait for explicit user approval:
- User approves: Proceed to execute fixes
- User modifies: Incorporate feedback, update plan
- User rejects: Exit gracefully without changes
CRITICAL: Never make code changes without user approval.
Step 12: Execute Fix (after approval only)
- Make the proposed code changes using Edit tool
- Run local tests to verify the fix:
<local-test-command>
- Report results:
- Success: "Fix verified locally. Tests pass."
- Failure: "Fix did not resolve the issue. [details]"
IMPORTANT: Do NOT auto-commit changes. Leave committing to the user or /commit-push-pr skill.
Error Handling
| Scenario | Action |
|---|---|
| gh CLI not installed | Direct user to install: brew install gh |
| gh not authenticated | Direct user to: gh auth login |
| No failures found | Report CI is green, exit gracefully |
| Rate limit exceeded | Suggest waiting or using gh auth refresh |
| Run not found | Verify run ID, suggest gh run list to find valid IDs |
| Large logs (>500 lines) | Truncate, note full logs on GitHub |
| Local reproduction fails | Note as flaky/env issue, offer re-run option |
| Network errors | Suggest retry, check connection |
Output Format
On finding a failure:
CI Failure Found
Run: #12345 (workflow-name)
Branch: feature-branch
Failed Job: test-python
Error Type: Test Failure
Analyzing logs...
[Summary of failure]
Reproducing locally...
[Result]
Entering plan mode to propose fix...
On success (after fix):
Fix Applied
- Modified: path/to/file.py
- Verification: Tests pass locally
Next steps:
- Review the changes
- Run `/commit-push-pr` to commit and push
- CI will re-run automatically on push
Notes for the Agent
- Always scope to current branch by default - Users expect
/fix-cito fix their current work, not random failures - Truncate logs wisely - CI logs can be huge; extract the relevant error sections
- Reproduce before fixing - Don't propose fixes for issues that can't be reproduced
- Plan mode is mandatory - Always use EnterPlanMode before making changes
- Never auto-commit - The user controls when changes are committed
- Be specific in analysis - Generic advice isn't helpful; identify exact files and lines
- Handle flaky tests - If reproduction fails, acknowledge it might be flaky
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.