feedback
Submit a bug report, feature request, or feedback to the AIWG GitHub repository — prefills system context automatically
Best use case
feedback is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Submit a bug report, feature request, or feedback to the AIWG GitHub repository — prefills system context automatically
Teams using feedback should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/feedback/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How feedback Compares
| Feature / Agent | feedback | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Submit a bug report, feature request, or feedback to the AIWG GitHub repository — prefills system context automatically
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Cursor vs Codex for AI Workflows
Compare Cursor and Codex for AI coding workflows, repository assistance, debugging, refactoring, and reusable developer skills.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
SKILL.md Source
# AIWG Feedback Submit a bug report, feature request, doc gap, or general feedback to the AIWG GitHub repository. System context (version, OS, provider, installed frameworks) is collected and prefilled automatically. ## Triggers Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description): - "report a bug" → bug report - "something isn't working" → bug report - "aiwg is broken" → bug report with doctor output - "request a feature" → feature request - "this feature is missing" → feature request - "docs are wrong" / "doc gap" → documentation issue - "file an issue" → issue submission (type selection prompt) - "give feedback about aiwg" → general feedback ## Trigger Patterns Reference | Pattern | Example | Action | |---------|---------|--------| | Bug report | "report a bug with aiwg serve" | `aiwg feedback --type bug` | | Feature request | "request a feature for aiwg" | `aiwg feedback --type feature` | | Doc gap | "the docs for mcp inject are wrong" | `aiwg feedback --type doc` | | General | "I have feedback" | `aiwg feedback` | | With context | "file this as a bug: X" | Extract title/body, run `aiwg feedback --type bug --title "..." --body "..."` | ## Behavior When triggered: 1. **Extract from conversation** (if the user described the issue): - **Type**: bug | feature | doc | other - **Title**: short phrase summarizing the issue (max 80 chars) - **Body**: structured description of what happened, what was expected 2. **Run the appropriate command**: ```bash # Interactive (when type/title/body not clear from context) aiwg feedback # With extracted type aiwg feedback --type bug # Fully extracted from conversation aiwg feedback --type bug --title "doctor crashes when .aiwg missing" --body "Running aiwg doctor in a new project with no .aiwg directory causes an unhandled exception..." # Feature request aiwg feedback --type feature --title "add --watch flag to aiwg index build" --body "..." # Doc gap aiwg feedback --type doc --title "mcp inject workflow not documented" --body "..." ``` 3. **Report the result** — confirm the issue was filed or provide the GitHub URL. ## Examples ### Example 1: Bug from conversation **User**: "aiwg doctor crashes when I run it in a new project — there's an unhandled error about missing .aiwg" **Extraction**: - Type: bug - Title: `doctor crashes when .aiwg directory is missing` - Body: description from user message **Action**: ```bash aiwg feedback --type bug \ --title "doctor crashes when .aiwg directory is missing" \ --body "Running aiwg doctor in a project with no .aiwg directory causes an unhandled exception. Steps: 1) Create a new empty directory 2) Run aiwg doctor 3) Error occurs." ``` ### Example 2: Feature request from conversation **User**: "I wish aiwg session could pass extra flags directly to the provider binary" **Extraction**: - Type: feature - Title: `aiwg session: pass-through flags to provider binary` - Body: description from user message **Action**: ```bash aiwg feedback --type feature \ --title "aiwg session: pass-through flags to provider binary" \ --body "Would be useful to be able to pass provider-specific flags through aiwg session, e.g. aiwg session -- --verbose" ``` ### Example 3: Ambiguous — ask **User**: "I have some feedback" **Clarification prompt**: "What's the feedback about? (Bug you found, feature you'd like, or something else?)" Then run interactively: ```bash aiwg feedback ``` ## Clarification Prompts If the user's intent is ambiguous: - "Is this a bug you found, a feature you'd like, or something else?" - "Can you describe what you expected vs what happened?" - "Which aiwg command or feature is this about?" ## References - @$AIWG_ROOT/src/cli/handlers/feedback.ts — Feedback command handler - @$AIWG_ROOT/docs/cli-reference.md — CLI reference (feedback section) - GitHub Issues: https://github.com/jmagly/aiwg/issues
Related Skills
execute-feedback
Execute tests on generated code and iterate until passing
aiwg-orchestrate
Route structured artifact work to AIWG workflows via MCP with zero parent context cost
venv-manager
Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.
pytest-runner
Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.
vitest-runner
Execute JavaScript/TypeScript tests with Vitest, supporting coverage, watch mode, and parallel execution. Use for JS/TS test automation.
eslint-checker
Run ESLint for JavaScript/TypeScript code quality and style enforcement. Use for static analysis and auto-fixing.
repo-analyzer
Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.
pr-reviewer
Review GitHub pull requests for code quality, security, and best practices. Use for automated PR feedback and approval workflows.
YouTube Acquisition
yt-dlp patterns for acquiring content from YouTube and video platforms
Quality Filtering
Accept/reject logic and quality scoring heuristics for media content
Provenance Tracking
W3C PROV-O patterns for tracking media derivation chains and production history
Metadata Tagging
opustags and ffmpeg patterns for applying metadata to audio and video files