ln-914-community-responder
Responds to unanswered GitHub discussions and issues with codebase-informed replies. Use when clearing community question backlog.
Best use case
ln-914-community-responder is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Responds to unanswered GitHub discussions and issues with codebase-informed replies. Use when clearing community question backlog.
Teams using ln-914-community-responder 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/ln-914-community-responder/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ln-914-community-responder Compares
| Feature / Agent | ln-914-community-responder | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Responds to unanswered GitHub discussions and issues with codebase-informed replies. Use when clearing community question backlog.
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.
SKILL.md Source
> **Paths:** File paths (`shared/`, `references/`, `../ln-*`) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root. If `shared/` is missing, fetch files via WebFetch from `https://raw.githubusercontent.com/levnikolaevich/claude-code-skills/master/skills/{path}`.
# ln-914-community-responder
**Type:** L3 Worker (standalone)
**Category:** 9XX Community Engagement
Responds to unanswered GitHub Discussions and Issues by analyzing the question, searching the codebase for answers, and composing a helpful reply. Supports single-item and batch modes.
---
## Overview
| Aspect | Details |
|--------|---------|
| **Input** | `$ARGUMENTS`: discussion/issue number (`#42`), `batch` (all unanswered P1), or empty (interactive) |
| **Output** | Response comment(s) published to GitHub |
| **Pattern** | Read question → Search codebase → Compose response → Fact-check → Publish |
---
## Phase 0: GitHub Discovery
**MANDATORY READ:** Load `shared/references/community_github_discovery.md`
Execute the discovery protocol. Extract:
- `{owner}/{repo}` for URLs and API calls
- `repo.id` for GraphQL mutations
- `maintainer` login (authenticated user)
- Discussion category IDs
Load strategy: check `docs/community_engagement_strategy.md` in target project, fallback to `shared/references/community_strategy_template.md`. Extract Section 5 (Engagement Metrics) and Section 6 (Tone Guide).
**MANDATORY READ:** Load `shared/references/community_discussion_formatting.md`
**MANDATORY READ:** Load [response_styles.md](references/response_styles.md)
**MANDATORY READ:** Load `shared/references/humanizer_checklist.md`
---
## Phase 1: Load Items
### Single Item Mode
If `$ARGUMENTS` contains a number (e.g., `42`, `#42`):
```bash
# For discussions
gh api graphql -f query='query($owner: String!, $name: String!) {
repository(owner: $owner, name: $name) {
discussion(number: {N}) {
id number title body
category { name }
author { login }
createdAt
answerChosenAt
comments(first: 20) {
totalCount
nodes { author { login } body createdAt }
}
}
}
}' -f owner="{owner}" -f name="{repo}"
```
```bash
# For issues (if discussion not found)
gh issue view {N} --repo {owner}/{repo} --json number,title,body,author,createdAt,comments,labels
```
### Batch Mode
If `$ARGUMENTS` is `batch`:
1. Fetch recent discussions + issues via GraphQL (last 30 days, open, sorted by created DESC)
2. Filter to P1 items: author != maintainer AND zero maintainer comments
3. Fetch full context for each item (max 10 per batch)
### Interactive Mode
If `$ARGUMENTS` is empty, list recent unanswered items and ask the user which to respond to.
---
## Phase 2: Analyze Context
For each item:
### 2a. Understand the Question
1. Read the discussion/issue body — identify the core question or problem
2. Read existing comments — check if partially answered or if follow-ups changed the scope
3. Detect item type: Q&A question, bug report, feature request, configuration help, general feedback
### 2b. Search Codebase for Answer
Based on the question type, search for relevant information:
| Question Type | Search Strategy |
|---------------|----------------|
| "How do I..." | Grep for keywords in SKILL.md files, README.md, docs/ |
| Bug report | Grep for mentioned function/file, check git log for recent fixes |
| Configuration | Read docs/tools_config.md, CLAUDE.md, relevant SKILL.md |
| Feature request | Check if feature already exists, grep for related patterns |
| Installation | Read README.md installation section, plugin.json |
```
FOR each item:
1. Extract keywords from question (function names, skill names, error messages)
2. Grep codebase for keywords (max 5 searches)
3. Read relevant files (max 3 files, prioritize SKILL.md and docs/)
4. Check git log for recent changes related to the topic
5. If answer found → proceed to Phase 3
6. If not found → mark as "needs-manual" and suggest the user respond directly
```
### 2c. Detect First-Time Poster
```bash
gh api graphql -f query='query($owner: String!, $name: String!) {
repository(owner: $owner, name: $name) {
discussions(first: 100) {
nodes { author { login } }
}
}
}' -f owner="{owner}" -f name="{repo}"
```
Count how many discussions the author has created. If 0 previous (this is their first) → flag for Welcome style.
---
## Phase 3: Classify Response Type
**MANDATORY READ:** Load [response_styles.md](references/response_styles.md) — use the classification matrix.
| Item Type | Response Style |
|-----------|---------------|
| Q&A question with answer found | **Technical Answer** |
| Bug report | **Bug Acknowledgment** |
| Feature request / idea | **Feature Acknowledgment** |
| Question already answered elsewhere | **Redirect** |
| First-time poster (any type) | **Welcome** + appropriate content style |
| Stale item with progress update | **Status Update** |
| Cannot find answer in codebase | Mark `needs-manual` — skip composition |
---
## Phase 4: Compose Response
Use the selected style template from `response_styles.md`.
### Required Elements (All Styles)
- **Thank the author** — by name if possible, acknowledge their contribution
- **Answer the question** — direct, clear, linked to source code/docs
- **Invite follow-up** — "Let us know if this helps" or similar
- **No jargon without context** — explain internal terms
- **Link to code** — at least one link to relevant file in the repo
### Batch Mode Composition
In batch mode, compose all responses first, then present ALL for user review before publishing any.
---
## Phase 5: Fact-Check
Before presenting to user, verify every claim:
1. **File paths & links** — verify each linked file exists: `ls {path}`
2. **Code references** — verify mentioned functions/classes exist: `grep -r "{name}"`
3. **Feature descriptions** — re-read source file, confirm accuracy
4. **Install/usage commands** — verify against README.md
5. **Humanizer audit** -- run the audit protocol from `humanizer_checklist.md`. If 3+ AI patterns found, rewrite flagged sections.
**Gate:** If any check fails, fix the response before proceeding.
---
## Phase 6: Review and Publish
### Single Item Mode
Present the composed response to the user. **Wait for explicit approval before publishing.**
### Batch Mode
Present ALL responses in a summary table:
```
### Batch Responses — {N} items
| # | Type | Title | Style | Status |
|---|------|-------|-------|--------|
| {number} | {Discussion/Issue} | {title} | {Technical/Bug/Welcome/...} | Ready |
| {number} | ... | ... | ... | needs-manual |
```
Then show each response body. User can approve all, approve selectively, or edit individual responses.
### Publish Discussion Comment
```bash
gh api graphql -f query='
mutation($discussionId: ID!, $body: String!) {
addDiscussionComment(input: {
discussionId: $discussionId,
body: $body
}) {
comment { url }
}
}
' -f discussionId="{discussion.id}" -f body="{response body}"
```
### Publish Issue Comment
```bash
gh issue comment {number} --repo {owner}/{repo} --body "{response body}"
```
Report the comment URL(s) to the user.
---
## Rules
- **Always require user approval** before publishing any response
- **Never close** discussions or issues — only respond
- **Never mark as answered** — let the author do it (for Q&A discussions)
- **Batch limit:** max 10 items per batch (prevent context overload)
- **needs-manual items:** report to user with GitHub URL + reason, do not attempt response
- **Tone:** per strategy Section 6 — respectful, helpful, link to code
---
## Definition of Done
- [ ] Items loaded (single, batch, or interactive selection)
- [ ] Question context analyzed + codebase searched for answers
- [ ] Response type classified per response_styles.md
- [ ] First-time posters detected and welcomed
- [ ] Response(s) composed with links to relevant code/docs
- [ ] Fact-checked (file paths, code references, commands verified)
- [ ] User approved response(s)
- [ ] Published via GraphQL/CLI, comment URL(s) reported
- [ ] needs-manual items reported to user with URLs
---
**Version:** 1.0.0
**Last Updated:** 2026-03-14Related Skills
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