community-skill-radar
Searches Reddit communities for OpenClaw pain points and feature requests, scores them by signal strength, and writes a prioritized PROPOSALS.md for you to review and act on.
git clone --depth 1 https://github.com/ArchieIndian/openclaw-superpowers /tmp/community-skill-radar && cp -r /tmp/community-skill-radar/skills/openclaw-native/community-skill-radar ~/.claude/skills/community-skill-radarSKILL.md
# Community Skill Radar ## What it does Your best skill ideas don't come from guessing — they come from what the community is actually struggling with. Community Skill Radar scans Reddit every 3 days for posts and comments mentioning OpenClaw pain points, feature requests, and skill gaps. It scores them by signal strength (upvotes, comment depth, recurrence) and writes a prioritized `PROPOSALS.md` in the repo root. You review the proposals. You decide what to build. The radar just makes sure you never miss a signal. ## When to invoke - Automatically, every 3 days (cron) - Manually when you want a fresh pulse-check on community needs - Before planning a new batch of skills ## Subreddits searched | Subreddit | Why | |---|---| | `openclaw` | Primary OpenClaw community | | `LocalLLaMA` | Local AI users — many run OpenClaw | | `ClaudeAI` | Claude ecosystem — overlaps with OpenClaw users | | `MachineLearning` | Broader AI practitioners | | `AIAgents` | Agent-specific discussions | Custom subreddits can be configured via `--subreddits`. ## Signal scoring Each candidate is scored on 5 dimensions: | Signal | Weight | Source | |---|---|---| | Upvotes | 2x | Post/comment score | | Comment depth | 1.5x | Number of replies — more discussion = stronger signal | | Recurrence | 3x | Same pain point appearing across multiple posts | | Keyword density | 1x | Concentration of problem/request keywords | | Recency | 1.5x | Newer posts score higher (7-day decay) | ## How to use ```bash python3 radar.py --scan # Full scan, write PROPOSALS.md python3 radar.py --scan --lookback 7 # Scan last 7 days (default: 3) python3 radar.py --scan --subreddits openclaw,LocalLLaMA python3 radar.py --scan --min-score 5.0 # Only proposals scoring ≥5.0 python3 radar.py --status # Last scan summary from state python3 radar.py --history # Show past scan results python3 radar.py --format json # Machine-readable output ``` ## Cron wakeup behaviour Every 3 days at 9am: 1. Fetch recent posts from each configured subreddit via Reddit's public JSON API (no auth required) 2. Filter for posts/comments containing OpenClaw-related keywords 3. Extract pain points and feature request signals 4. Score each candidate 5. Deduplicate against previously seen proposals (stored in state) 6. Write `PROPOSALS.md` to the repo root 7. Print summary to stdout ## PROPOSALS.md format ```markdown # Skill Proposals — Community Radar *Last scanned: 2026-03-16 09:00 | 5 subreddits | 14 candidates* ## High Signal (score ≥ 8.0) ### 1. Skill auto-update mechanism (score: 12.4) - **Source:** r/openclaw — "Anyone else manually pulling skill updates?" - **Signal:** 47 upvotes, 23 comments, seen 3 times across 2 subreddits - **Pain point:** No way to update installed skills without manual git pull - **Potential skill:** `skill-auto-updater` — checks upstream repos for new versions ### 2. Context window usage dashboard (score: 9.1) - **Source:** r/LocalLLaMA — "My openclaw agent keeps losing context mid-task" - **Signal:** 31 upvotes, 18 comments - **Pain point:** No visibility into how much context each skill consumes - **Potential skill:** `context-usage-dashboard` — real-time token budget display ## Medium Signal (score 4.0–8.0) ... ## Previously Seen (already in state — not re-proposed) ... ``` ## Procedure **Step 1 — Let the cron run (or trigger manually)** ```bash python3 radar.py --scan ``` **Step 2 — Review PROPOSALS.md** Open `PROPOSALS.md` in the repo root. High-signal proposals are the ones the community is loudest about. **Step 3 — Act on proposals you want to build** For each proposal you decide to build, either: - Ask your agent to create it: `"Build a skill for <pain point> using create-skill"` - Open a GitHub issue for the community **Step 4 — Mark proposals as actioned** ```bash python3 radar.py --mark-actioned "skill-auto-updater" ``` This moves the proposal to the "actioned" list in state so it won't be re-proposed on future scans. ## State Scan results, seen proposals, and actioned items stored in `~/.openclaw/skill-state/community-skill-radar/state.yaml`. Fields: `last_scan_at`, `subreddits`, `proposals` list, `actioned` list, `scan_history`. ## Notes - Uses Reddit's public JSON API at `reddit.com/<subreddit>/search.json`. No authentication required. Rate-limited to 1 request per 2 seconds to respect Reddit's guidelines. - Does not post, comment, or interact with Reddit in any way — read-only scanning. - `PROPOSALS.md` is gitignored by default (local working document). Add to `.gitignore` if not already present.
Syncs agent daily memory and MEMORY.md to an Obsidian vault so notes are human-browsable. Use nightly or on demand.
Structured ideation before any implementation. Use when starting any non-trivial task.
Scaffolds and validates new superpowers skills. Use when creating a new skill for this repository.
Executes plans task-by-task with verification. Use when implementing a plan.
Triggers a secondary verification pass for any agent output containing factual claims, numbers, dates, or named entities before the output is acted on
Crawls a new codebase to infer stack, conventions, and key invariants, then generates a PROJECT.md context file for the agent
Handles PR review feedback by fetching comments, grouping issues, fixing one group at a time, and verifying before replies.
Detects skill name shadowing and description-overlap conflicts that cause OpenClaw to trigger the wrong skill or silently ignore one when two skills compete for the same intent.