Community Skill Radar
SkillDev toolsSearches 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.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Community Skill Radar skill
What this skill tells your AI
The instructions your AI receives, as published by archieindian/openclaw-superpowers in skills/openclaw-native/community-skill-radar/SKILL.md and read by ahel’s review.
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
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:
- Fetch recent posts from each configured subreddit via Reddit's public JSON API (no auth required)
- Filter for posts/comments containing OpenClaw-related keywords
- Extract pain points and feature request signals
- Score each candidate
- Deduplicate against previously seen proposals (stored in state)
- Write
PROPOSALS.mdto the repo root - Print summary to stdout
PROPOSALS.md format
# 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)
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
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.mdis gitignored by default (local working document). Add to.gitignoreif not already present.
Signals
- GitHub stars
- 72
- Forks
- 14
- Last commit
- May 2026
Advanced
- Item type
- skill
- Key
community-skill-radar- Source
- github.com/archieindian/openclaw-superpowers
github.com/archieindian/openclaw-superpowers
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