Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add clawd-aftermath/nunchi-for-aftermath --skill radargit clone --depth 1 https://github.com/clawd-aftermath/nunchi-for-aftermathWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/clawd-aftermath/nunchi-for-aftermath/radar)<a href="https://agentmods.dev/skills/clawd-aftermath/nunchi-for-aftermath/radar"><img src="https://agentmods.dev/badge/skills/clawd-aftermath/nunchi-for-aftermath/radar/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/clawd-aftermath/nunchi-for-aftermath/radar"><img src="https://agentmods.dev/badge/skills/clawd-aftermath/nunchi-for-aftermath/radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.01199 |
| Opus 5 | $0.00009 | $0.00600 |
| Sonnet 5 | $0.00003 | $0.00240 |
| Haiku 4.5 | $0.00002 | $0.00120 |
Grade A, and why
opportunity-radar scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to opportunity-radar — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Opportunity Radar
Screens all Hyperliquid perpetual markets through a 4-stage funnel to surface the highest-conviction trading setups.
Architecture
Stage 0: BTC Macro Context (EMA 5/13 on 4h, 1h momentum)
Stage 1: Bulk Screen (~500 assets → ~70 by volume)
Stage 2: Top-N Selection (by composite liquidity score)
Stage 3: Deep Dive (multi-TF technicals + 3-pillar scoring)
Stage 4: Momentum + Final Ranking
3-Pillar Scoring (0-400 scale)
| Pillar | Weight | Signals |
|---|---|---|
| Market Structure | 35% | Volume tiers, surge, OI, OI/Vol health |
| Technicals | 40% | 4h trend, hourly trend, RSI, patterns, volume |
| Funding | 25% | Neutral (+40), favorable, unfavorable penalties |
Hard Disqualifiers
- Counter-trend on hourly structure
- Extreme RSI (>80 for LONG, <20 for SHORT)
- Strong 4h counter-trend (strength > 50)
- Volume dying on both timeframes
- Heavy unfavorable funding (>50% annualized)
- BTC macro headwind (modifier < -30)
Usage
CLI
hl radar once # Single scan
hl radar run --tick 900 # Continuous (15 min intervals)
hl radar once --json # JSON output
hl radar once --mock # With mock data (no HL connection)
hl radar status # Show last scan results
hl radar presets # List presets
Standalone
from skills.radar.scripts.standalone_runner import RadarRunner
from cli.hl_adapter import DirectHLProxy
runner = RadarRunner(hl=hl, tick_interval=900)
runner.run()
Configuration
Via YAML config or CLI flags:
--min-volume: Minimum 24h volume to qualify (default: $500K)--top-n: Assets to deep dive (default: 20)--preset: "default" or "aggressive"--score-threshold: Minimum final score (default: 150)
Agent Mandate
You are the opportunity radar. Your job is to screen the entire Hyperliquid perps universe and rank assets by trading conviction. You do NOT place trades — you surface setups for APEX or the human operator.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 124 lines · 17 tokens per session scan A d8ca2a2b15cc
opportunity-radar is a skill published in the GitHub repository clawd-aftermath/nunchi-for-aftermath (2 stars, last pushed 20d ago), licensed MIT. It adds 17 tokens to every session and 1,199 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to opportunity-radar, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
opportunity-radar
Screens all Hyperliquid perps and surfaces top trading setups.
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