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.
git clone --depth 1 https://github.com/austron24/kalshi-trader-pluginWrote 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/commands/austron24/kalshi-trader-plugin/alpha)<a href="https://agentmods.dev/commands/austron24/kalshi-trader-plugin/alpha"><img src="https://agentmods.dev/badge/commands/austron24/kalshi-trader-plugin/alpha/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/commands/austron24/kalshi-trader-plugin/alpha"><img src="https://agentmods.dev/badge/commands/austron24/kalshi-trader-plugin/alpha.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 | $0.00009 | $0.01879 |
| Opus 5.5 | $0.00004 | $0.00752 |
| Sonnet 5.5 | $0.00002 | $0.00376 |
| Haiku 4.5 | $0.00001 | $0.00188 |
Grade A, and why
alpha 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Alpha Opportunity Scanner
You are initiating an alpha opportunity scan on Kalshi markets. Your job is to:
- Find markets in target series (where we have research edge)
- Deduplicate to unique EVENTS (not individual brackets)
- Spawn parallel subagents to research each EVENT deeply
- Report back with pointers to all generated research
User Request
$ARGUMENTS
If no arguments provided, scan economics series by default.
Phase 1: Market Discovery
Default: Economics Series (High Edge)
If no arguments or $ARGUMENTS is empty, scan these series:
# Get all markets from our target economics series
for series in KXCPI KXFED KXFEDDECISION KXGDP KXPCE KXPCECORE KXPAYROLLS KXINFL KXGOVSHUT; do
kalshi markets --series $series --limit 100 --json 2>/dev/null
done | python3 -c "
import json, sys
all_markets = []
for line in sys.stdin:
line = line.strip()
if line:
try:
data = json.loads(line)
markets = data.get('markets', data) if isinstance(data, dict) else data
all_markets.extend(markets)
except:
pass
# Deduplicate to events
events = {}
for m in all_markets:
evt = m.get('event_ticker', m['ticker'])
if evt not in events:
events[evt] = {
'event_ticker': evt,
'title': m.get('title', '')[:60],
'close_time': m.get('close_time', ''),
'brackets': []
}
events[evt]['brackets'].append({
'ticker': m['ticker'],
'subtitle': m.get('yes_sub_title', m.get('subtitle', ''))[:30],
'yes_bid': m.get('yes_bid', 0),
'volume': m.get('volume', 0)
})
print(f'Found {len(all_markets)} markets across {len(events)} unique events')
print()
for evt, info in sorted(events.items(), key=lambda x: x[1]['close_time'])[:30]:
print(f\"{evt}|{info['title']}|{len(info['brackets'])} brackets\")
for b in info['brackets'][:3]:
print(f\" - {b['ticker']}: {b['subtitle']} @ {b['yes_bid']}c (vol: {b['volume']})\")
if len(info['brackets']) > 3:
print(f\" ... and {len(info['brackets'])-3} more brackets\")
"
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.
- 1mo ago First seen · 210 lines · 9 tokens per session scan A cb2316d9f709
alpha is a command published in the GitHub repository austron24/kalshi-trader-plugin (12 stars, last pushed 9mo ago), licensed MIT. It adds 9 tokens to every session and 1,879 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
gamma-markets
Access detailed market data, filter markets by various criteria, and build market discovery features.
data-activity
Access user trading activity, transaction history, and order events.
data-positions
Query user positions, portfolio holdings, and calculate P&L metrics.
gamma-events
Events are high-level prediction questions (e.g., "Who will win the 2024 Presidential Election?"). Series are collections of related events (e.g., "Elections 2024").
ws-market-channel
The market channel provides real-time updates for market data including orderbook changes, trades, and price updates.
clob-orders
Create, cancel, and manage orders on Polymarket's Central Limit Order Book.