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hyperliquid-market-data
Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the s
概要
Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the strategy lab. Read-only, no key. Use for any market brief, depth read, funding question or data pull.
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Hyperliquid market data
All reads are POST /info with a JSON body; no key, no signing. Market data is usually read from mainnet even when the desk trades on testnet, because testnet prices and books are thin; say which network a figure came from. Every figure the desk reports carries source (request type), network and UTC time.
BASE=https://api.hyperliquid.xyz # or https://api.hyperliquid-testnet.xyz
hl() { curl -sS -m 15 -X POST "$BASE/info" -H 'Content-Type: application/json' -d "$1"; }
Python header (SDK):
from hyperliquid.info import Info
from hyperliquid.utils import constants
info = Info(constants.MAINNET_API_URL, skip_ws=True) # TESTNET_API_URL for testnet
Prices
hl '{"type":"allMids"}' | jq '{BTC, ETH, SOL}' # mid per coin, strings
allMids falls back to last trade when the book is empty. For mark, oracle and mid together use metaAndAssetCtxs below. Python: info.all_mids().
Market metadata, funding, open interest, volume
hl '{"type":"metaAndAssetCtxs"}' | jq -r '
.[0].universe as $u | .[1] | to_entries[] | . as $e | $u[$e.key] as $m
| select($m.name == "BTC" or $m.name == "ETH" or $m.name == "SOL")
| [$m.name, $e.value.midPx, $e.value.markPx, $e.value.oraclePx, $e.value.funding, $e.value.openInterest, $e.value.dayNtlVlm, $e.value.premium, $m.maxLeverage, $m.szDecimals] | @tsv'
Fields per asset (same order as meta.universe): midPx, markPx, oraclePx, funding (hourly rate as a decimal: 0.0000125 = 0.00125%/h), openInterest (coin units), dayNtlVlm (24h USD volume), premium (impact bid/ask versus oracle, the input to funding), prevDayPx, impactPxs. Universe fields: name, szDecimals, maxLeverage, marginTableId, onlyIsolated/marginMode, isDelisted.
Python: meta, ctxs = info.meta_and_asset_ctxs().
Derived numbers the desk uses (show the formula): OI notional = openInterest x markPx; annualised funding = funding x 24 x 365; 24h change = markPx / prevDayPx - 1.
Margin tiers (max leverage by notional) come from meta:
hl '{"type":"meta"}' | jq --arg c BTC '(.universe[] | select(.name==$c)) as $u | ($u.marginTableId // $u.maxLeverage) as $id
| {name:$u.name, maxLeverage:$u.maxLeverage, marginTableId:$id,
tiers: (if $id < 50 then [{lowerBound:"0.0", maxLeverage:$id}]
else ((.marginTables[] | select(.[0]==$id) | .[1].marginTiers) // [{lowerBound:"0.0", maxLeverage:$u.maxLeverage}]) end)}'
Ids below 50 are single-tier tables whose max leverage equals the id, and they are not listed under marginTables, so the snippet synthesises that tier; ids of 50 and above are looked up.
Order book and depth
hl '{"type":"l2Book","coin":"ETH"}' | jq '{time, bids: .levels[0][:5], asks: .levels[1][:5]}'
Up to 20 levels per side; each level is {px, sz, n} (n = number of orders). Optional nSigFigs (2-5) aggregates price levels; mantissa (1, 2 or 5) only with nSigFigs: 5.
Twenty levels is a page, not the book. On a liquid perp those levels stop a few bps from the mid - around 8 bps on ETH and under 3 bps on BTC at normal spreads - so a 25 bps band summed from the default response is whatever the page happened to contain, not the depth within 25 bps. Read the reach before quoting a band: when the side came back with 20 levels and the furthest one is nearer than the band, the number is a floor. To reach further, re-request with nSigFigs: 4, which buckets prices coarsely enough to push 20 levels out to roughly 20-25 bps on a major perp (measured live: 82 bps ETH, 25 BTC, 24 HYPE, 20 SOL), and drop to nSigFigs: 3 when even that stops short. Read each band off the finest page that reaches it - the coarse page moves the band edges by up to one bucket, and its top of book is not the real one - and say which page a figure came from. scripts/opening_bell.py is that ladder in code.
Depth within a band, the way the Risk Manager and Execution Trader want it:
hl '{"type":"l2Book","coin":"ETH"}' | jq '
(.levels[0][0].px|tonumber) as $bb | (.levels[1][0].px|tonumber) as $ba | (($bb+$ba)/2) as $mid
| def within(side; bps): [side[] | select((((.px|tonumber) - $mid) | fabs) / $mid * 10000 <= bps) | .sz|tonumber] | add // 0;
def reach(side): (((side[-1].px|tonumber) - $mid) | fabs) / $mid * 10000;
{mid: $mid, spread_bps: (($ba-$bb)/$mid*10000),
levels: [(.levels[0]|length), (.levels[1]|length)], reach_bps: [reach(.levels[0]), reach(.levels[1])],
bid_5bps: within(.levels[0]; 5), ask_5bps: within(.levels[1]; 5),
bid_10bps: within(.levels[0]; 10), ask_10bps: within(.levels[1]; 10),
bid_25bps: within(.levels[0]; 25), ask_25bps: within(.levels[1]; 25)}'
reach_bps is the answer's own scope: any band wider than it, on a side that returned 20 levels, is a floor and is quoted as >= size. Two bands reporting the same total is that cut-off, not a flat book.
Expected slippage for a size: walk the relevant side accumulating sz until the target size is reached; report the volume-weighted price versus mid in bps. If the size exceeds the visible 20 levels, say "beyond visible depth". Python: info.l2_snapshot("ETH").
Recent trades: hl '{"type":"recentTrades","coin":"ETH"}' (public prints: px, sz, side, time).
Candles
END=$(date +%s000); START=$((END - 48*3600*1000))
hl "{\"type\":\"candleSnapshot\",\"req\":{\"coin\":\"ETH\",\"interval\":\"1h\",\"startTime\":$START,\"endTime\":$END}}" \
| jq -r '.[] | [.t, .o, .h, .l, .c, .v, .n] | @csv'
Fields: t open time ms, T close time ms, o h l c strings, v base volume, n trade count. Intervals: 1m 3m 5m 15m 30m 1h 2h 4h 8h 12h 1d 3d 1w 1M. Only the most recent 5000 candles per market and interval exist on the API, so history depth depends on the interval: about 3.5 days of 1m, 208 days of 1h, 2.3 years of 4h, 13 years of 1d. Choose the interval to fit the history you need; requests for older candles return nothing. Python: info.candles_snapshot(coin, interval, start_ms, end_ms).
Saving a dataset for the strategy lab (walks back until the API runs out):
import csv, time
from hyperliquid.info import Info
from hyperliquid.utils import constants
info = Info(constants.MAINNET_API_URL, skip_ws=True)
coin, interval, days = "ETH", "4h", 365 # 4h keeps a year inside the 5000-candle ceiling
end = int(time.time() * 1000); start = end - days * 86_400_000
rows = {}
cursor_end = end
while cursor_end > start:
batch = info.candles_snapshot(coin, interval, start, cursor_end)
if not batch: break
for c in batch: rows[c["t"]] = c
oldest = min(c["t"] for c in batch)
if oldest <= start or len(batch) < 2: break
cursor_end = oldest - 1
path = f"/workspace/trading-desk/data/{coin}-{interval}-{days}d.csv"
with open(path, "w", newline="") as f:
w = csv.writer(f); w.writerow(["t","T","o","h","l","c","v","n"])
for t in sorted(rows): c = rows[t]; w.writerow([c["t"],c["T"],c["o"],c["h"],c["l"],c["c"],c["v"],c["n"]])
print(path, len(rows), "candles", "fetched", time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()))
Record the exact request (coin, interval, start, end, fetched-at, network) next to the file.
Funding history and predictions
START=$(( $(date +%s000) - 7*86400000 ))
hl "{\"type\":\"fundingHistory\",\"coin\":\"ETH\",\"startTime\":$START}" | jq -r '.[] | [.time, .fundingRate, .premium] | @tsv'
# venue, raw rate, its interval in hours, rate per hour, next funding
hl '{"type":"predictedFundings"}' | jq -r '.[] | select(.[0]=="ETH") | .[1][] | select(.[1]) | [.[0], .[1].fundingRate, (.[1].fundingIntervalHours // "?"), (if .[1].fundingIntervalHours then (.[1].fundingRate|tonumber) / .[1].fundingIntervalHours else "?" end), .[1].nextFundingTime] | @tsv'
fundingHistory returns hourly rates (up to 500 per call; paginate by startTime).
predictedFundings compares venues, and the venue rates are over different periods, so never compare them raw. Every coin carries the same three slots in the same order - BinPerp, HlPerp, BybitPerp - but match on the venue name rather than the position, and each slot is either a payload or null when the coin is not listed on that venue. Divide fundingRate by the payload's own fundingIntervalHours to get a per-hour rate. HlPerp is always 1; the CEX venues are 4 or 8 depending on the coin, not a fixed 8: on mainnet on 2026-08-28, BinPerp was 4h for 126 coins and 8h for 63, BybitPerp 4h for 118 and 8h for 69. BTC, ETH and DOGE are all 8h, so an assumed 8 looks right on the majors and is wrong by 2x across most alts. A few BinPerp payloads (19 that day, including TON, MKR and IP) omit fundingIntervalHours entirely; treat the interval as unknown and say so rather than defaulting it.
Funding is paid every hour at size x oracle price x hourly rate; longs pay shorts when positive. Python: info.funding_history(coin, start_ms).
Spot markets
hl '{"type":"spotMeta"}' | jq '.universe[] | select(.name=="PURR/USDC" or .name=="@107")'
hl '{"type":"spotMetaAndAssetCtxs"}' | jq '.[1][:3]'
Spot pairs are named PURR/USDC or @<index> on the API (the app shows HYPE/USDC); tokens: [base, quote] indexes into spotMeta.tokens. A pair's asset id for orders is 10000 + universe index; its size decimals are the base token's szDecimals. Ids differ between mainnet and testnet.
HIP-3 builder perps
Other perp dexs exist beside the main one: hl '{"type":"perpDexs"}' lists them; coins are named dex:COIN, and meta, metaAndAssetCtxs, clearinghouseState accept "dex": "<name>". The desk uses the default dex unless the user says otherwise.
Brief format
Use the block in agents/market-analyst.md: sources and time on the first line, then facts, derived, read, unknown, next.
Rate limits
/info weight per IP is 1200 per minute: allMids, l2Book, clearinghouseState, orderStatus cost 2; most others cost 20; candleSnapshot adds 1 per 60 candles. Batch questions, do not poll faster than the desk needs, and prefer hyperliquid-websocket for anything continuous. HTTP 429 means back off.
Pitfalls
- Reporting a number without the request type, network and UTC time.
- Treating
fundingas an 8h or daily rate; it is hourly. - Comparing
predictedFundingsvenues without dividing each by its ownfundingIntervalHours, or assuming the CEX venues are 8h when most coins are 4h. - Reading a book once and calling it "the depth" ten minutes later.
- Forgetting spot
@indexnaming and base-tokenszDecimals. - Expecting deep history at fine intervals; only the latest 5000 candles per interval exist, so pick the interval to fit the lookback.
ファイルのメタデータ
name: hyperliquid-market-data description: Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the strategy lab. Read-only, no key. Use for any market brief, depth read, funding question or data pull. license: MIT metadata: version: "1.1.0" author: Galleon Labs category: hyperliquid network-default: mainnet-for-reads
元のテキストを表示
---
name: hyperliquid-market-data
description: Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the strategy lab. Read-only, no key. Use for any market brief, depth read, funding question or data pull.
license: MIT
metadata:
version: "1.1.0"
author: Galleon Labs
category: hyperliquid
network-default: mainnet-for-reads
---
# Hyperliquid market data
All reads are `POST /info` with a JSON body; no key, no signing. Market data is usually read from **mainnet** even when the desk trades on testnet, because testnet prices and books are thin; say which network a figure came from. Every figure the desk reports carries source (request type), network and UTC time.
```bash
BASE=https://api.hyperliquid.xyz # or https://api.hyperliquid-testnet.xyz
hl() { curl -sS -m 15 -X POST "$BASE/info" -H 'Content-Type: application/json' -d "$1"; }
```
Python header (SDK):
```python
from hyperliquid.info import Info
from hyperliquid.utils import constants
info = Info(constants.MAINNET_API_URL, skip_ws=True) # TESTNET_API_URL for testnet
```
## Prices
```bash
hl '{"type":"allMids"}' | jq '{BTC, ETH, SOL}' # mid per coin, strings
```
`allMids` falls back to last trade when the book is empty. For mark, oracle and mid together use `metaAndAssetCtxs` below. Python: `info.all_mids()`.
## Market metadata, funding, open interest, volume
```bash
hl '{"type":"metaAndAssetCtxs"}' | jq -r '
.[0].universe as $u | .[1] | to_entries[] | . as $e | $u[$e.key] as $m
| select($m.name == "BTC" or $m.name == "ETH" or $m.name == "SOL")
| [$m.name, $e.value.midPx, $e.value.markPx, $e.value.oraclePx, $e.value.funding, $e.value.openInterest, $e.value.dayNtlVlm, $e.value.premium, $m.maxLeverage, $m.szDecimals] | @tsv'
```
Fields per asset (same order as `meta.universe`): `midPx`, `markPx`, `oraclePx`, `funding` (**hourly** rate as a decimal: `0.0000125` = 0.00125%/h), `openInterest` (coin units), `dayNtlVlm` (24h USD volume), `premium` (impact bid/ask versus oracle, the input to funding), `prevDayPx`, `impactPxs`. Universe fields: `name`, `szDecimals`, `maxLeverage`, `marginTableId`, `onlyIsolated`/`marginMode`, `isDelisted`.
Python: `meta, ctxs = info.meta_and_asset_ctxs()`.
Derived numbers the desk uses (show the formula): OI notional = `openInterest x markPx`; annualised funding = `funding x 24 x 365`; 24h change = `markPx / prevDayPx - 1`.
Margin tiers (max leverage by notional) come from `meta`:
```bash
hl '{"type":"meta"}' | jq --arg c BTC '(.universe[] | select(.name==$c)) as $u | ($u.marginTableId // $u.maxLeverage) as $id
| {name:$u.name, maxLeverage:$u.maxLeverage, marginTableId:$id,
tiers: (if $id < 50 then [{lowerBound:"0.0", maxLeverage:$id}]
else ((.marginTables[] | select(.[0]==$id) | .[1].marginTiers) // [{lowerBound:"0.0", maxLeverage:$u.maxLeverage}]) end)}'
```
Ids below 50 are single-tier tables whose max leverage equals the id, and they are not listed under `marginTables`, so the snippet synthesises that tier; ids of 50 and above are looked up.
## Order book and depth
```bash
hl '{"type":"l2Book","coin":"ETH"}' | jq '{time, bids: .levels[0][:5], asks: .levels[1][:5]}'
```
Up to 20 levels per side; each level is `{px, sz, n}` (`n` = number of orders). Optional `nSigFigs` (2-5) aggregates price levels; `mantissa` (1, 2 or 5) only with `nSigFigs: 5`.
**Twenty levels is a page, not the book.** On a liquid perp those levels stop a few bps from the mid - around 8 bps on ETH and under 3 bps on BTC at normal spreads - so a 25 bps band summed from the default response is whatever the page happened to contain, not the depth within 25 bps. Read the reach before quoting a band: when the side came back with 20 levels and the furthest one is nearer than the band, the number is a floor. To reach further, re-request with `nSigFigs: 4`, which buckets prices coarsely enough to push 20 levels out to roughly 20-25 bps on a major perp (measured live: 82 bps ETH, 25 BTC, 24 HYPE, 20 SOL), and drop to `nSigFigs: 3` when even that stops short. Read each band off the finest page that reaches it - the coarse page moves the band edges by up to one bucket, and its top of book is not the real one - and say which page a figure came from. `scripts/opening_bell.py` is that ladder in code.
Depth within a band, the way the Risk Manager and Execution Trader want it:
```bash
hl '{"type":"l2Book","coin":"ETH"}' | jq '
(.levels[0][0].px|tonumber) as $bb | (.levels[1][0].px|tonumber) as $ba | (($bb+$ba)/2) as $mid
| def within(side; bps): [side[] | select((((.px|tonumber) - $mid) | fabs) / $mid * 10000 <= bps) | .sz|tonumber] | add // 0;
def reach(side): (((side[-1].px|tonumber) - $mid) | fabs) / $mid * 10000;
{mid: $mid, spread_bps: (($ba-$bb)/$mid*10000),
levels: [(.levels[0]|length), (.levels[1]|length)], reach_bps: [reach(.levels[0]), reach(.levels[1])],
bid_5bps: within(.levels[0]; 5), ask_5bps: within(.levels[1]; 5),
bid_10bps: within(.levels[0]; 10), ask_10bps: within(.levels[1]; 10),
bid_25bps: within(.levels[0]; 25), ask_25bps: within(.levels[1]; 25)}'
```
`reach_bps` is the answer's own scope: any band wider than it, on a side that returned 20 levels, is a floor and is quoted as `>= size`. Two bands reporting the same total is that cut-off, not a flat book.
Expected slippage for a size: walk the relevant side accumulating `sz` until the target size is reached; report the volume-weighted price versus mid in bps. If the size exceeds the visible 20 levels, say "beyond visible depth". Python: `info.l2_snapshot("ETH")`.
Recent trades: `hl '{"type":"recentTrades","coin":"ETH"}'` (public prints: `px, sz, side, time`).
## Candles
```bash
END=$(date +%s000); START=$((END - 48*3600*1000))
hl "{\"type\":\"candleSnapshot\",\"req\":{\"coin\":\"ETH\",\"interval\":\"1h\",\"startTime\":$START,\"endTime\":$END}}" \
| jq -r '.[] | [.t, .o, .h, .l, .c, .v, .n] | @csv'
```
Fields: `t` open time ms, `T` close time ms, `o h l c` strings, `v` base volume, `n` trade count. Intervals: `1m 3m 5m 15m 30m 1h 2h 4h 8h 12h 1d 3d 1w 1M`. **Only the most recent 5000 candles per market and interval exist on the API**, so history depth depends on the interval: about 3.5 days of `1m`, 208 days of `1h`, 2.3 years of `4h`, 13 years of `1d`. Choose the interval to fit the history you need; requests for older candles return nothing. Python: `info.candles_snapshot(coin, interval, start_ms, end_ms)`.
Saving a dataset for the strategy lab (walks back until the API runs out):
```python
import csv, time
from hyperliquid.info import Info
from hyperliquid.utils import constants
info = Info(constants.MAINNET_API_URL, skip_ws=True)
coin, interval, days = "ETH", "4h", 365 # 4h keeps a year inside the 5000-candle ceiling
end = int(time.time() * 1000); start = end - days * 86_400_000
rows = {}
cursor_end = end
while cursor_end > start:
batch = info.candles_snapshot(coin, interval, start, cursor_end)
if not batch: break
for c in batch: rows[c["t"]] = c
oldest = min(c["t"] for c in batch)
if oldest <= start or len(batch) < 2: break
cursor_end = oldest - 1
path = f"/workspace/trading-desk/data/{coin}-{interval}-{days}d.csv"
with open(path, "w", newline="") as f:
w = csv.writer(f); w.writerow(["t","T","o","h","l","c","v","n"])
for t in sorted(rows): c = rows[t]; w.writerow([c["t"],c["T"],c["o"],c["h"],c["l"],c["c"],c["v"],c["n"]])
print(path, len(rows), "candles", "fetched", time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()))
```
Record the exact request (coin, interval, start, end, fetched-at, network) next to the file.
## Funding history and predictions
```bash
START=$(( $(date +%s000) - 7*86400000 ))
hl "{\"type\":\"fundingHistory\",\"coin\":\"ETH\",\"startTime\":$START}" | jq -r '.[] | [.time, .fundingRate, .premium] | @tsv'
# venue, raw rate, its interval in hours, rate per hour, next funding
hl '{"type":"predictedFundings"}' | jq -r '.[] | select(.[0]=="ETH") | .[1][] | select(.[1]) | [.[0], .[1].fundingRate, (.[1].fundingIntervalHours // "?"), (if .[1].fundingIntervalHours then (.[1].fundingRate|tonumber) / .[1].fundingIntervalHours else "?" end), .[1].nextFundingTime] | @tsv'
```
`fundingHistory` returns hourly rates (up to 500 per call; paginate by `startTime`).
`predictedFundings` compares venues, and the venue rates are over **different periods**, so never compare them raw. Every coin carries the same three slots in the same order - `BinPerp`, `HlPerp`, `BybitPerp` - but match on the venue name rather than the position, and each slot is either a payload or `null` when the coin is not listed on that venue. Divide `fundingRate` by the payload's own `fundingIntervalHours` to get a per-hour rate. `HlPerp` is always 1; the CEX venues are **4 or 8 depending on the coin**, not a fixed 8: on mainnet on 2026-08-28, BinPerp was 4h for 126 coins and 8h for 63, BybitPerp 4h for 118 and 8h for 69. BTC, ETH and DOGE are all 8h, so an assumed 8 looks right on the majors and is wrong by 2x across most alts. A few BinPerp payloads (19 that day, including `TON`, `MKR` and `IP`) omit `fundingIntervalHours` entirely; treat the interval as unknown and say so rather than defaulting it.
Funding is paid every hour at `size x oracle price x hourly rate`; longs pay shorts when positive. Python: `info.funding_history(coin, start_ms)`.
## Spot markets
```bash
hl '{"type":"spotMeta"}' | jq '.universe[] | select(.name=="PURR/USDC" or .name=="@107")'
hl '{"type":"spotMetaAndAssetCtxs"}' | jq '.[1][:3]'
```
Spot pairs are named `PURR/USDC` or `@<index>` on the API (the app shows `HYPE/USDC`); `tokens: [base, quote]` indexes into `spotMeta.tokens`. A pair's asset id for orders is `10000 + universe index`; its size decimals are the **base token's** `szDecimals`. Ids differ between mainnet and testnet.
## HIP-3 builder perps
Other perp dexs exist beside the main one: `hl '{"type":"perpDexs"}'` lists them; coins are named `dex:COIN`, and `meta`, `metaAndAssetCtxs`, `clearinghouseState` accept `"dex": "<name>"`. The desk uses the default dex unless the user says otherwise.
## Brief format
Use the block in `agents/market-analyst.md`: sources and time on the first line, then facts, derived, read, unknown, next.
## Rate limits
`/info` weight per IP is 1200 per minute: `allMids`, `l2Book`, `clearinghouseState`, `orderStatus` cost 2; most others cost 20; `candleSnapshot` adds 1 per 60 candles. Batch questions, do not poll faster than the desk needs, and prefer `hyperliquid-websocket` for anything continuous. HTTP 429 means back off.
## Pitfalls
- Reporting a number without the request type, network and UTC time.
- Treating `funding` as an 8h or daily rate; it is hourly.
- Comparing `predictedFundings` venues without dividing each by its own `fundingIntervalHours`, or assuming the CEX venues are 8h when most coins are 4h.
- Reading a book once and calling it "the depth" ten minutes later.
- Forgetting spot `@index` naming and base-token `szDecimals`.
- Expecting deep history at fine intervals; only the latest 5000 candles per interval exist, so pick the interval to fit the lookback.
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ライセンス: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- The skill references `scripts/opening_bell.py` and `agents/market-analyst.md`, but these files are not included in the skill directory. This may cause confusion for agents trying to locate them.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 59 GitHub stars
- Stars/forks activity: 59 stars, 9 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
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- 2Agent に計画を求め、設定と費用を承認してから隔離環境でテストします。
- 3出力と変更ファイルを確認し、実行した結果だけを報告します。再現用にソースの版を保存します。
依存関係、API キー、外部サービスの料金をソースで確認してください。公開リポジトリでも全サービスが無料とは限りません。
出典と利用上の注意
登録済み
メタデータと審査情報は参考です。人気、ソースの発見、実行成功は別の事実です。
- ソースリポジトリ
- galleonlabs/hypergrok-trading-desk
- ライセンス
- MIT
- バージョン
- 1.1.0
- 最終 GitHub プッシュ
- 2026年9月7日
- 登録情報の更新日
- 2026年9月9日
登録されたバージョンです。ソースのリリース情報を確認してください。
品質
62/100
有望
信頼
56/100
Do not auto-install
監査
71/100
要レビュー
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- The skill references `scripts/opening_bell.py` and `agents/market-analyst.md`, but these files are not included in the skill directory. This may cause confusion for agents trying to locate them.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 59 GitHub stars
- Stars/forks activity: 59 stars, 9 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- 成果
- —
コピーはインストールではありません。件数は成功報告に基づき、品質全体を保証しません。
Agent 接続
Registry API 経由で判断、信頼、監査、ユースケース、インストールのシグナルを提供し、UI をスクレイピングせずに Agent が順位付けできます。
詳細情報
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}クリエイター向け
掲載元
Registry により登録
この掲載は公開ソースから登録されており、メンテナー申請が承認されるまで公式として表示されません。
- 作成者
- Galleon Labs
- インデックス作成者
- OpenAgentSkill コミュニティインデックス
帰属は公開リポジトリまたは作成者プロフィールにリンクされています。作成者は掲載を申請して所有権シグナルを更新できます。
このスキルを申請所有者の申請
このスキル掲載を申請
この Registry により登録 掲載は Galleon Labs に帰属していますが、まだ公式として表示されていません。申請すると、確認済み所有者シグナルが追加され、今後の公開、インストール、監査更新の信頼性が高まります。
共有キット
クリエイター被リンクキット
README にエビデンスバッジを追加
開発者がリポジトリを評価する場所で、正規掲載、現在の信頼・監査シグナル、実際の Agent-Proven エビデンスを表示します。
[](https://www.openagentskill.com/skills/galleonlabs-hyperliquid-market-data?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/galleonlabs-hyperliquid-market-data?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/galleonlabs-hyperliquid-market-data/audit)
[](https://www.openagentskill.com/skills/galleonlabs-hyperliquid-market-data?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)コミュニティシグナル
このスキルが Agent ワークフローに役立つかを共有してください。集約されたフィードバックがランキングを改善します。