htx-liquidation-stream

Included with Lifetime

$97 forever

Query HTX USDT-M perpetual liquidation orders — recent forced-liquidation events for long/short squeeze monitoring and cluster detection. Public, no API key required.

Backend & APIs

What this skill does


# HTX Liquidation Stream

Focused skill for **liquidation orders** on HTX USDT-M perpetuals. Use to detect short-squeezes, long-squeezes, and price levels where stop-cascades have been triggered.

## When to use this skill

Load this skill when the user asks about:

- "Recent BTC liquidations"
- "How much was liquidated in the last 24h?"
- "Were there any large liquidations near $X price?"
- "Long squeeze on ETH?"
- "Where did the recent liquidation cluster happen?"
- "Liquidation volume by side (long vs short)"

For aggregated *cross-exchange* liquidation heatmaps, HTX does not expose this — you would need to integrate CoinGlass externally.

## Underlying tool

Drives `htx-cli`. Binary on `$PATH` or `$HTX_CLI_BIN`. Always pass `--json`.

## Endpoint catalog (1)

| # | Method | Endpoint | CLI invocation | Description |
|---|--------|----------|----------------|-------------|
| 1 | GET | `/linear-swap-api/v1/swap_liquidation_orders` | `htx-cli futures market liquidation-orders <contract-code> --json` | Recent forced-liquidation orders for one contract |

## Query parameters

The convenience command auto-fills sensible defaults. For custom filtering use the underlying call form:

```bash
htx-cli futures call GET /linear-swap-api/v1/swap_liquidation_orders \
  --query contract_code=BTC-USDT&trade_type=0&create_date=7&page_size=50 \
  --json
```

| Param | Values | Meaning |
|-------|--------|---------|
| `trade_type` | `0` (all), `1` (closed long forced), `2` (closed short forced), `3` (long order forced), `4` (short order forced) | Filter by liquidation direction |
| `create_date` | `7`, `14`, `30`, `60`, `90` | Days lookback |
| `page_size` | 1–50 | Records per page |
| `page_index` | int (default 1) | Pagination |

## Contract code format

USDT-M perpetual codes follow `<BASE>-USDT` (e.g. `BTC-USDT`).

## Typical queries → CLI

| User question | CLI command |
|---------------|-------------|
| "BTC liquidations last 7d" | `htx-cli futures market liquidation-orders BTC-USDT --json` |
| "ETH long liquidations last 30d" | `htx-cli futures call GET /linear-swap-api/v1/swap_liquidation_orders --query contract_code=ETH-USDT&trade_type=1&create_date=30 --json` |
| "Largest liquidations on SOL last 24h" | Pull `--query contract_code=SOL-USDT&page_size=50 --json` then sort client-side by `volume * price`, filter to last 24h |

## Output guidance

When summarizing liquidations, return:
- **Total liq value (USD)** in the requested window
- **Long liq vs short liq breakdown** (% / absolute)
- **Top 5 single events** by USD value (price + side + time)
- **Cluster zones**: price ranges where ≥ 3 liq events happened within ±0.5%
- **Time-of-day pattern** if relevant (e.g. concentrated during US open)

## Squeeze interpretation

| Pattern | Signal |
|---------|--------|
| Heavy long liqs + price ↓ | Capitulation cascade — potential reversal zone |
| Heavy short liqs + price ↑ | Short squeeze — potential exhaustion as squeeze fuel runs out |
| Liq cluster at round number | Stop-loss bunch — price often retests |

## Related skills

- `https://github.com/htx-exchange/htx-skills-hub/funding-rate` — pre-squeeze crowdedness
- `https://github.com/htx-exchange/htx-skills-hub/oi-tracker` — post-squeeze OI drop confirms cascade
- `https://github.com/htx-exchange/htx-skills-hub/derivatives-analyst` — *(planned Layer 2)* combines liq + funding + OI for unified pressure score

Files: 3

Size: 5.1 KB

Complexity: 16/100

Category: Backend & APIs

Source: https://github.com/htx-exchange/htx-skills-hub/tree/main/skills/htx/liquidation-stream

Related in Backend & APIs

jfrog

Included

Interact with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.

Backend & APIsscripts

cupynumeric-migration-readiness

Included

Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.

Backend & APIsscripts

alibabacloud-data-agent-skill

Included

Invoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".

Backend & APIsscripts

token-optimizer

Included

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.

Backend & APIsscripts

resend-cli

Included

Use this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.

Backend & APIsscripts

alibabacloud-odps-maxframe-coding

Included

Use this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.

Backend & APIsscripts