htx-elite-positioning
Top-trader long/short ratio on HTX USDT-M perpetuals — both account-based and position-based ratios, the core sentiment signal that distinguishes "smart money" from retail. Public, no API key.
What this skill does
# HTX Elite Positioning Focused skill for **elite (top-trader) long/short ratio** on HTX USDT-M perpetuals. Distinguishes signals from sophisticated traders vs. retail crowd. Public — agent may call freely. ## When to use this skill Load this skill when the user asks about: - "Are top traders long or short on BTC?" - "Smart money positioning on ETH" - "Top-trader long/short ratio for SOL" - "Is the elite cohort crowded long?" - "Compare account vs position ratio for BTC perpetual" - "Which side are the whales on?" For **retail / market-wide** ratio, HTX does not currently expose a non-elite long/short endpoint — that's a data gap. Document this in your reply if the user asks for it. ## Underlying tool Drives `htx-cli`. Binary on `$PATH` or `$HTX_CLI_BIN`. Always pass `--json`. ## Endpoint catalog (2) | # | Method | Endpoint | CLI invocation | Description | |---|--------|----------|----------------|-------------| | 1 | GET | `/linear-swap-api/v1/swap_elite_account_ratio` | `htx-cli futures call GET /linear-swap-api/v1/swap_elite_account_ratio --query contract_code=<code>&period=<period> --json` | Top-trader **account-count** long/short ratio (1 trader = 1 vote) | | 2 | GET | `/linear-swap-api/v1/swap_elite_position_ratio` | `htx-cli futures call GET /linear-swap-api/v1/swap_elite_position_ratio --query contract_code=<code>&period=<period> --json` | Top-trader **position-size** long/short ratio (size-weighted) | ## Why two ratios? The two ratios answer different questions: | Ratio | Question | Strength | |-------|----------|----------| | **Account ratio** | How many top traders are net long vs short? | Reflects breadth of conviction | | **Position ratio** | How much capital is net long vs short? | Reflects size-weighted exposure | **Divergence is informative**: e.g. account ratio 1.2 (slight long majority) but position ratio 0.6 (heavy short capital) → a few top traders are very heavily short. ## Period values Both endpoints accept `period`: - `5min`, `15min`, `30min`, `60min`, `4hour`, `12hour`, `1day` Returns a time series, typically last 48 data points. ## Contract code format USDT-M perpetual codes follow `<BASE>-USDT` (e.g. `BTC-USDT`). ## Typical queries → CLI | User question | CLI command | |---------------|-------------| | "BTC top-trader long/short by account" | `htx-cli futures call GET /linear-swap-api/v1/swap_elite_account_ratio --query contract_code=BTC-USDT&period=1hour --json` | | "ETH top-trader L/S by position size" | `htx-cli futures call GET /linear-swap-api/v1/swap_elite_position_ratio --query contract_code=ETH-USDT&period=4hour --json` | | "Is smart money crowded long on SOL?" | Both endpoints + interpret divergence | ## Output guidance Return both ratios when relevant. Compute and label: | Ratio range | Label | |-------------|-------| | `> 2.0` | Heavily long (extreme) | | `1.3 – 2.0` | Long-leaning | | `0.77 – 1.3` | Balanced | | `0.5 – 0.77` | Short-leaning | | `< 0.5` | Heavily short (extreme) | (Ratios are symmetric on log scale — `2.0` and `0.5` are equally extreme.) Always show: - Current value of both ratios - Direction of change vs. prior period (rising / falling) - **Divergence flag** if account ratio and position ratio disagree by > 30% ## Important caveat - HTX defines "elite" as the top tier of traders by performance/volume on the platform, not "VIP" tier - The exact constituency is determined by HTX (not user-configurable) - Elite ratio is **HTX-only** — do not compare 1-to-1 with other exchanges' "top trader" ratios ## Related skills - `https://github.com/htx-exchange/htx-skills-hub/funding-rate` — combine with funding for crowdedness picture - `https://github.com/htx-exchange/htx-skills-hub/oi-tracker` — combine with OI for new positioning vs. unwinding - `https://github.com/htx-exchange/htx-skills-hub/sentiment-analyst` — *(planned Layer 2)* uses elite ratio as a sentiment input
Related in Backend & APIs
jfrog
IncludedInteract 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.
cupynumeric-migration-readiness
IncludedPre-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.
alibabacloud-data-agent-skill
IncludedInvoke 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".
token-optimizer
IncludedReduce 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.
resend-cli
IncludedUse 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.
alibabacloud-odps-maxframe-coding
IncludedUse 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.