Loading...
Loading...
Found 1,145 Skills
Ichimoku Cloud (一目均衡表) five-line system signal engine for stocks listed in HK / US / A-share / Singapore via Longbridge Securities. Computes Tenkan-sen, Kijun-sen, Senkou Span A/B, and Chikou Span from OHLCV data; generates price-vs-cloud position, line-cross signals, and full trend-confirmation scores. Triggers: "一目均衡表", "一目云", "云图", "转折线", "基准线", "先行带", "迟行线", "云上", "云下", "一目均衡表", "一目雲", "雲圖", "轉折線", "基準線", "先行帶", "遲行線", "ichimoku", "ichimoku cloud", "tenkan sen", "kijun sen", "senkou span", "chikou span", "cloud breakout".
This skill should be used when debugging full-stack issues that span UI, backend, and database layers. It provides a systematic workflow to detect errors, analyze root causes, apply fixes iteratively, and verify solutions through automated server restarts and browser-based testing. Ideal for scenarios like failing schedulers, import errors, database issues, or API payload problems where issues originate in backend code but manifest in the UI.
Use when you want to retrieve semi-quantitative protein expression and spatial localisation data from the Human Protein Atlas (HPA).
Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.
Translation skill for validated Orbitant blog posts. Takes a Spanish article that has already been reviewed and approved by a human editor, and produces an English version optimised for English-speaking audiences. Keyword selection is not a literal translation but a search-intent-driven choice for the English market. Use this skill only on articles that have completed the full editorial and review process.
Guide for migrating an existing web app, PWA, or SPA into a store-ready Capacitor iOS and Android app. Use this skill when users want to wrap or convert a web app into a mobile app, avoid thin WebView app store rejection, add native-feeling UX, handle permissions, offline behavior, account deletion, billing, testing, and Capgo live updates.
Master skill for SynkOS multi-agent orchestration. Use whenever you need to spawn panes, delegate work to agents, manage parallel execution, coordinate multi-model squads, or use todo_manager.
Write spatial SQL against the connected warehouse — dialect-specific guidance, performance defaults, and CARTO's query/job execution model.
Activate when creating new modules, refactoring class hierarchies, introducing design patterns, or making changes spanning 3+ files in the APM CLI codebase.
Pull Bigdata.com (RavenPack) financial and news data through the official `bigdata-client` SDK and its public `/v1/*` REST endpoints when the Bigdata MCP server returns only pre-synthesized tearsheets but you need the machine-readable substrate underneath. MCP search returns prose chunks (text + relevance only — no per-chunk sentiment, no entity spans); its tearsheets give only aggregate values, not computable time series or per-field JSON. This skill bundles a verified, cost-guarded toolkit over the official REST API: annotated chunk search, entity/ISIN resolution, analyst estimates, calendar/surprise/ ratings/targets, financial statements, TTM metrics & ratios, prices, dividends, revenue segments, a daily entity-sentiment series, co-mention graph, screener, and batch search. Use it whenever the user mentions Bigdata.com, RavenPack, a `bd_v2_` key, the bigdata MCP, rp_entity_id, chunk/query_unit cost, or wants structured financials, fundamentals, prices, sentiment, or annotated news.
BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view (BEV) space, used in autonomous driving for robust 3D perception. Use when training, evaluating, or running inference for a TAO BEVFusion model. Trigger phrases include "train BEVFusion", "LiDAR + camera fusion", "BEV 3D detection", "multi-sensor 3D perception".
Static source-code vulnerability scan. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes, use vuln-pipeline instead. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", or as the step between /threat-model and /triage.