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概要と使いどころ
Track crypto options flow to identify institutional positioning and market sentiment. Use when tracking institutional options flow. Trigger with phrases like "track options flow", "analyze derivatives", or "check institutional".
日本語の概要は準備中です。原文の説明を表示しています。
Process perform on-chain analysis including whale tracking, token flows, and network activity. Use when performing crypto analysis. Trigger with phrases like "analyze crypto", "check blockchain", or "monitor market".
日本語の概要は準備中です。原文の説明を表示しています。
Run a fail-closed production readiness review for an Algolia-backed search release. Use when promoting new indices, settings, credentials, UI behavior, or event tracking. Trigger with "Algolia production checklist", "search go-live", or "Algolia launch review".
日本語の概要は準備中です。原文の説明を表示しています。
Turns one-shot external recon into a continuous monitoring program. Covers the scheduled re-scan-and-diff loop (baseline snapshot -> interval sleep -> re-scan -> asset/finding delta -> threshold-gated webhook alert), the scan-to-scan diff engine (new/removed/changed assets by a tracked-attribute table, new/resolved findings by a stable cross-scan fingerprint), adversary CTI / chatter monitoring across six public feeds (ransomwatch, ransomware.live, HackerNews Algolia search, Reddit security-subreddit RSS, GitHub Gist code-search, public Telegram channel scraping) with a source-kind-aware severity engine (leak-site/forum/telegram/paste tiers, CRITICAL through INFO), literal/glob/regex watchlist pattern matching, full-corpus capture with retroactive rescan on new watchlist entries, infrastructure-tracking-over-time discipline (certificate-transparency, passive-DNS, port/service, and typosquat re-enumeration cadence, and what a genuine 'perimeter drift' event looks like in the diff output), a five-state finding-lifecycle state machine (open/triaged/risk_accepted/resolved/false_positive) with per-severity SLA and fingerprint-based cross-scan dedup and auto-resolve/reopen rules, the alert-fatigue trap where a lifecycle-unaware rule re-fires on an already-accepted finding, a durable retry/backoff alert-outbox pattern ('queued is not delivered'), and copy-paste bash-cron plus PowerShell-Scheduled-Task recipes for a re-scan+diff loop with the Slack-compatible webhook payload shape. Passive OSINT and analysis only -- no new active-intrusion technique. Use when setting up ongoing monitoring for a retainer or MSSP engagement, tuning alert thresholds to avoid fatigue, triaging a finding's lifecycle status, investigating adversary chatter about a brand, building a 'what changed on the perimeter since last week' report, or deciding whether a persisting finding should re-alert.
日本語の概要は準備中です。原文の説明を表示しています。
Creates event-driven email listeners that monitor for specific conditions (like urgent emails from boss, newsletters to archive, package tracking) and execute custom actions. Use when user wants to be notified about emails, automatically handle certain emails, or set up email automation workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize resumes for Applicant Tracking Systems, check ATS compatibility, and analyze keyword match
日本語の概要は準備中です。原文の説明を表示しています。
Use Parallel CLI for web search, URL extraction, deep research, structured data enrichment, entity discovery, and recurring web monitoring. Best for requests that explicitly need current web evidence, academic-source discovery, repeated entity lookups, exhaustive reports, or ongoing change tracking.
日本語の概要は準備中です。原文の説明を表示しています。
Create a source-linked briefing for the latest published GitHub release of a public repository. Use for engineering teams tracking a dependency release; do not use it to publish releases or change repositories.
日本語の概要は準備中です。原文の説明を表示しています。
Audit one blog post file for AI-citation readiness (self-contained, sourced passages, entity clarity, structure, crawler access) and score it with an internal 0-100 heuristic plus per-surface notes for AI Overviews, AI Mode, ChatGPT, Perplexity, Claude, Gemini, and Copilot. Use when the user says "geo", "aeo", "ai citation", "citation audit", "chatgpt citation". Not for a site-wide audit, live AI-visibility tracking, or schema generation (use claude-seo seo-geo, blog-audit, blog-schema), and not a promise of citations.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to set up, debug, or interpret app install attribution — including SKAdNetwork (SKAN), Apple's AdAttributionKit, Google Play Install Referrer, MMPs (AppsFlyer, Adjust, Singular, Branch, Kochava), deep links, deferred deep links, conversion values, postback windows, or privacy thresholds. Use when the user mentions "SKAdNetwork", "SKAN", "SKAN 4", "AdAttributionKit", "AAK", "MMP", "AppsFlyer", "Adjust", "Singular", "Branch", "attribution", "conversion value", "postback", "Install Referrer", "deferred deep link", "iOS 14.5", "ATT", "App Tracking Transparency", "IDFA", or "I can't measure my ad campaigns". For paid campaign strategy, see ua-campaign and apple-search-ads. For analytics events, see app-analytics.
日本語の概要は準備中です。原文の説明を表示しています。
Run inference on Fireworks AI — direct, through the Leanmcp AI Gateway proxy (aigateway.leanmcp.com) for observability, or via LiteLLM / the OpenAI SDK / raw HTTP. Covers the endpoint-and-key matrix for each route, serverless vs dedicated deployments and their cold starts, browsing and filtering the model catalogue, reasoning models that return content:null with the answer in reasoning_content, function calling, concurrency and load testing, and runner scripts where many models share one protocol with a --use-proxy toggle. Use this skill whenever the user mentions Fireworks, FIREWORKS_API_KEY, api.fireworks.ai, fireworks_ai/ model ids, accounts/fireworks/models/..., a Fireworks deployment id, the Leanmcp gateway or LEANMCP_API_KEY, app.leanmcp.com observability, or gpt-oss / kimi / glm / qwen / deepseek served on Fireworks; wants to route LLM calls through a proxy for logging or cost tracking; is debugging empty responses, 404s, scope errors, cold starts, or "the gateway isn't working"; wants to pick a Fireworks model with tool calling; or wants to sweep several models over one benchmark. Reach for it even on vague asks like "why is this model returning nothing" or "set up inference for these models" when Fireworks is in play.
日本語の概要は準備中です。原文の説明を表示しています。
Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".
日本語の概要は準備中です。原文の説明を表示しています。
Runs the loop from discovering a weakness to confirming it is fixed — scanning, triage, prioritization by real exploitability, remediation tracking, and patch policy. Use this to stand up or fix a vulnerability program, triage scanner output, decide what to fix first, set patch SLAs, handle a disclosure from an outside researcher, or report posture to leadership.
日本語の概要は準備中です。原文の説明を表示しています。
Plans and delivers a single project — scope, estimation, scheduling, critical path, tracking, and recovering when it slips. Use this to plan a project, build or challenge a schedule, estimate credibly, track progress meaningfully, or recover a project that is late.
日本語の概要は準備中です。原文の説明を表示しています。
Ensures projects deliver the value they were approved on — defining measurable benefits, baselining, tracking after delivery, and honest post-implementation review. Use this to define benefits for a business case, set a baseline, track whether value actually landed, or run a post-implementation review that produces something useful.
日本語の概要は準備中です。原文の説明を表示しています。
Taobaoke all-in-one toolkit for Taobao, JD, and Pinduoduo affiliate marketing. Convert product links to affiliate links, cross-platform price comparison, one-click price protection, and commission tracking. Use for e-commerce affiliate operations.
日本語の概要は準備中です。原文の説明を表示しています。
DiDi ride-hailing service skill. Supports taxi booking, price estimation, route planning (driving/transit/walking/cycling), order management, driver location tracking, and scheduled rides. Use when user expresses any transportation need including ride-hailing, route queries, or commuting.
日本語の概要は準備中です。原文の説明を表示しています。
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
日本語の概要は準備中です。原文の説明を表示しています。
This skill is applicable when using LaminDB. LaminDB is an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR-compliant. It is suitable for managing biological datasets (scRNA-seq, spatial transcriptomics, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakes, or ensuring data lineage and reproducibility in biological research. It covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integration with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation
日本語の概要は準備中です。原文の説明を表示しています。
Automates pre-development workflow for large-scale complex tasks. Use when the user mentions "rewrite", "migrate", "overhaul", "refactor entire project", "transform", "rebuild in [language]", "spec-driven", or describes any large-scale project transformation that requires planning before coding. Also triggers on Chinese keywords: "改造", "重写", "迁移", "重构", "大规模", "规范驱动". Performs full project analysis, task decomposition, documentation generation, project-level instruction and native memory surface resolution, progress tracking setup, and then executes the plan within the same session.
日本語の概要は準備中です。原文の説明を表示しています。
Adding integral action to MPC for offset-free tension tracking.
日本語の概要は準備中です。原文の説明を表示しています。
Combine visual features (face detection, lip movement analysis) with audio features to improve speaker diarization accuracy in video files. Use OpenCV for face detection and lip movement tracking, then fuse visual cues with audio-based speaker embeddings. Essential when processing video files with multiple visible speakers or when audio-only diarization needs visual validation.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze and count objects in videos using Google Gemini API (object counting, pedestrian detection, vehicle tracking, and surveillance video analysis).
日本語の概要は準備中です。原文の説明を表示しています。