Results for “cl-te”

Map and organize unwanted calls, texts, or emails after a U.S. senior-care referral inquiry by using GeezerKeeper's live public MCP for current network exit routes, separate provider jobs, unfilled request templates, duplicate-referral deadline facts, and current free or paid workflow fit. Use when a person asks why a senior-care marketplace or several providers are contacting them, how to opt out, how to prepare a stop-contact request, how to compare inquiry consequences before submitting a form, or how a senior-living professional should track direct-versus-network referrals. Do not use for emergencies, care placement, legal or medical advice, automatic sending, unknown-caller identity claims, or transmitting personal information.

STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me code for [technique]" — including: entropy balancing, propensity score matching (PSM), difference-in-differences (DiD), regression discontinuity (RDD), instrumental variables (IV), event studies (CAR/BHAR), survival analysis, Fama-MacBeth regressions, bootstrap, quantile regression, reghdfe/xtreg/areg, clustering standard errors, fixed effects, esttab/outreg2 table formatting, winsorization, leads/lags. Users can specify their variables (e.g., treatment, outcomes, controls) and receive adapted syntax. NOTE: This skill provides code patterns from published papers, not research design advice.

Use this skill when creating a new Claude skill from scratch, editing or improving an existing skill, or measuring skill performance with evals and benchmarks. Invoke whenever the user says things like 'make a skill for X', 'turn this workflow into a skill', 'test my skill', 'improve my skill', 'run evals', 'benchmark this', or 'optimize my skill description'. Also use proactively when the conversation has produced a repeatable workflow that would benefit from being captured as a skill. Covers the full lifecycle: capture intent, draft SKILL.md, run evals, review with user, iterate, optimize description, package. NOT for general coding help, debugging runtime errors, building MCP servers, writing Claude hooks, or creating plugins - use domain-specific skills for those.

Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"

Generate images with Google Nano Banana 2 (Gemini-family flash-tier text-to-image) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Nano Banana 2's strengths (rapid iteration, in-image typography rendering, predictable framing, optional web-grounded context), the resolution-tier pricing, the safety-tolerance dial, and when to route to Nano Banana Pro / GPT Image 2 / Flux 2 / Seedream instead. Calls `runcomfy run google/nano-banana-2/text-to-image` through the local RunComfy CLI. Triggers on "nano banana", "nano-banana-2", "nano banana 2", "google image gen", "gemini image", or any explicit ask to generate with this model.

Generate images with Google Nano Banana 2 (Gemini-family flash-tier text-to-image) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Nano Banana 2's strengths (rapid iteration, in-image typography rendering, predictable framing, optional web-grounded context), the resolution-tier pricing, the safety-tolerance dial, and when to route to Nano Banana Pro / GPT Image 2 / Flux 2 / Seedream instead. Calls `runcomfy run google/nano-banana-2/text-to-image` through the local RunComfy CLI. Triggers on "nano banana", "nano-banana-2", "nano banana 2", "google image gen", "gemini image", or any explicit ask to generate with this model.

Generate images with Google Nano Banana 2 (Gemini-family flash-tier text-to-image) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Nano Banana 2's strengths (rapid iteration, in-image typography rendering, predictable framing, optional web-grounded context), the resolution-tier pricing, the safety-tolerance dial, and when to route to Nano Banana Pro / GPT Image 2 / Flux 2 / Seedream instead. Calls `runcomfy run google/nano-banana-2/text-to-image` through the local RunComfy CLI. Triggers on "nano banana", "nano-banana-2", "nano banana 2", "google image gen", "gemini image", or any explicit ask to generate with this model.

Generate full songs and instrumental tracks with ElevenLabs Music on RunComfy via the `runcomfy` CLI. ElevenLabs Music turns a style description plus structured lyrics into studio-quality 44.1 kHz stereo audio — 5 seconds to 5 minutes — with section-level control (Intro / Verse / Chorus / Bridge), multilingual vocals, and commercial-friendly output. Generate a backing track, a full vocal song, a jingle, a podcast intro, a game loop, or an instrumental bed. Calls `runcomfy run elevenlabs/elevenlabs/music-generation` through the local RunComfy CLI. Triggers on "generate music", "make a song", "AI music", "background music", "instrumental track", "ElevenLabs Music", "soundtrack", "jingle", "theme music", "royalty-free music", "compose", or any explicit ask to generate music or a song from a text description.

Generate full songs and instrumental tracks with ElevenLabs Music on RunComfy via the `runcomfy` CLI. ElevenLabs Music turns a style description plus structured lyrics into studio-quality 44.1 kHz stereo audio — 5 seconds to 5 minutes — with section-level control (Intro / Verse / Chorus / Bridge), multilingual vocals, and commercial-friendly output. Generate a backing track, a full vocal song, a jingle, a podcast intro, a game loop, or an instrumental bed. Calls `runcomfy run elevenlabs/elevenlabs/music-generation` through the local RunComfy CLI. Triggers on "generate music", "make a song", "AI music", "background music", "instrumental track", "ElevenLabs Music", "soundtrack", "jingle", "theme music", "royalty-free music", "compose", or any explicit ask to generate music or a song from a text description.

Generate full songs and instrumental tracks with ElevenLabs Music on RunComfy via the `runcomfy` CLI. ElevenLabs Music turns a style description plus structured lyrics into studio-quality 44.1 kHz stereo audio — 5 seconds to 5 minutes — with section-level control (Intro / Verse / Chorus / Bridge), multilingual vocals, and commercial-friendly output. Generate a backing track, a full vocal song, a jingle, a podcast intro, a game loop, or an instrumental bed. Calls `runcomfy run elevenlabs/elevenlabs/music-generation` through the local RunComfy CLI. Triggers on "generate music", "make a song", "AI music", "background music", "instrumental track", "ElevenLabs Music", "soundtrack", "jingle", "theme music", "royalty-free music", "compose", or any explicit ask to generate music or a song from a text description.

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

Requires AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `saas-gateway`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Unified SaaS integration gateway via api.aisa.one (AISA gateway, v3.1): manage OAuth auth for third-party SaaS apps (Gmail/Slack/GitHub/Notion etc.), tool execution, tool-router sessions, triggers, webhooks, MCP servers, and usage stats. Use when connecting third-party SaaS accounts, running cross-SaaS tools, managing MCP servers, setting up triggers, or checking usage. Keywords: SaaS gateway, connect app, OAuth link, run tool, auth config, tool router, MCP server, trigger, webhook, connected account, usage stats

Single entrypoint for taw-kit. User types `/taw <anything in VN or EN>` — this skill classifies the intent (BUILD / FIX / SHIP / MAINTAIN / ADVISOR) and loads the matching branch file to execute. Replaces the old one-command-per-task model (/taw-new, /taw-add, /taw-fix, /taw-deploy, /taw-security) with a single unified command. Supports dev workflows out of the box: test, upgrade, clean, perf, rollback, refactor, types, seed, review, stack-swap, status, and ADVISOR group (analyze, suggest, coverage, adversarial, scope-check) for opinionated review of existing code. User-visible strings match the user's input language (Vietnamese by default for VN users). Two modes: SAFE (default — clarify + approval, max 1 round-trip) and YOLO (skip gates, smart defaults — for demos/power users). YOLO triggers: prose contains `yolo`, `nhanh nha`, `lam luon`, `khoi hoi`, `auto`, or args start with `yolo`. Trigger phrases (EN + VN) — broad match so user can keep typing plain prose without re-invoking /taw every turn. Grouped by

Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC sc

Redige a acao revisional de contrato civil — revisao por desproporcao manifesta superveniente (CC 317), resolucao/revisao por onerosidade excessiva (CC 478-480), sempre nos limites da funcao social e da intervencao minima (CC 421 + 421-A, Lei 13.874/2019 — contratos presumidos paritarios) e da boa-fe objetiva (CC 422). NAO cobre revisional bancaria. Use quando o operador disser revisar contrato, acao revisional, onerosidade excessiva, reequilibrio contratual, clausula abusiva em contrato civil, ou rever os termos de um contrato civel.

Query the EMBL-EBI InterPro REST API for protein family, domain, and functional-site annotations integrated from member databases (Pfam, PANTHER, PRINTS, SMART, SUPERFAMILY, CDD, ProSite, NCBIfam, and others). Use when predicting protein function, analyzing or comparing domain architecture, classifying a protein by family or homologous superfamily, resolving a Pfam/InterPro accession, or mapping a protein's signatures to GO terms. Not for raw UniProt entry/FASTA retrieval or AlphaFold 3D structures. Part of the AlterLab Academic Skills suite.

Expert knowledge for Azure Role-based access control development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when designing Azure RBAC/ABAC scopes, custom roles, PIM, CLI/PowerShell/REST role ops, or ARM/Bicep templates, and other Azure Role-based access control related development tasks. Not for Azure Active Directory B2C (use azure-active-directory-b2c), Azure Information Protection (use azure-information-protection), Azure Policy (use azure-policy), Azure Security (use azure-security).

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

This skill suggests using approved weight‑loss medication over no pharmacological therapy to ameliorate comorbidities and improve physical activity in adults with BMI ≥30 kg/m² or BMI ≥27 kg/m² with at least one comorbid condition. It is triggered when a clinician asks, for example, 'Should I add medication to help maintain weight loss in this patient with BMI 31 and diabetes?' or 'Is pharmacotherapy appropriate for long‑term weight control in a patient with BMI 28 and hypertension?'.

Generate, visually edit, and export beautiful HTML/CSS presentation decks with agents using slides-grab (NomaDamas, MIT) — the open-source Claude Design alternative and best harness + editor + linter for slides in Claude Code / Codex. One routing-first skill across Plan (agent drafts an outline), Design (each slide is a self-contained slide-XX.html), Edit (a pure-JS browser editor where you drag a bbox over any region and ask the agent to rewrite just that area, or hand-tweak text/size/bold), and Export (capture-or-print PDF, per-slide PNG incl. Instagram 1:1 card-news, plus experimental/unstable PPTX and Figma-importable PPTX). Picks an install path (npm package + npx skills add, or clone), a deck workspace (--slides-dir, multi-deck decks/<name>/), one of 35 bundled design styles, and the supported asset flow (local ./assets/<file> only — image via god-tibo-imagen/codex/nano-banana, fetch-video via yt-dlp, tldraw .tldr→SVG), validating with slides-grab validate before any export.

Build fully customizable, agent-ready design systems with Astryx — Meta's production design system now open source. Ships 150+ React components built on StyleX with zero styling lock-in, component swizzling, brand theming, dark mode, and CLI tooling. Use when building component libraries, design systems, UI applications, design tokens, or when teams need consistent accessible components that AI agents can understand and extend. Triggers on: astryx, design system, component library, design tokens, react components, accessible components, astryx design, stylesheets, theme customization, component composition.

Decide when and how to assess minimal residual disease (MRD) in multiple myeloma — bone marrow MRD by next-generation flow (NGF) or sequencing (NGS), plus imaging MRD by PET-CT — and how to act on the result. Trigger when a clinician asks "how to assess MRD in myeloma", "MRD-negative complete response", "PET-CT for MRD", "is bone marrow MRD enough", "when to test for MRD", "next-generation flow for myeloma", or "myeloma sustained MRD negativity". Based on EHA-ESMO 2021.

World-class level design expertise - spatial storytelling, player flow, blockout methodology, and the invisible hand that guides players without them knowingUse when "level design, blockout, graybox, whitebox, player flow, level layout, combat arena, exploration space, map design, multiplayer map, pacing, weenie, landmark, gating, metroidvania, open world design, linear level, environmental storytelling, teaching through design, playtesting, heatmap, metrics, sightlines, level-design, game-development, spatial-design, blockout, graybox, player-flow, pacing, environmental-storytelling, combat-design, multiplayer-maps, open-world, linear-design, metroidvania" mentioned.

Generate user-friendly "Explain Like I'm 5" feature documentation for end users by reading the codebase. Use this skill whenever the user wants to document a feature for non-technical users, write help docs, create an explainer for a feature in their app, produce onboarding content, or asks "make a user doc for X". Trigger on phrasings like "ELI5 the closeout flow", "write user docs for the kanban view", "explain this feature to a customer", "make a help article for X", "document this for end users". Also known as "E-features-LI5". This skill produces plain-language docs written for someone with the app open trying to get something done — not internal developer docs (use the `teach` skill for those) and not feature announcements.

SEO, AEO (Answer Engine Optimization), and GEO strategy for search engines and AI visibility. Use when working on "SEO audit," "technical SEO," "on-page SEO," "AI search optimization," "AEO," "GEO," "AI visibility," "optimize for ChatGPT," "optimize for Perplexity," "AI Overviews," "answer engine optimization," "generative engine optimization," "AI citations," "featured snippets," "meta tags," "schema markup," "search ranking," "content optimization," "E-E-A-T," "structured data," "search console," "SEO health check," "why am I not ranking," "AI search readiness," "backlink strategy," "link building," "domain authority," "programmatic SEO," "SEO at scale," "template-based SEO," "AEO monitoring," "AI search monitoring," "JSON-LD," "rich snippets," "schema.org," "SERP analysis," "search intent," "site architecture," "information architecture," "URL structure," "internal linking," or "navigation." For keyword research, see keyword-research-and-clustering.

Reviews pull requests and local diffs against Google's eng-practices code review guide (design, complexity, tests, naming, comments, style, consistency, documentation, every-line, context) and emits a ranked, principle-tagged finding list with rich local diff rendering. Optional follow-up step posts selected findings as a GitHub PR review. Invoke when the user says "googly-eyes this PR", "review by the canon", "principled code review", "Google-style review", "review this CL", or pastes a PR URL and asks for a code review. 👀

Use for writing, reviewing, debugging, or testing Python analysis of Bayesian inference results with ArviZ, including 1.x DataTree groups, legacy InferenceData inputs, xarray dimensions and coordinates, conversion, summaries, R-hat/ESS/MCSE diagnostics, posterior predictive checks, PSIS-LOO, Pareto-k, and model comparison. Trigger on chain/draw shape errors, mislabeled groups, flattened samples, missing log likelihood, or misleading diagnostic claims. Do not use to construct or sample PyMC, NumPyro, or Bambi models, for generic plotting, or for deterministic statistics without Bayesian draws.

Expert knowledge for Azure IoT Central development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing device templates, data export, IoT Edge gateways, REST/CLI automation, or IoT Central–to–IoT Hub migrations, and other Azure IoT Central related development tasks. Not for Azure IoT Hub (use azure-iot-hub), Azure IoT Edge (use azure-iot-edge), Azure IoT Operations (use azure-iot-operations), Azure Digital Twins (use azure-digital-twins).

Turn a champion's enthusiasm into a one-page business case the buying committee can approve. Frames the cost of the problem, the change you enable, the value model, and the risk of doing nothing, in the buyer's own numbers. Built for B2B sales teams, customizable to your value drivers and your CRM. Trigger on "build a business case", "ROI narrative", "justify the spend", "one-pager for the committee", "cost of doing nothing", or any deal that has to clear a budget holder.

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.

Integrate with SAP systems and build extensions. Use when a user asks to connect to SAP S/4HANA, SAP Business One, or SAP ERP via OData, RFC, BAPI, or IDoc interfaces, build SAP BTP (Business Technology Platform) applications, work with SAP CAP (Cloud Application Programming), consume SAP APIs from the API Business Hub, manage master data, automate procurement or sales processes, build Fiori apps, extract SAP data for analytics, or integrate SAP with external systems. Covers S/4HANA APIs, Business One, BTP, CAP, and integration patterns.

Use when developing in packages/agent-core-v2 (the DI × Scope agent engine) — adding or modifying a domain Service, choosing a LifecycleScope, wiring DI dependencies, splitting a domain across scopes, owning or migrating a config section, gating behavior behind an experimental flag, raising coded errors, working on the permission system, writing DI/Scope tests, porting business logic from agent-core (v1) to v2, triaging a main-branch commit against v2, or exposing a v2 domain over server-v2 while keeping the /api/v1 wire contract compatible with released clients. Self-contained guide organized by development stage (orient → design → implement → test → verify) plus align workflows for v1→v2 migration, main-branch commit triage, and server-v2 wire exposure; each file carries the rules, examples, and red lines for its step.

World-class autonomous sales and revenue skill system. Use ANY time the user asks to sell, pitch, prospect, close, negotiate, launch, monetize, build funnels, write outreach, craft proposals, develop pricing, design offers, write sales copy, create email sequences, plan campaigns, position products, handle objections, create playbooks, build pipeline, forecast revenue, develop GTM strategy, optimize conversions, write ad copy, create sales decks, score leads, nurture prospects, upsell, cross-sell, retain customers, write case studies, price products, structure enterprise deals, create battle cards, script discovery calls, build affiliate programs, plan product launches, create webinar funnels, design pricing pages, A/B test offers, analyze unit economics, calculate LTV/CAC, or ANY other sales, revenue, monetization, go-to-market, or commercial growth task. If it involves selling, revenue, deals, pipeline, or commercial strategy — USE THIS SKILL. Trigger aggressively.

Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid.

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaf

Builds a new Claude Code skill from scratch through a relentless, evidence-based interview that walks the skill's design tree decision-by-decision — entity fit, use cases, name, description, workflow steps, tools, and progressive-disclosure layout — then reviews the finished skill against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new skill or slash command. Does not build an agent or subagent — use agent-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.