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Use when an American Economic Journal: Applied Economics (AEJ: Applied) manuscript needs a model to interpret, discipline, or structure its empirical estimates — not to lead the paper. Calibrates how much theory belongs in an empirical-first journal and where it goes; it does not design the identification (aeja-identification) or build a standalone structural estimation.

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Use when an AEJ: Economic Policy manuscript's headline policy estimate needs to be shown stable and credible against specification, sample, inference, and identification threats. Organizes the robustness program by threat-to-the-policy-conclusion; it does not design the primary identification or write exhibits.

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。

Render a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.

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Analyze code coverage and CRAP (Change Risk Anti-Patterns) scores to identify high-risk code. Use OpenCover format with ReportGenerator for Risk Hotspots showing cyclomatic complexity and untested code paths.

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Plot per-gene mutation distributions on a protein-domain map (lollipop / needle plots) showing mutation position, recurrence count, and variant classification with maftools, g3-lollipop, trackViewer, and ProteinPaint. Use when visualizing recurrent mutation hotspots on a single gene's protein, marking domain boundaries from UniProt/Pfam, comparing missense vs truncating distributions, or contrasting two cohorts on the same lollipop.

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Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.

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Looks up current LiveKit facts (API signatures, CLI flags, config options, model and provider support, SDK changelogs, pricing) from the docs instead of answering from memory. Use whenever a question touches LiveKit specifics: "does LiveKit support X", "what changed in agents 1.8", "what are the arguments to Y", "how much does LiveKit cost", "find an example of Z in the LiveKit repos", or before writing any LiveKit code. Other LiveKit skills load this one first. Covers the LiveKit Docs MCP server and the `lk docs` CLI.

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This skill should be used when the user asks "how to setup GitHub CLI", "configure gh", "gh auth not working", "GitHub CLI connection failed", "gh CLI error", or needs help with GitHub authentication.

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Use when publishing harness improvements to the remote kit repo. Diffs managed files, shows what changed, bumps version, and pushes. Counterpart to vc-update (pull).

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Use when writing, rewriting, or improving technical docs (quickstarts, how-tos, tutorials, concept pages, or API references).

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。

Automated outreach and sales pipeline — cold email sequences, partnership outreach, lead generation, and prospect management. Use when the user needs cold email campaigns, sales sequences, lead generation, partnership outreach, B2B sales, or says 'cold email', 'outreach', 'lead gen', 'find prospects', 'sales sequence', or 'partnerships'.

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Paid advertising automation for Google Ads, Meta Ads, and other ad platforms. Creates campaigns, optimizes budgets, manages keywords, writes ad copy, and tracks ROAS. Leverages third-party ad management skills when available. Use when the user needs ad campaigns, PPC, SEM, paid traffic, or says 'run ads', 'Google Ads', 'Meta Ads', 'Facebook Ads', 'ad campaign', 'PPC', 'ROAS', or 'paid traffic'.

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。

Code and product quality gates for shipping with confidence. Runs code review, QA testing, performance checks, and security audits. Use when the user says 'review my code', 'is this ready to ship', 'check quality', 'run QA', 'test this', 'security check', 'pre-launch review', or wants a quality assessment before deploying.

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Social media management and community building automation. Creates content calendars, drafts posts, manages engagement, and builds audience across X/Twitter, LinkedIn, Reddit, Product Hunt, and other platforms. Use when the user needs social media strategy, content scheduling, community building, or says 'social media', 'tweet', 'LinkedIn post', 'Reddit', 'Product Hunt launch', or 'build audience'.

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Render the multi-agent orchestration DAG for a session — parent→child subagent edges, tree depth, and fan-out — from the Agent Monitor workflow intelligence API. Cross-checks the orchestration dataset against the raw agent records and session detail. Use when visualizing how a session's agent structure was organized.

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Audit model delegation and subagent effectiveness for a session — which models handled which subagent types, per-type success rates and average durations, and wasted delegations (heavy models on trivial work or types that consistently fail) — using the Agent Monitor workflow intelligence API. Use when reviewing how a session delegated work across models and subagents.

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Fetch unread GitHub notifications for open PRs where review is requested from a specified team or opened by a team member. Use when asked to "find PRs I need to review", "show my review requests", "what needs my review", "fetch GitHub review requests", or "check team review queue".

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Runs a security scan on a deployed Power Pages site, fetches the latest scan report, and produces a plain-language summary. Scans the live site's public surface for vulnerabilities and surfaces issues by severity. Use when the user wants to scan, check, test, audit, or assess a published site, find vulnerabilities on production, view the latest scan report, see previous scan results, run a security audit, or asks "how safe is my live site?", "is my site vulnerable?", "audit my production site" — even if they say "find issues" or "check for problems" without mentioning "scan" or "security".

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Use when the user wants to seed Dataverse tables with realistic sample records so a freshly-scaffolded code app shows real-looking data on first launch. Generates contextually appropriate rows from each table's schema and inserts them in dependency order. Mirrors microsoft/power-platform-skills/power-pages/add-sample-data, adapted for mobile apps.

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 when rigging a character, creature or prop in Blender through Python: "rig this character", Rigify metarig fitting or generation errors, automatic weights or "Bone Heat Weighting failed", weight painting fixes, clothes that do not follow, IK/FK switch, foot roll, twist bones, drivers and custom properties, corrective shape keys, a stylized face rig, a mechanical or hard-surface rig, or testing how a rig deforms.

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Evaluate architecture and module boundaries for volatility-based decomposition using Juval Lowy's framework (from "Righting Software", building on Parnas 1972). Use when reviewing module splits, service boundaries, new abstractions, or any decomposition decision. Trigger on phrases like "where should this boundary be", "how to split this", "module boundaries", "encapsulate change", "volatility", or references to Lowy, Parnas, or "Righting Software". Complements /hickey (interleaved concerns) with a different lens (change encapsulation).

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Plan how to earn customer trust and appropriate access. Use when credibility, permissions or AI policy constrain the engagement.

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Prepare a customer demo or executive walkthrough around an evidenced outcome. Use before show-and-tell to rehearse the journey and prepare a fallback.

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How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.

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。