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概要と使いどころ
Guide for running acceptance tests for a Terraform provider. Use this when asked to run an acceptance test or to run a test with the prefix `TestAcc`, when a test fails with missing environment variables, or when diagnosing a failing or suspiciously passing acceptance test.
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Audit all language versions of a site for parity and quality. Tags only → hreflang-check. "is the German site missing pages"
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。
Pick the right LLM for CONTRACT REVIEW — reviewing an existing agreement for risk, deviations from standard, missing protections, and internal contradictions, and proposing redlines. Vendor-neutral routing grounded in mid-2026 legal benchmarks. Contract review is a COMPOSITE skill (extraction + reasoning + redlining + conflict-detection), so this routes by the dominant sub-task. Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/ language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to review this contract / NDA / MSA", "best AI to redline", "route this contract review", or is about to review an agreement without a fixed model.
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Screen a client, counterparty, payer or corporate against the sanctions lists published by the designating authorities themselves — the UK Sanctions List (FCDO), OFSI, the UN Security Council, the EU consolidated list and OFAC's SDN and non-SDN lists. Use when onboarding, before taking a payment, before completing a transaction, or whenever a name must be checked against sanctions. Produces candidates and provenance, never a clearance: the record names every list searched with its publication date and count, states what was NOT searched, and ends at a blank decision block. Fails closed — a cache that is missing, damaged, truncated or stale is reported NOT SEARCHED and the process exits non-zero, so nothing can read an incomplete run as clean. Matches across transliteration, diacritics, homoglyphs and initials, because nobody spells a name the way the publisher does. Standard library only; screening opens no network connection.
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Adversarial verification of AI-generated legal content with systematic fact-checking, source validation, and quality control. Use when a user asks to verify, fact-check, red-team, validate sources, or quality-control a legal document, briefing, compliance summary, or regulatory analysis before it is distributed to clients, stakeholders, or published. Trigger phrases include "verify", "fact-check", "red team", "red-flag", "check accuracy", "validate sources", "quality control", "is this correct", and "review for errors". Produces a structured verification report with severity-categorized errors, verified sources, unsupported claim list, missing disclaimers, and an explicit distribution-readiness assessment.
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First-pass framework for reading, interpreting, and structuring statutory analysis of US federal, state, and local law. Produces draft analysis for attorney review — not legal advice. Use this skill whenever the user references a specific US statute, regulation, ordinance, or rule by citation, asks "what does [statute X] require," asks for compliance scoping, applicability thresholds, requirement extraction, exemption analysis, definitional analysis, federal preemption analysis, or multi-state comparison — even if they don't explicitly ask for "statutory analysis." Halts and asks for missing inputs rather than guessing. Out of scope for non-US law.
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。
NIL (Name, Image, and Likeness) contract analysis for NCAA student-athletes from the athlete's perspective. Use when user says 'review this NIL contract', 'analyze this NIL deal', 'check this athlete agreement', 'review my NIL agreement', or uploads a PDF NIL contract for review. Identifies red flags, missing protections, and compliance issues. Produces a structured review memorandum with negotiation positions. Do NOT use for general contract review, employment agreements, non-NIL endorsements, or brand-side deal analysis.
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Evaluate completed mediation-competition problems consisting of general information and confidential packets for both sides. Use when asked to validate, audit, score, quality-control, or assess competition readiness; identify factual, numerical, chronological, authority, commercial-logic, or confidentiality defects; test mediation usefulness, BATNAs, settlement corridors, information asymmetry, and opportunities for firmness, cooperation, imagination, and clarification; or produce an author-only validation report. Perform a qualified partial review when packets are missing. Diagnose by default; do not generate a new problem or edit source packets unless repairs are expressly requested. Requires access to all supplied files and a host capable of reading their formats; otherwise disclose the unreviewed material and resulting limits.
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Audits legal AI outputs, prompts, skills, workflows and MCP/tool instructions for fluent but unsupported reasoning, missing evidence, overconfidence, hidden assumptions, weak causal links and absent human-review gates.
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Best-practices reference for preparing and instrumenting applications on Temps. Covers the app runtime contract (`.temps.yaml` health, HOST/PORT, readiness, SIGTERM, replicas, migrations) and production observability (errors, traces, metrics, logs, analytics, privacy, sampling, cardinality, credential boundaries). Use whenever building or reviewing an app for Temps, configuring health checks, adding telemetry, diagnosing missing/noisy signals, or checking OTLP/Sentry/analytics ingestion. Triggers include "temps best practices", "prepare this app for Temps", ".temps.yaml health", "temps health check", "ignore health checks in otel", "temps observability", "wire up telemetry", and "instrument this app for temps". Prefer focused setup skills for a single SDK. Use temps-cli for executing deploy and resource-management commands.
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。
Manage, deploy, operate, and instrument applications with Temps. Use this skill whenever the user mentions Temps, `@temps-sdk/cli@0.1.37`, deploying or migrating an app to Temps, projects, environments, services, domains, backups, logs, monitoring, analytics, browser Performance Insights/Core Web Vitals, observability, error tracking, OpenTelemetry, tracing, session replay, or Temps Cloud. Also use it when preparing an application for production on Temps even if the user does not explicitly ask for the CLI. Route to focused references, proactively detect missing observability during create/link/deploy journeys, and use pinned `bunx` or `npx` CLI invocations with explicit target contexts.
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Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node. Render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video completes but looks wrong, such as artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, or a two-stage refiner or upscale degrading the result. For errors/OOM/missing nodes use the troubleshooting skill.
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。
Common ComfyUI errors and fixes. OOM, missing nodes, dtype mismatches, black images, and debugging strategies
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Curated download URLs and target directories, organized by family (Flux, WAN, LTX, Qwen, Z-Image, SD15/SDXL), for every model the comfyui-mcp skills reference, covering checkpoints, VAEs, text encoders, and LoRAs. Use when downloading models with download_model (action:"download" / action:"download_civitai"), when a workflow fails with a missing-model error, or when setting up a new machine.
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Verify UI *workflow* correctness that a task list, code review, and static screenshots miss. Drives end-to-end user flows in the Simulator via XCUITest with per-step screenshots, statically audits the navigation graph for dead-ends and missing edit paths, and emits a human discoverability checklist. Use after building any phase/slice that adds or changes UI, or when a user reports "I could only figure out the flow by running it."
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。
Stage-by-stage audit of an app's App Store growth machinery against a 54-item P0–P9 playbook — every item scored from an App Store Connect MCP call, a codebase check, or an explicit question to the user, then routed to the skill or command that fixes it. Read-only on App Store Connect. Use for a growth audit or scorecard, a pre-launch growth plan, a quarterly re-audit, or "which growth levers am I missing."
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Take a one-time in-app purchase from MISSING_METADATA to READY_TO_SUBMIT in App Store Connect — set its price schedule and localized display name/description (and optional review screenshot) via the ASC REST API. Use at Phase 6 (Pre-Release), after the IAP is built in-app (Phase 4) and its ASC record exists. NOT for subscriptions, and NOT the same as promoted-iap (which displays IAPs in-app).
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。
How App Store ratings actually behave — per-storefront isolation (your US stars show nowhere else), the never-reset rule, phased release + manual release as rating protection, and where prompting/replying fit. Use when planning ratings strategy for new markets, considering a ratings reset, setting release options, or diagnosing "why is my rating missing in country X."
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An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.
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performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results.
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Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA o
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。
Point Aeon at a service's /feedback endpoint - pull what agents reported (bugs, missing features), triage and cluster it, then build the best accepted request as a PR for a human to approve.
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。
Helps the user come up with free lead magnets or hooks to offer in cold emails. Asks about their business, what they can legally give away, and what would be genuinely valuable to their ICP. Returns 5-10 concrete offer ideas with example CTA phrasings. Use when the user says their campaign "needs a hook", "needs an offer", "what can I give away", or when ICP onboarding flagged a missing lead magnet.
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。
Amazon listing builder and optimizer for sellers. Two modes: (A) Create — build keyword-optimized listings from scratch using keyword lists + product characteristics + AI copywriting, (B) Optimize — audit existing listings, find keyword gaps, score across 8 dimensions, and rewrite with missing keywords. Integrates with amazon-keyword-research for keyword input. Works on 12 Amazon marketplaces. No API key required. Use when: (1) creating a new Amazon listing from keywords, (2) auditing an existing listing for SEO and conversion, (3) checking keyword coverage in title/bullets/description, (4) generating listing copy with target keywords and tone, (5) comparing listings against competitors, (6) preparing a listing for launch or relaunch.
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。