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Tests API contracts for REST, GraphQL, gRPC, AsyncAPI, and webhooks. Use when adding schema validation or breaking-change gates (Pact, can-i-deploy, oasdiff, buf); not API design.

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Builds ML pipelines: EDA, features, leakage checks, evaluation. Use when doing data science or explaining fairness, privacy, speech, vision-language, or diffusion mechanics.

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Use when the user wants to switch this Codex session to another Cognee dataset, see which datasets are available, or move memory capture to a different dataset. Syncs the current session first, then starts a fresh Cognee session bound to the chosen dataset.

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Multi-model agent orchestration using specialized agents for planning, coding, research, math/science, visual analysis, and adversarial review. Use when tasks are complex enough to benefit from different models' strengths, when you want adversarial review to catch blind spots, or when coordinating multi-step workflows across agent roles. Triggers on complex projects, multi-step tasks, architecture decisions, or when explicitly requested.

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Executes an existing implementation plan task by task, preserving its interfaces and constraints, running focused checks after each slice, and stopping when the repository differs from the plan. Use when a written plan or approved checklist already exists. Not for inventing the plan, debugging an unexplained failure, or broad unscoped cleanup.

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Processes code review feedback by classifying each finding, reproducing or validating it against the real contract, applying the smallest in-scope fix, and re-running focused proof. Use after comments arrive on a diff, pull request, design, or implementation. Not for requesting a first review or debugging an unrelated failure.

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。

Closes a completed development branch by checking the final diff and proof, presenting merge, pull-request, keep, or discard options, and cleaning up only after the user or repository workflow chooses a path. Use when feature work is complete and the branch must be integrated or retired. Not for claiming a feature is complete before verification.

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Audits an existing rendered interface or frontend diff for hierarchy, content clarity, interaction states, accessibility, responsive behavior, visual consistency, performance-sensitive UI patterns, and anti-generic design defects. Use for a UI/UX review, redesign audit, screenshot review, pre-release visual check, or design-system consistency pass. Not for building a new interface from scratch or for backend-only review.

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Investigates a bug, failing test, build error, regression, flaky behavior, performance symptom, or unexpected result by reproducing it, minimizing the case, testing ranked hypotheses, tracing the first incorrect state, and locking the root cause with a regression check. Use before proposing a fix. Not for planning a new feature or reviewing a healthy diff.

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Prepares a focused code review request for a branch, commit, pull request, or substantial working-tree diff by defining the review boundary, contract, risk areas, evidence, and questions for an independent reviewer. Use before merge or when another agent needs to review an implementation. Not for processing feedback already received or for claiming runtime correctness.

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。

Reviews a GitHub pull request or focused branch diff for correctness, regressions, security, compatibility, test gaps, and maintainability. Use when an implementation needs an independent source-aware review before merge. Produces prioritized findings with evidence and actionable fixes. Not for architecture-only audits, formatting preferences, or processing existing review feedback.

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Verify HitKeep tracking setup and automatic event collection. Use when installation status, missing pageviews or events, WordPress or script installs, automatic outbound clicks, downloads, form submissions, hostname mismatches, blockers, consent, CSP, or the difference between tracker failure and analytics interpretation is in question.

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Use this skill when you need to read, inspect, or extract content from PDF files — especially when file content is NOT in your context and you need to read it from disk. Covers content inventory, text extraction, page rasterization for visual inspection, embedded image/attachment/table/form-field extraction, and choosing the right reading strategy for different document types (text-heavy, scanned, slide-decks, forms, data-heavy). Do NOT use this skill for PDF creation, form filling, merging, splitting, watermarking, or encryption — use the pdf skill instead.

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Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations as PowerPoint (.pptx) files; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email, summary, or creating a different type of slide deck); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user asks for a PowerPoint or .pptx file, or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. However, when the user asks for a deck, slides, a slide deck, or a presentation without naming a file format, default to using a dedicated slide-deck artifact type or a separate slides skill if this session offers one; otherwise, use this 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。

Nexscope keyword expansion tool that expands a seed keyword into a list of related keywords with search volume, trends, PPC bids, ranking difficulty, and other metrics, covering 10 Amazon marketplaces including US, UK, DE, JP, etc. Trigger when the user mentions keyword expansion, keyword mining, long-tail keyword mining, related keywords, keyword suggestions, keyword discovery, PPC bid research, keyword competition, keyword discovery, Nexscope keywords, keyword expansion, keyword discovery, keyword scout, related keywords, long-tail keywords, keyword suggestions, PPC bid research, keyword competition, seed keyword expansion, keyword mining. Even if the user does not explicitly mention "Nexscope", if their need involves finding more related keywords and their search volume, competition, and other metrics starting from a seed keyword, this skill should also be triggered.

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Static code review of the active diff: trace changed paths and report concrete P1/P2/P3 correctness, security, or spec bugs with file:line evidence. Use for code review or bug checks. Not runtime QA.

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Converting technical knowledge into durable learning documents and publishable articles. Use for diff-based teaching, decision records, onboarding, note/Zenn/Qiita/dev.to posts, article series, retrospectives, and cross-platform repurposing.

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Instrument a new AI agent in a Python codebase for Monte Carlo Agent Observability. Detects AI libraries, installs the Monte Carlo OpenTelemetry SDK, and proposes tracing setup and decorator placements as diffs. Asks before editing any file.

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Analyze WeChat Mini Program platform constraints and evidence layers for an idea or existing repository, including source configuration, build output, WeChat DevTools loading, device behavior, permissions, privacy declarations, cloud or backend linkage, upload, trial, review, and production status. Use when users ask about WeChat-specific configuration, project roots, compile paths, capability permissions, privacy compliance, official platform rules, upload or release readiness, or why source and platform behavior differ. Performs read-only evidence classification by default, verifies time-sensitive or high-risk claims against current official WeChat sources, redacts identifiers and secrets, and never upgrades one evidence layer into another.

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Generates multiple means of engagement, representation, and action/expression for a given learning goal. Produces specific, practical alternatives — not generic options — and recommends the highest-impact single change.

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Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.

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Audits an existing lesson against UDL's three principles — engagement, representation, and action/expression. Identifies specific access barriers and suggests concrete modifications ranked by impact.

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Claude Code CLI second opinion for non-Claude Code hosts. Review a diff, challenge a change for failure modes, or consult Claude with read-only repo access and session continuity. Use for "claude review", "claude challenge", "ask claude", or an explicit Claude Code second opinion. (gstack)

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The learner teaches the concept to the AI, which plays a curious novice peer and identifies gaps through authentic questions. Use when the learner wants to test their understanding — teaching forces a different kind of organisation than studying.

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。