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Score how hard a keyword is to rank for in the AI-search era — page-level URL Rating of real competitors (not just domain DR), Ahrefs keyword difficulty, and whether a given site already ranks or is cited in Google's AI Overview. Use when the user asks 'how hard is <keyword> to rank', 'geo difficulty for <keyword>', or 'keyword difficulty for <domain>'.
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Creates a lesson plan, student-facing materials, and observation template. Load this skill BEFORE asking the teacher any clarifying question about grade, subject, topic, standard, or timing. Use when a K-12 teacher needs a math, ELA, science, or social studies lesson built from scratch — even if grade, subject, or topic isn't yet stated. Do NOT load for grading, a rubric, assessment feedback, a quiz, or a standards lookup — answer those directly. Triggers on explicit requests (lesson plan, mini-lesson, unit plan, daily plan) and implicit teacher intent: "I'm teaching long division to 5th graders," "need to teach photosynthesis tomorrow." Core signal: teacher needs new instructional content created. A new lesson that asks for differentiated, tiered, or leveled materials is still ONE planning request — this skill produces those materials inside the lesson package; do not also invoke k12-lesson-differentiation. Not for differentiating an existing lesson (use k12-lesson-differentiation) or passage rewrites.
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Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.
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Use Diffusion Planner for nuPlan autonomous-driving trajectory generation: prepare model-ready data, train or resume the diffusion model, configure closed-loop planning, and add differentiable collision or classifier guidance.
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Use audio-diffusion-pytorch for PyTorch waveform diffusion generators, text-conditioned audio generation, inpainting, upsampling, vocoding, and diffusion autoencoding.
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Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.
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Deploy differential privacy in production systems including epsilon selection strategies, noise calibration with Laplace and Gaussian mechanisms, privacy budget tracking, composition theorems, and Python implementation patterns. Covers both central and local differential privacy models.
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Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
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All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.
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Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.
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Maps the `hunkdiff/extension` authoring surface for Hunk, the terminal diff viewer — hiding or reordering reviewed files, docked panes, alternate file views, commands and key bindings, dialogs, workspace writes, themes, syntax languages, VCS backends, lifecycle events. Use when writing, debugging, or installing a Hunk extension, or when a request asks Hunk itself to behave differently. Not for reviewing a diff in a live session — that is hunk-review.
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Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
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See the difference between Chat and Cowork with a live side-by-side comparison for understanding why Cowork is built differently. Use when evaluating AI tools, for comparing chat versus agent capabilities, during an AI tool decision, when switching from ChatGPT or Claude Chat, or when the user says "what is different about cowork", "cowork vs chat", "why not just use chatgpt", "show me the difference", "compare chat and cowork", or "why should I use cowork instead of chat".
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Compares two related malware samples to surface what changed between variants using section-level hashing, import-set differences, and fuzzy similarity scoring. Activates for requests to diff two samples, compare malware variants, or measure similarity between two binaries for variant tracking.
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Causal inference with treatment/control groups over time. Use when user mentions: treatment effects, policy evaluation, pre/post comparison, parallel trends, DID, DiD, difference in differences, natural experiment, quasi-experimental.
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Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.
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Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
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Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.
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Expert-level differential equations knowledge. Use when working with ordinary differential equations, partial differential equations, systems of ODEs, Laplace transforms, Fourier methods, boundary value problems, stability analysis, or numerical methods for ODEs. Also use when the user mentions 'ODE', 'PDE', 'initial value problem', 'boundary value problem', 'Laplace transform', 'separation of variables', 'eigenvalue problem', 'heat equation', 'wave equation', 'Laplace equation', 'stability', or 'phase plane'.
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Build a rich, self-contained HTML explanation of a code change — diff, branch, commit range, MR/PR — with background, intuition, a code walkthrough, diagrams, and a glossary, then quiz the reader interactively in chat. Use when the user asks to explain a change, understand a PR/MR, walk through a diff, onboard onto a branch, or says "explain this PR", "what does this branch do", "teach me this change", or "/explain-diff".
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spec-drift issue が起票され diff トリアージが必要なとき、C11 が再構成した issue 単位の完全 diff を hunk 化し name/type/required/enum/semantics 各軸の影響を before/after/evidence 付きで判定して triage-report を出したいときに使う。
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Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.
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ISTQB/JSTQB のテストプロセス各工程の出口ゲートとして、工程成果物（test-plan.md / test-analysis.md / test-design.md / test-case.md / テスト実装 / test-execution-log.md / test-monitoring.md / test-summary-report.md）をレビューするスキル。決定論スクリプトの機械検査（トレーサビリティ ID 突合・テンプレ準拠）と test-reviewer サブエージェントの定性レビューを行い、利用者が通過/条件付き通過/差し戻しを判定して軽量記録 test-review-<工程>.md を残す。上流の仕様・基本設計（spec / design-doc 工程。docs/dev/<対象>/spec.md・basic-design.md）は機械検査と利用者判定のみの軽量ゲートとして扱う。各工程スキル（test-plan〜test-report / feature-spec / basic-design）が完了宣言の前に本スキルの実行を促す。ソースコード diff のレビュー（diff-review）とは別物で、対象はテストプロセスの成果物のみ。成果物の修正・代作はしない。/test-review <テスト対象名> [工程] で明示的に呼び出されたときのみ使用する。
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テストは存在するがアサーションが実質何も検証しておらず、実装が壊れても落ちない（常に PASS する）構造を diff-time で検出する。Check 1 missing assertion（テスト本体にアサーションが無い）、Check 2 tautological assertion（定数同士・入力自身・mock の戻り値自身を assert し SUT に依存しない）、Check 3 nonexistent expected literal（assertDontSee / assertNotContains 等の期待文字列が対象ファイルに実在しない）、Check 4 stale expected value（同一 diff で対象の出力が変わったのに期待値が据え置き）、Check 5 unscoped expectation（汎用的な属性・クラスを応答全体に対して assert し対象要素にスコープされていない）、Check 6 swallowed failure（例外の握り潰しや到達しない位置のアサーションで判定が成立しない）、の 6 Check を対象とする report-only。テストの有無は test-existence、未テスト経路の量は coverage-gap、非決定性は flaky-test、命名は test-naming、JS/TS の un-awaited resolves / rejects は vitest-mock-isolation、tdd-ledger artifact ベースの RED/GREEN 検証は plangate-tdd-evidence、.only / .skip / xit / @ts-ignore と空の catch は heuristic-review.mjs の決定論検出器へ委譲する
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