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Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.

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Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.

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。

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

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N-day 补丁差分到利用。从厂商发布的补丁里反推漏洞点、写 PoC、做成可用的攻击模块。 适用场景：已知 CVE 编号但只有补丁没有 PoC、SRC/红队需要打击未及时更新的资产、N-day 武器化、Patch Tuesday 跟进。 核心方法：拿 before/after 二进制 → 对齐符号 → 二进制 diff → 看新增的安全检查反推 bug class → 写 PoC 触发漏洞。 触发关键词：N-day、Nday、补丁差分、patch diff、patch tuesday、1day、binary diff 漏洞、bindiff 利用、ghidriff、Diaphora、补丁分析、CVE 复现、漏洞还原、补丁反推、N-day 武器化。

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Predict small-molecule binding poses with DiffDock-L (Corso et al. 2023/2024, github.com/gcorso/DiffDock) — blind diffusion docking that places a ligand into a protein pocket without a predefined search box and ranks the samples with a learned confidence model. Reach for this skill to dock a SMILES or SDF against a PDB, to generate ranked 3D poses for a small fragment library, or to get a starting pose for downstream rescoring. DiffDock predicts geometry, not affinity.

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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).

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。

提供在WinForms中通过ElementHost嵌入DiffPlex的WPF DiffViewer控件，实现两个字符串的对比及差异高亮显示的代码方案。

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Generate and interpret security-research diffs between Windows versions or patch levels using this repo's WinDiff CLI and databases. Use when comparing Windows builds or binaries such as ntoskrnl.exe, ntdll.dll, win32k*.sys, ci.dll, or cng.sys to find changed syscalls, symbols, types, mitigation flags, callbacks, ETW/EtwTi telemetry, code-integrity behavior, drivers, or attack surface. Explain likely intent and security relevance with Windows-internals knowledge, and frame findings for anti-malware/EDR, anti-cheat, and vulnerability-research audiences rather than returning an uninterpreted symbol list.

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Use denoising-diffusion-pytorch for PyTorch DDPM/DDIM image diffusion, 1D sequence diffusion, conditioning and guidance, and advanced diffusion variants.

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N-day / patch-diff workflow - given a CVE/advisory or a suspicious patch, diff pre- vs post-patch to locate the fixed bug, build a PoC for the unpatched version, and run variant analysis for a fresh bug. Triggers - "n-day", "patch diff", "diff the patch", "bindiff".

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Use Pierre Computer Company's `@pierre/trees` (path-first file tree) and `@pierre/diffs` (shiki-based code/diff renderer) in Kolu. Pierre ships Preact/vanilla cores with optional React wrappers — Kolu consumes the vanilla classes and wraps them in thin SolidJS components. Trigger when: wiring up a file tree, rendering unified diffs or syntax-highlighted files, replacing `@git-diff-view`, or any mention of `@pierre/trees` / `@pierre/diffs` / "pierre library".

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Implement a generative diffusion model (DDPM or score-based) with noise scheduling, U-Net architecture, training loop, and sampling procedures including DDIM acceleration. Use when building a generative model for image, audio, or molecular synthesis; implementing DDPM from a research paper; adding a custom noise schedule or conditioning mechanism; replacing a GAN-based generator with a diffusion alternative; or prototyping before scaling with production frameworks like diffusers.

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Analyze pre-trained generative diffusion models (Stable Diffusion, DALL-E, Flux) by computing quality metrics (FID, IS, CLIP score, precision/recall), inspecting noise schedules, extracting and visualizing attention maps, and probing latent spaces. Use when evaluating a pre-trained generative diffusion model's output quality, comparing noise schedule variants, analyzing cross-attention patterns for text-conditioned generation, interpolating between latent codes, or detecting out-of-distribution inputs.

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Before committing any non-trivial change, dispatch the diff to a reviewer runtime via ATO (`ato dispatch <reviewer> --session <id>`), parse the numbered/severity-tagged findings, apply or defer each one with a recorded justification, then commit. Fights the "build passes therefore ship it" failure mode — what Garry Tan calls the AI agent complexity ratchet. Place in the v2.16 stack: this skill is the LAST gate. `ato-warroom` decides the design; `ato-mission` runs the multi-step work and produces the diff; `ato-review` checks the diff before commit. When the review is part of a Mission, dispatch the review with `--require-tools read_file,grep,git_diff,git_log` so the reviewer can walk the source itself instead of reasoning from a paraphrase (PR-1.5 tool surface). Receipts land in `execution_logs` and the Mission narrative. Fires automatically before commits touching public surface (CLI subcommands, Tauri commands, MCP tools, schema migrations, security boundaries) or whenever a diff exceeds ~50 LOC of behavior change.

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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).

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。

Geração de imagens com tecnologia de ponta usando Stable Diffusion via HuggingFace Diffusers. Use quando gerar imagens a partir de prompts em texto, realizar tradução de imagem para imagem, inpainting ou construir pipelines de difusão customizados.

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Install or synchronize BMS差分 from a beatoraja-compatible difficulty table, optionally download missing parent songs, and place charts into matching music folders without overwriting existing files. Use for 難易度表 URL, header.json, url_diff, or beatoraja差分 installation requests; do not use for ordinary BMS library organization unrelated to a difficulty table.

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PR・ブランチの変更差分（git diff）を静的解析してセキュリティ脆弱性を検出する。開発中・PRレビュー時に使用。手動呼び出し専用: /sec-diff-review

提供在WinForms中通过ElementHost嵌入DiffPlex的WPF DiffViewer控件，实现两个字符串的对比及差异高亮显示的代码方案。

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Review code changes from the current branch, staged changes, a git ref, or a diff/patch file. Pure git-native — no platform/PR awareness. Trigger when user says "review this", "review diff", "/aiwork:code-review-diff", or provides a git ref or diff path. Read-only — never posts comments.

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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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Converts a markdown PR writeup or code review (one with ```diff fenced blocks and severity-tagged > [!BLOCKER]/[!MAJOR]/[!MINOR]/[!NIT] callouts) into a single-file 2-column HTML review — unified-diff on the left, severity-tagged annotation cards on the right, top jump-nav listing every finding, mandatory named reviewer footer. Triggers when the markdown-html-orchestrator classifies an input as REVIEW, or when invoked directly via /cs:md-review. Refuses without explicit --reviewer (a code review must name a human), refuses if no diff hunks present (route to md-document instead), and refuses to encode severity in color only (every badge ships color + icon + aria-label per WCAG 1.4.1). Use after orchestrator routing.

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Hunt shadow / zombie / undocumented API surface (OWASP API9 Improper Inventory Management) — enumerate the full API version history (v1/v2/beta/legacy paths, header- and subdomain-based versioning), pull and diff every reachable OpenAPI/Swagger spec (including ones only findable via the Wayback Machine), and behaviorally diff old vs. current versions for auth/rate-limit/validation regressions rather than just response-shape differences. Distinct from hunt-api-misconfig, which owns exploitation once you have a spec or endpoint (mass assignment, JWT, OData, Swagger-chain attacks); distinct from hunt-subdomain, which owns host-level discovery. This skill owns the version-inventory and behavioral-diff workflow itself. Use when the target has versioned API paths, multiple specs, a changelog referencing deprecated endpoints, or a mobile app whose hardcoded backend calls look older than the current web app's.

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This skill covers causal inference methods in observational and quasi-experimental settings. Use when the user is implementing, choosing between, or debugging causal identification strategies — including instrumental variables, difference-in-differences, regression discontinuity, synthetic control, or matching estimators. Triggers on "causal effect", "identification strategy", "instrumental variable", "2SLS", "GMM", "difference-in-differences", "DiD", "staggered treatment", "regression discontinuity", "RDD", "synthetic control", "matching", "propensity score", "IPW", "AIPW", "doubly robust", "LATE", "ATT", "ATE", "parallel trends", "exclusion restriction", "first stage", "weak instruments", or "endogeneity".

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。