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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Configures and enforces SwiftLint in Swift projects using build tool plugins, run scripts, and CI. Covers .swiftlint.yml configuration, disabled_rules, opt_in_rules, only_rules, analyzer_rules, baselines, autocorrect, swiftlint:disable suppressions, reporter formats (sarif, json, checkstyle), strict and lenient modes, SwiftLintBuildToolPlugin via SimplyDanny/SwiftLintPlugins, swift package plugin swiftlint, Xcode run script phases, CI integration, multiple configuration files, and rollout strategies for existing codebases. Use when setting up SwiftLint, configuring lint rules, suppressing warnings, creating baselines, choosing between build tool plugin and run script, or integrating SwiftLint into CI.

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NIST SP 800-53 Rev 5 compliance advisor — all 20 control families (AC, AT, AU, CA, CM, CP, IA, IR, MA, MP, PE, PL, PM, PS, PT, RA, SA, SC, SI, SR), Low/Moderate/High baseline selection, FIPS 199/200 system categorization, control tailoring and overlays, privacy controls (PT family), supply chain risk management (SR family), assessment procedures (SP 800-53A), OSCAL, RMF integration (SP 800-37), and mapping to FedRAMP, FISMA, CMMC 2.0, and ISO 27001. Use for any federal system security controls, FISMA compliance, RMF step guidance, control narrative writing, or baseline tailoring question.

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Set up and run rank tracking using Ahrefs MCP: pick the right keywords to track, segment them by purpose, set baselines, and define alert thresholds. Use this skill when starting a new tracking project, baselining for a campaign, choosing what to monitor, or building a rank reporting cadence. Triggers on rank tracking, keyword tracking, monitor rankings, track positions, what should we track, ranking dashboard, baseline rankings, alert thresholds. Also triggers when a stakeholder wants weekly or monthly ranking visibility.

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。

NIST SP 800-53 Rev 5 compliance advisor — all 20 control families (AC, AT, AU, CA, CM, CP, IA, IR, MA, MP, PE, PL, PM, PS, PT, RA, SA, SC, SI, SR), Low/Moderate/High baseline selection, FIPS 199/200 system categorization, control tailoring and overlays, privacy controls (PT family), supply chain risk management (SR family), assessment procedures (SP 800-53A), OSCAL, RMF integration (SP 800-37), and mapping to FedRAMP, FISMA, CMMC 2.0, and ISO 27001. Use for any federal system security controls, FISMA compliance, RMF step guidance, control narrative writing, or baseline tailoring question.

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。

Establish MSBuild/.NET build performance baselines before optimizing. USE FOR: a .NET build or solution that is slow or has build-performance concerns; cold, warm, incremental, or no-op measurements; before/after comparisons; CI build output caching; static graph build decisions; artifacts output; and dependency graph trimming. Start here before build-perf-diagnostics, incremental-build, or build-parallelism. DO NOT USE for webpack, npm, JavaScript bundlers, or other non-MSBuild build systems. For detailed target/task/analyzer bottleneck analysis after baselining, use build-perf-diagnostics.

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Add, repair, or review Playwright visual regression tests for browser-facing .NET apps, including screenshot baselines, Pixelmatch thresholds, deterministic rendering, and GitHub Actions artifacts. USE FOR: toHaveScreenshot, page.screenshot visual checks, Pixelmatch/pngjs comparison scripts, visual baseline updates, screenshot diff triage, or CI workflows for UI regression screenshots. DO NOT USE FOR: pure unit tests, accessibility audits, browser-debugging sessions, or frontend linting.

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System hardening, detection engineering, security baseline monitoring, patch management, defense-in-depth architecture, and security posture improvement

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 the user wants to design experiments, plan ablation studies, structure baselines, or create incremental evaluation strategies. Triggers on phrases like "design ablation", "plan experiment", "what experiments should I run", "baseline comparison", or "experiment matrix".

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Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run performance.

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OI-3 spike harness — heavy consumer skill, BASELINE arm. Builds an incident post-mortem and sources the readability standard mid-workflow by reading a file directly, with NO Skill-tool call. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.

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Build DSPy 3.2.x programs through spec, program, metric and baseline; extend to optimization and export when requested and justified by task budget. Orchestrates the other four DSPy skills (dspy-fundamentals, dspy-evaluation-harness, dspy-gepa-optimizer, dspy-rlm-module) in the correct order. Use for greenfield DSPy builds; prototypes may stop at a validated baseline.

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Standardize a 2D animation baseline image with `/img2mo-std xxx.png/pos`. Use when preparing a character, creature, prop, or weapon baseline before motion generation, especially if the source image is too large, tightly cropped, lacks transparent action margin, has a white background, or later walk/attack/idle frames show clipping, overlap, scale popping, or inconsistent character size.

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Think and work like an expert Environmental Policy Analyst. Use when a task calls for Environmental Policy Analyst judgment. Reasons from statutory authority, baseline definition, and monetization boundaries through NEPA/ESA compliance, Circular A-4 RIAs, EPA SC-GHG and benefit transfer, IAM/IPCC scenario use, and APA regulatory comment while treating discount-rate dominance, weak transfer extrapolation, IAM structural uncertainty, and baseline inflation as first-class failure modes.

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。

Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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。

Build the deterministic evidence ledger (artifact_manifest.json + claims.json) that every other Anti-Autoresearch auditor reads. One pass inventories artifacts, derives the observability level (L0 PDF-only / L1 +LaTeX / L2 +repo+results) by fixed rule, and extracts span-anchored, hashed, checkable claims (numbers, comparisons, scope, method, baselines, citations, captions, table cells) into claims.json. An OPTIONAL additive cross-model pass ADDS span-anchored semantic claims — method, theorem statements with their assumptions, definitions, proof/derivation steps and equations, scope, baselines, conclusions, the motivation span, and reproducibility-artifact references (the proof, derivation, and structure anchors the family B/D/G auditors need) — it never invents a number, emits a finding, or computes a verdict. Run FIRST, before any audit skill. Triggers: "build the ledger", "extract claims", "prep for integrity audit", "evidence ledger", "建证据账本".

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。

Scaffold-time observability so a shipped product is not blind in prod from day one — error capture (Sentry), request-id structured logging, and /healthz + /readyz endpoints. stack-baseline pins Sentry but nothing wires it; this is the wiring. Loaded by app-scaffolder (bake into the scaffold), infra-provisioner (prod env + probes), and consumed by l3-support (traces) and devops (deploy gate).

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。

Internal Harness instruction source for baseline-repro. Route through visible Harness aliases instead of invoking directly.

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。

Audit existing performance testing — find missing benchmarks, stale baselines, and CI gaps. Use when asked "do we have benchmarks", "audit our performance testing", or "are our baselines stale".

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Set up and maintain Hierarchical Agent Memory (HAM) for Codex using scoped local memory files. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "HAM audit".

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Set up Hierarchical Agent Memory (HAM) — scoped CLAUDE.md files per directory that reduce token spend. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "ham sync".

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。

Execute structured acceptance checks and record their observed results for later comparison. Use to capture a pre-change baseline or a post-change verification state without treating expected nonzero baseline results as execution 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。

Scan a repository for secrets, keep an auditable baseline, and review only newly introduced findings during commits or CI checks.

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Use when benchmarking, profiling, or A/B-testing CUDA kernels or end-to-end perf in the imp inference engine on RTX 5090 (sm_120), including refreshing tests/perf_baseline.json or publishing numbers to docs/BENCHMARKS.md and the README. Triggers on "benchmark kernel", "profile cuda", "ncu", "nsys", "kernel timing", "kernel sum", "occupancy", "bandwidth bound", "compute bound", "roofline", "perf baseline", "is this regression real", "decode dropped", "aggregate throughput", "two-image A/B", "prefill kernel A/B". Do NOT use for writing/optimizing kernel code (sm120-cuda-expert) or output-quality checks (check-degeneration).

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。