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Expert guide for MCP client sampling (sampling/createMessage), Critic-in-the-Loop agentic quality gates, autonomous human-in-the-loop emulation, and multi-model consensus verification / Panduan ahli sampling MCP client, quality gate agen Critic-in-the-Loop, dan verifikasi konsensus multi-model.
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Use on any core for first-run planning intake — Phase 0 runs the vendored BMAD-METHOD planning shelf, shared by every core, and Phase 1 (mining `04-prd.md` into intent records plus the Add-ons/sync-and-remote-entities decision) is full-stack-app's and pwa-app's shared procedure — identical mechanics, a different decision at step 5/8. Phase 0 also defines compressed intake, the path a user's explicit "just build it" choice takes on full-stack-app, pwa-app, authored, and copywriting cores: one batched round of questions in place of the shelf, writing a thinned archive at the same paths so Phase 1 (or, on copywriting, that core's own mining step), `ux-planner`, and the Re-entry pass all keep their documented source. Also use for the Re-entry pass, which mines new scope into additional intents without re-running the shelf, on any core with a module axis to add an intent to (full-stack-app, pwa-app, authored) — landing-page has none, so its own new-scope path runs through `hedgehog-landing-loop`'s Correction Protocol instead. Invoked by the `planner` agent, which decides the path; don't run standalone. landing-page runs this skill's Phase 0 on first run, then mines the same archive through `hedgehog-landing-loop`'s own planning-intake section, that core's counterpart to this skill's Phase 1. copywriting runs this skill's Phase 0 the same way (full or compressed), then mines the same archive through `hedgehog-copywriting-loop`'s own planning-intake section into a what/audience/register brief — no module axis, so its own new-scope path runs through that loop's Correction Protocol rather than the Re-entry pass below. An authored core runs this skill's Phase 0, then `hedgehog-core-design`, then this skill's Phase 1 mining against the designed layer sequence. A brownfield adoption (`hedgehog-adopt`) never runs this skill's shelf at all — the drivers BMAD elicits are already settled facts of a repo that already exists.
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8つの専門エージェント（市場規模分析・トレンド調査・競合インテリジェンス・投資シグナル・日本市場・ユーザー需要分析・規制調査・データ検証）が ディスカッション型でアプリ/デジタルプロダクトの市場調査を実施するスキル。 エージェント同士が対話・批判し合い、data-critic がライブモデレーターとして矛盾を検出・収束を判定する。 トライアンギュレーション（データ・方法論・調査者・理論の4種類）をディスカッションに組み込み、 確証バイアスを排除した信頼度付きレポートを出力する。 Gate 1（全員参加の合意形成）→ Gate 2（最終検証 + Devil's Advocate）の2段階ゲートで品質を担保。 コンテキストファイルで過去のセッションを引き継げる。 Use when: 市場調査をしたい、TAM/SAM/SOMを算出したい、市場の成長性を調べたい、 参入タイミングを判断したい、競合市場を分析したい、規制リスクを調べたい。 Triggers: "市場調査", "market research", "TAM", "SAM", "SOM", "市場規模", "市場分析", "market analysis", "参入", "market entry", "市場性", "market viability", "市場トレンド", "market trend"
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Analiza textos aplicando pensamiento crítico — el marco de los 8 elementos del pensamiento y los estándares intelectuales universales de Richard Paul y Linda Elder — y detecta las falacias lógicas graves presentes. Úsala siempre que el usuario comparta un texto (pegado o en un archivo) y pida analizarlo críticamente, detectar falacias, evaluar argumentos, identificar sesgos, o preguntas como "¿qué problemas tiene este argumento?", "¿es sólido este razonamiento?", "analiza esto", "dame una crítica". También actívala cuando el usuario comparta un artículo de opinión, un post, un discurso, un ensayo o cualquier texto argumentativo y quiera evaluar su calidad argumentativa.
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Establishes safe practices for acquiring, storing, transferring, and disposing of malware samples: password-protected archives, neutralized extensions, hashing for identity, and chain-of-custody. Activates for requests about safely storing or sharing malware, sample handling hygiene, or defanging artifacts.
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Use when a task needs the judgment of an Environmental Engineering Technologist/Technician — verifying field meter calibration before a sampling event, computing a flow-proportional composite sampling program, checking chain-of-custody entries against 40 CFR 136 holding times, running an air-monitoring isokinetic sampling check, or deciding whether a field QC exception invalidates a sample.
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Compare ENCODE experiments across different biosamples, tissues, or cell lines to identify tissue-specific regulatory patterns. Use when the user wants cross-tissue comparison, cell-type comparison, tissue-specific elements, differential chromatin, biosample matching, disease vs normal comparison, developmental time course, constitutive vs variable regulation, or multi-tissue data availability mapping. Handles batch effect detection, biosample hierarchy, and comparison design.
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Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, normalization, integration strategy selection, and provenance documentation.
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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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Use this skill when an auditor will test change management, or the team wants to know before they do, and asks "sample our production changes", "check our PR approvals", "do we have segregation of duties on deploys", "test change management before the audit", "were emergency changes approved" or "can a developer push to production alone". Builds the change population from what actually reached production, reconciles it with tickets and pull requests, draws a recorded sample, tests approval, testing, segregation and emergency attributes, and writes the results and the exceptions as facts.
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Use when working with Aws Sam — aWS Serverless Application Model (SAM) management. Covers template validation, local testing, build and packaging, deployment orchestration, stack inspection, log tailing, and API Gateway debugging. Use when building serverless applications with SAM, testing locally, deploying Lambda functions, or troubleshooting API Gateway endpoints.
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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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Use psamvault MCP server: credential vault, browser login, API key injection, .env secret protection. MCP tools keep secrets out of the agent's context window.
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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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An example skill loaded through resources_discover and supplied by the dynamic-resources extension. The material identifies its role as a sample but does not describe a specific task, workflow, or output.
Create and run automated tests that score LLM answers against expected outputs or judging criteria. Monitor progress, inspect individual results, compare models on the same tests, and use score thresholds in CI, an automated checking workflow.
Review author information in structured data, a machine-readable description of page content. Check names, profile links, and connections to articles or pages, then identify corrections using the rendered HTML and HTTP response.
Review local character animations before sharing previews or final renders. Check structured design data, asset files, browser errors, sampled motion frames, and MP4 metadata, then classify the result as pass, revise, or fail.