146 件 ・ 関連度順
概要と使いどころ
Targeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment. Use when quantifying specific metabolites using calibration curves and internal standards.
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Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.
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。
Full-site blog health assessment scanning all blog files for quality scores, orphan pages, topic cannibalization, stale content, and AI citation readiness. Runs canonical batch analysis before site-wide checks. Produces per-post scores and a prioritized action queue. Use when user says "audit blog", "blog audit", "site audit", "blog health", "audit all posts", "check all blogs".
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Conduct professional peer reviews for papers or theses, providing structured evaluations and improvement suggestions; use when you need a pre-submission assessment, an internal review, or academic quality control.
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。
Generates the Methods section for a meta-analysis paper, including search strategy, screening, quality assessment, data extraction, and statistical analysis.
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。
Structured code quality assessment with Conventional Comments format, scaled review depth, and soft-gating verdicts preserving user autonomy.
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。
Multi-dimensional code assessment across security, quality, performance, and maintainability with confidence-gated reporting (>=80%) and Router Contract generation.
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。
Tech debt assessment that identifies, classifies, and prioritizes maintainability problems — helping teams build a refactoring roadmap — drawing on twelve classic engineering books. Triggers when: user asks about tech debt, refactoring priorities, what to clean up first, or asks "why is this so hard to change?", "what should we fix first?", or "how do I justify refactoring to management?". Do NOT trigger for: server health checks, HTTP /health endpoints, Kubernetes probes, database health, or application uptime — "health" in those contexts is infrastructure, not code quality. Also not for single-function refactoring questions.
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。
Sanity-check a greenhouse gas inventory before it goes into a report or gets audited. Use when asked to review a carbon footprint, check a GHG inventory, validate scope 1/2/3 numbers, explain a year-over-year emissions change, or prepare emissions data for assurance. Produces a boundary review, data-quality assessment, emission-factor sensitivity list, YoY bridge, and a fix-before-publishing list.
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Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with academic-paper-composer skill).
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Detects and removes contamination in genome assemblies via two disjoint workflows - foreign-sequence screening of a single-organism (eukaryote/isolate) assembly with NCBI FCS-GX (GenBank-submission-mandatory), FCS-adaptor, and BlobToolKit blob plots; and MAG/bin quality assessment with CheckM2 plus GUNC (chimerism) plus GTDB-Tk taxonomy, judged against MIMAG. Covers why CheckM2 alone is blind to disjoint-marker chimeras, the FCS-GX RAM wall, organelle/NUMT triage, strain heterogeneity, and the HGT-vs-contamination (tardigrade) trap. Use when screening an assembly for foreign contamination before GenBank submission, assessing MAG completeness/contamination/chimerism, deciding which contigs to remove, or distinguishing real HGT from contaminant contigs.
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Load this first for any task involving images, pictures, photos, scans, frames, volumes, or visual data — including reading, writing, filtering, enhancing, denoising, sharpening, deblurring, thresholding, segmenting, masking, detecting, finding or counting objects, removing backgrounds, measuring, comparing, registering, stitching, aligning, cropping, resizing, transforming, or analyzing. Use for image I/O (TIFF, PNG, JPEG, RAW, HDR), histograms, edge detection, color processing, morphology, geometric transforms, deep learning for images, quality assessment, and 3-D volume processing. Also triggers when users describe image problems (dark, noisy, blurry, low contrast, uneven lighting, hazy, color cast) or data exploration tasks (folder of images, characterize data, understand format). Handles microscopy (cells, particles) and general scientific imaging. Routes to sibling skills for medical imaging (DICOM, NIfTI), hyperspectral data, and visual inspection.
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This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, an
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Review dimensions and scoring for root cause analysis quality assessment
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Requirements quality critique dimensions for peer review - confirmation bias detection, completeness validation, clarity checks, testability assessment, and priority validation
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Use when the user asks for a visual or UI quality review of frontend code that already shipped, or passes a phase number. Produces {phase_num}-UI-REVIEW.md with a graded 1-4 assessment across six pillars.
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Audits existing tests in any language using formal, research-backed test smell names and the testsmells.org 19-smell academic taxonomy. Use when the caller asks for an academic or citable test-smell review, named smell categories, or a formal severity-ranked smell assessment. Covers Assertion Roulette, Conditional Test Logic, Mystery Guest, Eager Test, Sleepy Test, Unknown Test, Sensitive Equality, and the rest of the catalog across .NET, Python, JavaScript/TypeScript, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, and C++. DO NOT USE FOR a quick pragmatic test review (use test-anti-patterns), writing or running tests, framework migration, coverage, or assertion-diversity metrics.
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Evaluating scientific evidence and claims. Covers study design hierarchy (RCT to expert opinion), effect sizes (OR, RR, NNT, Cohen's d), confounding, p-value vs clinical significance, GRADE quality assessment, reproducibility, and bias types (selection, information, confounding, reporting). Use when reading a paper or assessing claims.
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。
Structured peer review of manuscripts and grants. 7-stage evaluation: initial assessment, section review, statistical rigor, reproducibility, figure integrity, ethics, writing. Covers CONSORT/STROBE/PRISMA and report structure. For evidence quality see scientific-critical-thinking; scoring see scholar-evaluation.
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。
Methodology for exploratory data analysis on scientific files. Decision frameworks by data type (tabular, sequence, image, spectral, structural, omics), quality assessment, report generation, format detection across 200+ formats. Use when given a data file for initial exploration or to pick an analysis before a pipeline.
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Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
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This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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。
Produce a standalone test plan by analyzing code for test coverage gaps and edge cases. Use when you need to create, generate, or draft a test plan for a branch, need to analyze test coverage, or need to identify what tests to write for specific files or directories. Does not produce a plain-language plan for a person to run tests by hand — use manual-test-planning for that. Does not write test code — use tdd to implement behavior test-first. Does not refine existing plans — use iterative-plan-review. Does not review code quality, security, or style — use code-review for full code review. Does not evaluate architectural testability or structural coupling — use architectural-analysis for architectural assessment.
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。
SEO optimization with keyword analysis, readability assessment, technical validation, content quality. Use for search rankings, blog posts, content audits, or encountering keyword density, readability scores, meta tags, schema markup errors.
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。