3.7K skills · sorted by relevance
Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
Review code changes and files for bugs, security risks, performance problems, design issues, and test coverage. Receive a structured report with prioritized findings, concrete improvement examples, positive feedback, and an overall review verdict.
Help an agent periodically review whether its work stays on task, remains thorough, avoids repetition, respects earlier decisions, and covers every item. Record assessments and revisit working notes when quality declines so the approach can be adjusted.
Plan or run quality checks and peak calling, which identifies enriched genomic regions, for ChIP-seq, CUT&RUN, and CUT&Tag. The workflow accounts for controls and replicates and prepares signal tracks, quality reports, and differential binding designs.
Assess whether data is trustworthy enough for analysis, dashboards, experiments, or modeling. Check missing values, duplicates, consistency, freshness, and changes over time, then explain the evidence, downstream risks, likely causes, and practical fixes.
Review web code for security, browser compatibility, and code quality. Covers HTTPS, dependency vulnerabilities, security headers, outdated APIs, HTML structure, error handling, and cleanup of event listeners, with examples and audit checklists to guide improvements.
Inspect production AI agents in FailproofAI Cloud using the fp CLI. Review errors, run histories, quality scores, and usage, then manage issues, audits, user access, and scoped API keys for the team's deployment.
Check code for complex logic, duplication, naming issues, and oversized functions. Get a quality report with file locations and severity levels, plus guidance on splitting complex code and extracting shared logic during reviews or after refactoring.
Inspect scientific data files to understand their format, structure, and quality. Covering over 200 formats, this skill produces Markdown reports with statistical summaries, potential issues, and recommendations for preprocessing, visualization, and further analysis.
Compare original and rewritten academic text to check that citations, statistics, and core claims remain intact. Receive a report covering writing quality, scholarly tone, and whether the changes should be approved, reviewed, or rejected.
Review academic drafts for exaggerated claims, vague attribution, repetitive wording, and other style issues. Produces prioritized findings, section-level assessments, and revision recommendations, with additional checks for LaTeX syntax problems that can disrupt document export.
Check a paper's numerical claims against saved analysis outputs before submission. Receive a report identifying verified figures, unsupported claims, missing files, unreferenced outputs, and citation keys absent from the bibliography, without changing the manuscript.
Review existing R scripts for code quality, reproducibility, domain correctness, and coding standards. Produce a separate report for each script, plus a severity-based summary of findings and the three most critical issues, without changing source files.
Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.
Build a software quality assurance process with structured test cases, execution tracking, bug severity levels, and quality metrics. Includes templates for progress reports and team handoffs, plus prompts for agent-driven test execution.
Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
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[omh] Deliverable demands premium polish: enforce superior content, design, layout, publishing, and visual QA gates. Use when the user says: design-quality-gate, design quality gate, ui ux pro max, design pro max, frontend pro max, visual qa pro, premium design, high quality design.
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Run an extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Use for a thermo-nuclear code quality review, thermonuclear review, deep code quality audit, or especially harsh maintainability review.
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Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why is it slow", "reduce latency", "set up observability", "CloudWatch dashboard", "how much does my agent cost", "cost optimization", "logs not showing up", "logs missing", "spans not found", "eval failing", "eval error", "dev traces", "local traces", "agentcore dev traces", "traces to CloudWatch". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.
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Diagnostic-only test suite auditor. Classifies repo type, maps against 7-layer testing taxonomy (git hooks → static → unit → integration → system → E2E → acceptance), runs deterministic quality gates (coverage, mutation, CRAP, architecture, escape-scan), builds RTM / personas / journeys traceability, produces TEST_AUDIT.md, updates tests/TESTING.md, and mandatorily hands off to implement-tests when gaps are found. Use when auditing test quality, finding test gaps, or running the full 7-layer sweep. Trigger with "audit tests", "find gaps", "test audit", "check test quality", "full sweep", "7-layer audit", "rtm check".
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Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase renders wrong (blank cells, garbled numbers, an unsortable date column) and the cause needs isolating.
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This skill should be used when comparing two videos to analyze compression results or quality differences. Generates interactive HTML reports with quality metrics (PSNR, SSIM) and frame-by-frame visual comparisons. Triggers when users mention "compare videos", "video quality", "compression analysis", "before/after compression", or request quality assessment of compressed videos.
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Check live air quality anywhere with zero API keys — Open-Meteo's air-quality API via curl, decoded from raw PM2.5 and AQI numbers into what they mean for going outside. Use when asked what's the air quality, is it safe to run outside, AQI in my city, or pollution levels right now. Produces the current AQI and pollutant levels, the plain-language health read with the standard bands, and the rerunnable command.
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