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How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metrics, network effects, dashboard reconciliation, and the interpretation failures that produce confidently wrong shipping decisions. Use this skill whenever the user is reading a finished experiment result panel and about to make a ship, kill, or iterate decision, or when an experiment number does not match the dashboard number. Triggers on read experiment results, result panel, ship or kill decision, p-value, confidence interval, statistical significance, multiple testing, peeking, sequential testing, CUPED, variance reduction, heterogeneous treatment effects, ratio metric, network effects, inconclusive test, experiment versus dashboard mismatch. Use `experiment-design` instead when the test has not run yet and the question is hypothesis, sample size, duration, or what to test.

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Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

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Expert Section 508 compliance advisor for US federal ICT accessibility. Use this skill whenever a user asks about Section 508, WCAG 2.0/2.1 AA for federal systems, VPAT or Accessibility Conformance Reports (ACR), accessibility audits, remediation planning, PDF accessibility, web or software accessibility, mobile accessibility, federal procurement accessibility requirements, contractor obligations, undue burden exceptions, assistive technology compatibility, or Section 508 testing. Covers the Revised Section 508 Standards (2018), all WCAG 2.0 Level AA success criteria, the four POUR principles, testing methodologies, and agency compliance workflows. Trigger even if the user doesn't say "skill" — any Section 508 or ICT accessibility question for federal systems should use this skill.

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Multi-style article creation skill. Supports 5 writing styles (deep analysis, practical guide, story-driven, opinion, news brief), including complete workflow: material collection → outline → content → formatting. Activated when users mention "write article", "write post", "create", or "draft".

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Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with render_game_to_text.

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Grade a ForgeCAD or CAD-as-code model against a requirement, brief, prompt, reference, or acceptance criteria with evidence and a 0-10 score.

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Trace bugs and manual fixes back to kits and prompts; fix at the source so the iteration loop can reproduce the fix autonomously. Six-step revision process plus the single-failure backpropagation protocol. Use when a manual hot-fix has been applied, when convergence stalls, or when the same class of bug keeps reappearing.

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Search PubMed biomedical literature database

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Hypothesis generation from scientific literature and data patterns

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Conducts structured requirements workshops to produce feature specifications, user stories, EARS-format functional requirements, acceptance criteria, and implementation checklists.

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Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.

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SOC 2 Type I and Type II compliance management against the Trust Services Criteria. Use for SOC 2 readiness assessments, TSC gap analysis, audit evidence collection, infrastructure control validation, and CPA firm audit preparation.

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Agile product ownership for backlog management and sprint execution. Use for writing user stories and acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing a backlog.

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GDPR Art. 35 Data Protection Impact Assessment with threshold checking, risk registers, and EDPB criteria scoring. Use for DPIA evaluations.

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Run development from an executable specification with traceable requirement IDs and merge-time coverage gates. Use when starting a greenfield feature, reviving a stale spec, or gating merges on requirement coverage.

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Generate business documents — contracts, proposals, SOWs, NDAs, MSAs — with jurisdiction-aware clauses for US, EU, UK, and DACH. Use when starting client engagements, writing proposals, drafting agreements, or needing GDPR-compliant DPAs.

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Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when Codex is asked to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.

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Implement Terraform Provider resources and data sources using the Plugin Framework: CRUD operations, schema design, plan modifiers and validators, not-found handling, waiters for eventually consistent APIs, import support, resource design principles, and required acceptance test coverage. Use when adding or changing a resource or data source, deciding whether an API concept should be a resource, wiring a resource to the provider's configured client, handling drift or resource-not-found, or reviewing a resource implementation before submission.

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Write an influencer campaign brief: creator criteria, pay, approvals, disclosure. "write a brief for our influencer campaign"

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Use when the user wants help with academic papers or citations but it's unclear which specific workflow fits — reviewing a paper, checking a BibTeX file for fake references, or benchmarking multiple LLMs on reference-recommendation accuracy. Also use when the user mentions paper review, peer review, BibTeX verification, citation checking, reference hallucination, or academic literature accuracy and hasn't specified which of those three tasks they mean. This skill is the entry router for the academic-eval suite: it asks one diagnostic question then routes to the right sub-skill.

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Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.

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。

Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.

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Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.

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After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.

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。