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Claude Skills by mcorbett51090
github.com/mcorbett51090964 skills6 installs216 views
- Design Accessible PatternDesign an accessible-by-default component pattern, semantic HTML first and ARIA only where needed. Reach for this on a design-system or component question.Votes: 0GitHub stars: 7
- Prioritize RemediationRank audit issues by user-impact and effort into a sequenced remediation plan with owners. Reach for this when there are more fixes than time.Votes: 0GitHub stars: 7
- Run Wcag AuditAudit a page set against a named WCAG version and level, classify issues by severity/level, and compute a weighted conformance score. Reach for this on a conformance question.Votes: 0GitHub stars: 7
- Test Assistive TechVerify keyboard operability and screen-reader parity hands-on with the assistive technology real users use. Reach for this on a parity question.Votes: 0GitHub stars: 7
- Verify ContrastCompute the WCAG contrast ratio from hex foreground/background values and check AA/AAA for normal and large text. Reach for this on any color question.Votes: 0GitHub stars: 7
- Audit ControlsAudit segregation of duties and chart-of-accounts hygiene before trusting the books. Reach for this on a controls or data-quality question.Votes: 0GitHub stars: 7
- Estimate Bad DebtEstimate bad-debt from AR aging buckets weighted by loss rate. Reach for this on a receivables-risk question.Votes: 0GitHub stars: 7
- Read Working CapitalRead the cash conversion cycle (DSO + DIO − DPO) and locate trapped or surrendered cash. Reach for this on a cash question.Votes: 0GitHub stars: 7
- Reconcile AccountsReconcile bank and balance-sheet accounts to source before any statement ships. Reach for this first on any reporting question.Votes: 0GitHub stars: 7
- Run CloseRun the period-end close on a cadence: critical-path checklist, days-to-close, bottleneck. Reach for this on a close question.Votes: 0GitHub stars: 7
- Design Agent Tools And ContextDesign the tools/functions an agent calls and the context/memory strategy that keeps it coherent — unambiguous tool names, typed parameters, examples-in-description, errors that teach recovery, a small-enough tool count, plus a context plan (what stays in-window, what gets summarized, what moves to external memory) and short-term-vs-long-term memory design under a per-turn token budget. Reach for this when the user says 'the agent keeps calling tools wrong', 'the context overflows', or 'the a...Votes: 0GitHub stars: 7
- Evaluate And Harden AgentEvaluate an agent and harden its failure modes before it touches real traffic — an offline eval harness scored on task-completion, trajectory, and tool-use correctness against a fixed task set, plus loop hardening (step/tool-call caps, timeouts + retries, stop conditions, human-in-the-loop on irreversible actions) and tracing so every step/tool-call/token-cost is observable, with cost and latency reported alongside quality. Reach for this when the user asks 'how do I know this agent works?', ...Votes: 0GitHub stars: 7
- Triage Agentic ApproachDecide whether a task should be an agent at all — and if so, single-agent vs multi-agent, the orchestration topology, and the framework — by traversing the agentic decision tree (agent-vs-workflow gate → single-vs-multi → topology → framework), returning a go/no-go verdict that defaults to 'a fixed workflow or a single LLM call wins' unless the control flow is genuinely unknowable in advance. Reach for this when the user asks 'should we build an agent for this?', 'do we need multiple agents?'...Votes: 0GitHub stars: 7
- Codex Reasoning Level CalibrationCalibrate the OpenAI Codex reasoning-level dial before recommending a model upgrade. Maps task type, failure mode, and budget to the right reasoning effort level — ensuring developers exhaust the reasoning dial on the current model before paying for a bigger SKU. Domain-specific to the Codex reasoning API.Votes: 0GitHub stars: 7
- Coding Agent Task ScopingScope an autonomous AI coding agent task so it is well-bounded, recoverable, and matched to the right model tier before it runs. Reach for this skill before any long, unsupervised, or multi-step agentic run — a poorly scoped task on a frontier model is both expensive and hard to debug.Votes: 0GitHub stars: 7
- Context Window PlanningEstimate a coding task's context demand and match it to a model tier whose window is sufficient, without quoting specific token counts that churn monthly. Reach for this skill when the task involves large codebases, long conversation histories, or multi-file agentic runs where context overflow would produce silent truncation errors.Votes: 0GitHub stars: 7
- Copilot Surface AuditAudit a developer's GitHub Copilot configuration across all surfaces (completions, chat IDE, coding agent, cloud agent, mobile) to identify model gaps, plan mismatches, and org-policy constraints. Reach for this skill before recommending a Copilot model — surface and plan scope the answer, not just the model name.Votes: 0GitHub stars: 7
- Grok Model Retirement CheckCheck whether a Grok model ID in active use has been retired or silently redirected, with special attention to billing consequences. Reach for this skill before any Grok recommendation and whenever a developer mentions a specific Grok model ID — retirement redirects can incur unexpected charges at the new model's pricing.Votes: 0GitHub stars: 7
- Lineup Freshness SweepCheck the ai-coding-model-guidance knowledge bank for stale entries and produce a prioritized refresh list. Reach for this skill when the knowledge bank's retrieval date is more than 4 weeks old, when a consumer reports a model discrepancy, or on the monthly researcher-reminder cadence.Votes: 0GitHub stars: 7
- Multi Tool Model ComparisonCompare model options across GitHub Copilot, OpenAI Codex, and xAI Grok for a single task when the developer has not committed to one ecosystem. Produces a structured side-by-side that surfaces availability, tier mapping, and key tradeoffs without naming specific volatile numbers.Votes: 0GitHub stars: 7
- Org Policy Model Rules AuditAudit an organization's AI coding tool model-access policies across GitHub Copilot Business/Enterprise, OpenAI org-level controls, and xAI API governance. Reach for this skill when an enterprise team reports unexpected model access, when a compliance review requires documenting which models the org has allowed or blocked, or before rolling out a new model to a large org.Votes: 0GitHub stars: 7
- Quota Exhaustion FailoverHard gate when a coding agent/surface returns quota, rate-limit, weekly/monthly cap, spend-limit, or tokens-exhausted. Never silent-fail or blind-retry — try param/effort/scope levers before vendor failover, then traverse the vendor-neutral tier tree before naming a substitute SKU. Deep layer: coding-agent-levers-playbook.Votes: 0GitHub stars: 7
- Budget TokensCompute cost per request and right-size the context to fewest-high-precision chunks. Reach for this on a cost/context question.Votes: 0GitHub stars: 7
- Build Rag EvalBuild a judgment set and measure recall@k, precision@k, faithfulness, and answer-relevance with a baseline. Reach for this before shipping any change.Votes: 0GitHub stars: 7
- Diagnose RetrievalSeparate retrieval failure from generation failure by measuring recall@k before touching the model. Reach for this first on wrong answers.Votes: 0GitHub stars: 7
- Ground And GuardrailAdd citations, refuse-on-empty-retrieval, and context-constraint to cut hallucination. Reach for this on a faithfulness question.Votes: 0GitHub stars: 7
- Tune ChunkingTune chunk size, overlap, and structure-awareness against the eval and the context budget. Reach for this on a chunking question.Votes: 0GitHub stars: 7
- Design Ai Redteam PlanScope an AI red-team engagement by threat-modeling the system (assets, attackers, trust boundaries), splitting safety from security, traversing the attack-taxonomy decision tree to a prioritized OWASP LLM Top 10 / MITRE ATLAS attack list, and setting the rules of engagement plus likelihood×impact success and severity criteria. Reach for this when the user asks "how should we red-team this LLM feature?", "what should we attack first?", "is this a safety or a security problem?", or "what are th...Votes: 0GitHub stars: 7
- Harden And Remediate Ai SystemTriage red-team findings by likelihood×impact and drive defense-in-depth remediation — layered input/output guardrails, injection-resistant prompt structure, least-privilege tool scoping, allow-lists, human-in-the-loop on high-impact actions, output-handling hygiene, and rate/cost limits — then retest each fix with the exact attack that found it and bake it into the regression harness. Reach for this when the user asks "we have a pile of red-team findings — what do we fix and how?", "harden o...Votes: 0GitHub stars: 7
- Run Adversarial Attacks And JailbreaksExecute the prioritized attacks against an AI system within the rules of engagement — direct and indirect prompt injection, jailbreaks (roleplay, encoding, many-shot, crescendo), training-data extraction and data exfiltration, agentic tool-abuse / excessive agency, and multimodal attacks — capturing each as a reproducible payload plus transcript, then automating what repeats into a PyRIT / Garak / Promptfoo red-team / Giskard harness with a scorer and a CI regression gate. Reach for this when...Votes: 0GitHub stars: 7
- Data Quality TestingKeep the warehouse trustworthy: dbt tests (not_null/unique/accepted_values/relationships) gating the build in CI, source-freshness checks, model contracts at consumer boundaries, singular tests for business invariants, and anomaly detection beyond schema tests.Votes: 0GitHub stars: 7
- Dbt Ci GovernanceDesign a dbt CI pipeline that gates every pull request: compile, run, test, and check source freshness in an isolated developer schema; enforce model contracts on published marts; run slim CI on changed models only using dbt state comparison; and block merges on test failures or contract violations.Votes: 0GitHub stars: 7
- Dbt ModelingModel in dbt across staging -> intermediate -> marts layers, choose materialization (view/table/incremental) by the trade, write correct incremental models (reliable unique key, is_incremental filter, late-data strategy), and keep it DRY with refs/sources/macros.Votes: 0GitHub stars: 7
- Incremental Model PatternsBuild reliable incremental dbt models: choose the right unique_key and strategy (append, merge, delete+insert), handle late-arriving data and out-of-order events, write a safe is_incremental filter, and design the full-refresh fallback — so the model is idempotent from day one.Votes: 0GitHub stars: 7
- Semantic Metrics LayerBuild a governed semantic/metrics layer: define each metric once as metrics-as-code (dbt Semantic Layer/MetricFlow) with explicit grain and filters, model entities/dimensions to prevent fan-out, and expose one contract every BI tool consumes — ending metric drift.Votes: 0GitHub stars: 7
- Api Deprecation RolloutStep-by-step playbook for deprecating and sunsetting an API version — header strategy, consumer communication timeline, traffic monitoring gates, and the SDKs/portal update checklist.Votes: 0GitHub stars: 7
- Cursor Pagination DesignPlaybook for designing cursor (keyset) pagination on list endpoints: cursor encoding, response envelope, query parameter contract, and the migration path away from offset pagination.Votes: 0GitHub stars: 7
- Idempotency Key DesignPlaybook for designing safe-to-retry POST and PATCH operations using Idempotency-Key — covers key format, dedup window, stored-response replay, conflict handling, and OpenAPI declaration.Votes: 0GitHub stars: 7
- Openapi Contract AuthoringStep-by-step playbook for writing a contract-first OpenAPI 3.1 document — from info block to path items, component reuse, and Spectral pre-flight. Covers resource modeling, status codes, error schema, and pagination shape.Votes: 0GitHub stars: 7
- Problem Details Error DesignPlaybook for designing a consistent RFC 9457 Problem Details error model — type URIs, extension members, status code mapping, and a catalog template. Prevents per-endpoint bespoke error shapes.Votes: 0GitHub stars: 7
- Spectral Ruleset AuthoringPlaybook for writing and wiring a Spectral ruleset that enforces an API style guide in CI — covers rule anatomy, severity levels, custom functions, and the recommended core-rules baseline.Votes: 0GitHub stars: 7
- Choose Statistical TestPick the right hypothesis test for a described scenario by traversing the test-selection decision tree (data type → #groups → paired? → assumption gate → test), then return the recommended test, its assumption checks, its nonparametric fallback, and a ≤10-line runnable snippet. Reach for this when the user asks "which test do I use?" or hands over two-or-more groups/variables to compare. Used by `applied-statistician` (primary).Votes: 0GitHub stars: 7
- Experiment AnalysisAnalyze a completed A/B test or experiment defensibly — check it against the pre-registered plan, run the primary-metric test, report effect size + CI (not just p), check guardrail metrics, apply a multiple-comparison correction across metrics/segments, and screen for the peeking/p-hacking pitfalls before declaring a winner. Used by `applied-statistician` (primary).Votes: 0GitHub stars: 7
- Power And Sample SizeCompute or advise the sample size an experiment needs BEFORE it launches — from α (0.05), power (0.80), and a minimum detectable effect (MDE) — or compute the power/MDE a fixed sample can achieve. Prevents the underpowered-study pitfall and is the prerequisite to any A/B test. Returns the n, the assumptions behind it, and a runnable snippet. Used by `applied-statistician` (primary).Votes: 0GitHub stars: 7
- Regression And Forecasting ReviewReview or design a regression model or a time-series forecast so it's defensible — pick the model family (OLS / logistic / Poisson GLM; ARIMA / SARIMAX / ETS), check the assumptions that matter for that family, report honest prediction/confidence intervals, and screen for overfitting, data leakage, and "coefficient = cause" overreach. Used by `applied-statistician` (primary).Votes: 0GitHub stars: 7
- Statistical Qa Of MetricsDecide whether a dashboard metric movement, comparison, or trend is signal or noise — and annotate it honestly (significance, confidence interval, "not enough data yet"). The interop seam with data-platform — invoked by `data-platform/dashboard-builder` when a widget shows a comparison/trend that needs a statistical-validity annotation. data-platform answers "is this number correct?"; this skill answers "is it real?". Used by `applied-statistician` (primary) + `data-platform/dashboard-builder`.Votes: 0GitHub stars: 7
- Comfort Safety And AccessibilityTreat XR comfort, physical safety, and accessibility as requirements: hold a sustained framerate, choose comfortable locomotion, design for the tracking volume / guardian / play-space and passthrough so users don't hit walls, and ship accessibility options (seated mode, one-handed paths, snap turn, captions, adjustable text) as defaults. Comfort research verify-at-use.Votes: 0GitHub stars: 7
- Spatial Rendering And PerformanceHold the XR frame budget: derive the per-eye ms target from the device refresh rate, profile on device to find the CPU-vs-GPU-vs-thermal bound, cut draw calls / overdraw / fill rate in order, and use foveated rendering and reprojection as headroom not a crutch — budgeting for the thermal-sustained clock, not peak. Device numbers verify-at-use.Votes: 0GitHub stars: 7
- Xr Interaction And LocomotionDesign and implement XR interaction: hand / controller / gaze input on an OpenXR action abstraction, locomotion chosen for comfort (teleport / dash / snap-turn vs smooth + vignette), reachable and readable 3D UI, and intentional grab/physics — with accessibility built in as a requirement. Input-API specifics verify-at-use.Votes: 0GitHub stars: 7
- Xr Target And Engine SelectionChoose the XR target platform (standalone headset vs PC-VR vs WebXR vs mobile-AR) and engine (Unity / Unreal / native-OpenXR / WebXR) on the use-case, audience device, and distribution channel — then commit to an OpenXR-first architecture and derive the per-eye perf budget the target implies. Device/version specifics verify-at-use.Votes: 0GitHub stars: 7