Skills

Helps design and implement features for machine learning models from raw tabular, time-series, or text data. Suggests transformations, encodings, aggregations, and leakage-safe splits, explains the rationale, and generates reproducible feature pipeline code with validation.

Defines data contracts between producers and consumers to prevent breaking changes in pipelines. Covers schema definitions, semantic types, freshness and quality SLAs, ownership, versioning, and backward/forward compatibility rules. Generates contract specs (e.g., ODCS-style) and CI checks that fail builds when a producer violates the contract.

Drafts clear product requirements documents from rough ideas or stakeholder notes. Structures the problem statement, goals and non-goals, user stories, acceptance criteria, success metrics, and open questions, and surfaces ambiguities and edge cases that need decisions before engineering starts.

Configures HTTP security headers to harden web applications. Covers Content-Security-Policy (including nonces and strict-dynamic), HSTS, X-Content-Type-Options, Referrer-Policy, Permissions-Policy, COOP/COEP/CORP, and cookie flags. Produces server/CDN configuration, explains tradeoffs, and provides a rollout plan using report-only mode to avoid breakage.

Designs data quality checks for tables, pipelines, and warehouses. Generates expectation suites covering schema conformance, null and uniqueness constraints, referential integrity, freshness, and statistical drift, then wires them into pipelines so bad data is caught before it reaches dashboards or models.

Turns reliability goals into concrete SLIs, SLOs, and error budgets. Helps choose the right service level indicators, set realistic targets from historical data, compute burn rates, and design multi-window multi-burn-rate alerting so teams get paged before the budget is exhausted.

Builds retention and behavioral cohort analyses from event or transaction data. Defines cohorts by acquisition date or attributes, computes retention and churn curves, generates the SQL or pandas code to produce cohort tables, and interprets the results into actionable insights about engagement and lifecycle.

Generates realistic synthetic datasets that preserve the statistical properties and relationships of source data without exposing real records. Covers schema-aware generation, correlated and time-series fields, class balancing for ML training, and constraint preservation, with code for tools like SDV, Faker, or custom generators.

Reviews and rewrites Dockerfiles for smaller images, faster builds, and better security. Applies multi-stage builds, optimal layer ordering and caching, minimal base images, non-root users, and .dockerignore tuning, and flags vulnerabilities and bloat while keeping the build reproducible.

Generates and refactors Helm charts to package Kubernetes applications. Produces templated manifests, values.yaml with sensible defaults, helpers, chart dependencies, and hooks, and parameterizes images, resources, probes, and ingress so a workload can be deployed consistently across environments.

Conducts structured market and competitor research and turns it into a decision-ready brief. Covers market sizing (TAM/SAM/SOM), competitor feature and pricing matrices, positioning and differentiation, customer segments, and SWOT, with clearly stated assumptions and sources so conclusions can be traced and challenged.

Designs conversational scripts and dialog flows for voice assistants, IVR systems, and chatbots. Crafts natural prompts and responses, handles confirmations, errors, and re-prompts, designs branching flows and fallbacks, and adapts tone for the brand while keeping turns concise and accessible for audio-first interfaces.

Turns incident timelines, alerts, and chat logs into a clear, blameless postmortem. Produces an executive summary, impact assessment, detailed timeline, root-cause analysis using techniques like the five whys, and a prioritized list of follow-up action items with owners, in a format ready to share with stakeholders.

Plans and executes safe dependency upgrades across a project. Analyzes current versions, reads changelogs and release notes for breaking changes, sequences upgrades to minimize risk, applies required code migrations, and verifies with builds and tests. Works across npm, pip, Maven, Cargo, and Go modules.

Optimizes web content for search engines and readers. Performs keyword analysis and intent mapping, improves titles, meta descriptions, headings, and internal linking, and generates structured data (JSON-LD) while keeping copy natural and useful. Outputs an actionable, prioritized list of on-page SEO improvements.

Generates mock API servers and stubbed responses from OpenAPI specs, sample payloads, or natural-language descriptions. Produces realistic fixture data, configurable latency and error scenarios, and ready-to-run mock servers using tools like Prism, MSW, WireMock, or json-server so frontend and integration tests can proceed without the real backend.

Designs feature-flagging and progressive-delivery strategies and generates the integration code. Covers flag naming and lifecycle, targeting and segmentation rules, percentage rollouts, kill switches, and cleanup of stale flags across providers like LaunchDarkly, Unleash, Flagsmith, or a homegrown config service.

Assists with internationalization (i18n) and localization (l10n) of applications and content. Extracts translatable strings, generates resource bundles, translates copy while preserving placeholders and ICU plural/gender rules, and flags layout, date, number, and currency formatting concerns for target locales.

Writes safe, reversible database schema migrations and the corresponding rollback scripts. Plans zero-downtime changes using expand-and-contract patterns, handles data backfills and index creation without locking, and generates migrations for tools like Alembic, Flyway, Prisma, Knex, and Rails ActiveRecord with clear up/down steps.

Detects and redacts personally identifiable information (PII) and sensitive data from text, logs, and structured datasets. Recommends anonymization techniques such as masking, tokenization, pseudonymization, k-anonymity, and differential privacy, and generates reusable redaction code while preserving analytical utility and referential integrity.

Parses and analyzes application, system, and access logs to surface errors, anomalies, and root causes. Correlates events across services, identifies recurring patterns and spikes, extracts structured fields from unstructured lines, and produces a prioritized summary with likely causes and recommended next steps.