Lvtd Llc Skills

Generate and prioritize LinkedIn content ideas from audience problems, industry news, questions, hashtags, competitor posts, source material, and growth experiments. Use when planning LinkedIn post topics, content calendars, thought-leadership angles, post series, or idea backlogs.

Design and review adaptive Go CLI terminal experiences with explicit human, plain, and structured modes; per-stream TTY detection; color and accessibility controls; prompts; progress; secret input; cancellation; and broken-pipe handling. Use when a Go CLI must work safely for people, scripts, AI agents, CI, redirected streams, or interactive dashboards.

Design degradation holdbacks and long-term cumulative holdbacks for product experiments and feature rollouts. Use when a team needs a long-term counterfactual, wants to measure delayed impact after launch, is worried about metric degradation over time, needs to decide holdback size or duration, or must weigh the user/business cost of withholding a feature.

Design a sustainable test architecture using test desiderata, test pyramid tradeoffs, fast and slow test separation, integration boundaries, CI feedback loops, and architecture choices that make code testable. Use when a test suite is too slow, too brittle, too mock-heavy, unclear about unit vs integration coverage, or needs a testing strategy before major growth.

Plan and review survey-led digital PR campaigns that earn editorial links through credible data, methodology, story framing, journalist outreach, and promotion. Use when creating data studies, expert surveys, journalist pitches, methodology notes, PR exclusives, or linkable research assets for SEO.

Make Django deployment changes test-driven across Docker, production settings, Gunicorn, WhiteNoise/static files, environment variables, Ansible/server setup, staging smoke tests, and release checks. Use when containerizing Django, preparing production config, debugging staging, automating deploys, or deciding what deployment behavior functional tests should verify.

Test-drive Django validation across model constraints, forms, ModelForms, views, template errors, duplicate handling, and redirects. Use when adding or refactoring Django forms, surfacing validation errors, deciding model-vs-form-vs-view responsibility, or preventing invalid input from being saved.

Design and review recoverable Go CLI errors, exit semantics, structured logging, debug modes, traces, profiles, redaction, and user-consented diagnostic bundles. Use when a CLI needs actionable failures, safe support evidence, production troubleshooting, or observability that does not corrupt command output or expose secrets.

Design, build, test, and review production Go HTTP servers covering inbound adapters, modern ServeMux routing, middleware, strict bounded request decoding, response commitment, transparent ResponseWriter wrapping, server timeouts, health semantics, graceful shutdown, shared-state safety, and httptest verification. Use when implementing an HTTP service, API handler, middleware stack, server lifecycle, health endpoint, or inbound JSON boundary in Go.

Choose methods for measuring long-term product impact after or beyond an A/B test. Use when comparing long-term holdbacks, post-period analysis, continuous monitoring, CLV models, delayed effects, short-term versus long-term metric tradeoffs, or lower-cost alternatives to long-term holdbacks.

Audit and improve commercial pages so they have legitimate citation reasons for guest posts, resource pages, digital PR, partnerships, and editorial mentions. Use when a sales page needs citable sections, proof points, tools, data, definitions, expert quotes, or less promotional link targets before outreach.

Plan A/B testing platform strategy, architecture, and build-vs-buy decisions for product engineering teams. Use when deciding whether to build or buy an experimentation platform, scoping feature flagging, targeting, assignment, exposure logging, metrics pipelines, dashboards, governance, or evolving a simple testing setup into a durable platform.

Find, prioritize, and reclaim missed link opportunities from unlinked brand mentions, image uses, content citations, broken owned URLs, outdated references, and attribution gaps. Use when auditing mentions, requesting credit links, fixing attribution, handling negative mentions, or salvaging lost backlinks.

Use Python mocks and fakes as a design tool without losing behavioral confidence. Use when testing external dependencies, choosing monkeypatch versus unittest.mock.patch, isolating slow boundaries, avoiding mock-heavy tests, interpreting mock call assertions, or refactoring toward clearer dependency seams.

Edit and review developer documentation for technical accuracy, completeness, structure, clarity, brevity, peer feedback, technical review, technical verification, QA procedure testing, and feedback integration. Use when reviewing, editing, refactoring, or preparing developer docs for release, especially docs with procedures, examples, prerequisites, or high-risk technical claims.

Manage Django spikes and de-spiking with tests: branch experiments, exploratory code, learning capture, functional tests against spiked behavior, revert-and-rebuild, custom auth flows, email/token models, and dependency seams. Use when a Django feature was prototyped quickly and needs to be turned into production-quality tested code.

Audit and measure developer documentation quality using functional quality, structural quality, accessibility, purpose, findability, accuracy, completeness, clarity, concision, consistency, analytics, user goals, doc goals, and metric plans. Use when evaluating docs, designing documentation metrics, selecting dashboards, or linking docs work to user and business outcomes.

Evaluate A/B tests for inclusive product impact across user groups, accessibility needs, device constraints, privacy behavior, bandwidth, geography, and underrepresented segments. Use when checking whether an experiment benefits or harms different user groups, planning segmentation dimensions, auditing test/control balance, interpreting subgroup effects, or reviewing product changes for inclusive experimentation.

Audit and improve the site architecture around linkable content before promotion, including orphan pages, internal links, hubs, URL stability, crawlability, canonicals, conversion paths, and link equity flow. Use when a linkable asset exists but may be buried, isolated, technically weak, or poorly connected to business pages.

Plan, measure, and iterate LinkedIn content experiments using post hypotheses, format tests, analytics, comments, and learning loops. Use when comparing LinkedIn hooks, formats, topics, hashtags, posting cadence, newsletters, documents, videos, or content performance data.

Build link opportunities through genuine publisher, expert, partner, and community relationships before asking for mentions or links. Use when planning reciprocity-first outreach, expert roundups, content promotion networks, partner collaboration, warm pitches, or long-term link relationship systems.

Roll out an experimentation-friendly culture across product, engineering, data, and leadership teams. Use when introducing A/B testing to an organization, overcoming resistance to experiments, shifting teams away from launch-by-opinion, increasing experiment demand, defining rollout tactics, or creating an experimentation adoption plan.

Design and review maintainable Go CLI application boundaries, package layout, dependency direction, command factories, configuration ownership, adapters, services, storage, and composition roots. Use when starting a multi-command CLI, extracting business logic from Cobra, untangling globals, adding external dependencies, or deciding what belongs under cmd, internal, or reusable packages.

Operate scaled link outreach with segmentation, templates, personalization variables, pilot batches, suppression lists, deliverability checks, reply diagnostics, compliance review, and campaign reporting. Use when planning bulk outreach, reviewing email operations, scaling a proven offer, or debugging low reply rates.

Assess whether experiment results are credible enough to influence product decisions. Use when checking false positive or false negative risk, underpowered metrics, suspiciously large lifts, replication needs, meta-analysis, stratified sampling, covariate adjustment, or whether A/B test insights should be trusted.

Research developer documentation audiences by mapping user goals, learning objectives, business goals, developer traits, user questions, personas, user stories, journey maps, and friction logs. Use when planning docs for APIs, SDKs, platforms, CLIs, developer tools, onboarding guides, SaaS docs, or support-heavy docs before writing or restructuring content.

Design, debug, and maintain Django Selenium functional tests with live server setup, explicit waits, test isolation, page objects, screenshots, CI artifacts, and staging checks. Use when browser tests are flaky, too slow, duplicated, failing in CI, need a page pattern, or should verify a Django user journey end to end.

Evaluate documentation platforms and authoring tools by requirements, workflow fit, migration risk, content model, search, integrations, portability, support burden, and cost. Use when choosing docs-as-code, CMS, wiki, DITA, API reference, static-site, help-center, or custom docs tooling, planning a migration, or deciding whether tooling will fix documentation problems.

Prioritize an experimentation platform roadmap across rate, quality, cost, usability, process, infrastructure, and advanced methods. Use when deciding which experimentation capability to build next, whether to platformize interleaving or adaptive testing, how to align experimentation with company goals, or how to trade off speed versus rigor.

Plan adaptive experimentation strategies beyond fixed-horizon A/B tests. Use when evaluating sequential testing, early stopping, multi-armed bandits, Thompson sampling, contextual bandits, dynamic traffic allocation, exploration/exploitation tradeoffs, or readiness for adaptive testing infrastructure.

Research product and feature facts for developer documentation by mapping source-of-truth owners, validating behavior hands-on, interviewing SMEs, and turning uncertainty into doc-ready notes. Use when feature specs are incomplete, implementation details need user-facing meaning, SMEs disagree, or docs require evidence before planning, drafting, release notes, API docs, tutorials, scenarios, or troubleshooting content.

Verify and monitor running experiments for operational quality. Use when designing prelaunch QA, spot-check tooling, experiment canaries, A/A tests, leakage checks, interference monitoring, active experiment dashboards, alerts, or an experimentation quality roadmap.

Improve experiment sensitivity and reduce traffic or duration requirements. Use when choosing sensitive metrics, working with minimum detectable effect, reducing variants, applying capping metrics, CUPED, variance reduction, or deciding how to get trustworthy A/B test signal with fewer users.

Plan experiment throughput strategies for mature A/B testing programs. Use when testing capacity is constrained, teams are waiting for experiment slots, roadmap coordination is slowing learning, or a team must choose isolated, overlapping, parallel, or capacity-aware experiment scheduling without sacrificing result quality.

Plan, draft, repurpose, and audit LinkedIn articles and newsletters that demonstrate expertise and support a professional content strategy. Use when writing long-form LinkedIn articles, newsletter editions, article teasers, follow-me articles, issue plans, or post-to-newsletter repurposing.

Audit, organize, and redesign developer documentation information architecture, including content inventory, navigation, landing pages, sequences, hierarchies, webs, breadcrumbs, sidebars, metadata, redirects, migration, platform/tool selection, and maintainable IA decisions. Use when restructuring docs sites, improving findability, planning doc migration, evaluating docs tooling, or organizing large documentation sets.