Use when finishing a feature, fixing a bug, before committing React code, or when the user types `/doctor`, asks to scan, triage, or clean up React diagnostics. Covers lint, accessibility, bundle size, architecture. Includes a regression check and a full local-triage workflow that fetches the canonical playbook.

Story-to-e2e-test workflow that takes a Jira [QE] Story, discovers the project's e2e testing infrastructure, plans test scenarios, writes e2e tests matching project conventions, validates them, and manages review via GitHub PRs. Use when implementing [QE] stories produced by the design workflow. Activated by commands: /ingest, /plan, /revise, /code, /validate, /publish, /respond.

Use this skill when the user wants to convert a Prefect @task that refreshes a Tableau workbook or extract — using tableau-server-client (TSC) library, RefreshWorkbookRequest, or ScheduleItem — into an equivalent Orchestra pipeline task targeting Tableau Cloud. Triggers: any mention of migrating Tableau refresh Prefect tasks to Orchestra; Prefect flow code importing tableau_server_client and calling rest_api.workbooks.refresh.

Update the primary SDK dependencies of harness packages. Use when asked to update the harness SDKs or harness adapter dependencies.

Operates GitHub via gh CLI and REST/GraphQL — PRs, issues, Actions, self-hosted Runner inventory/capacity, repos, collaborators, org permissions, 2FA — with explicit target, authorization, and independent readback. Use when a write reports success but state didn't change, or choosing gh/REST/GraphQL/UI-only. Not for local Git recovery (use git-safety-net), maintainer PR review (use github-review-pr), or upstream contribution (use github-contributor).

Use this skill for any question involving telemetry data: "investigate an issue", "debug a problem", "find out why something is slow", "check error rates", "analyze user behavior", "understand a production incident", "query telemetry data", "look at logs", "search logs", "find errors", "find stack traces", "de-minify a stack trace", "map a RUM error to source code", "filter by severity", "check traces", "examine spans", "investigate request latency", "debug service-to-service calls", "look up a trace ID", "analyze RUM data", "query rum.events", "check frontend performance", "frontend errors", "Core Web Vitals", "JavaScript exceptions", "query metrics", "check CPU usage", "run a PromQL query", "check error rate", "look up a metric", "check memory usage", "how do I write a DataPrime query", "DataPrime syntax", or wants to answer questions using observability data from logs, metrics, traces, RUM, or APM.

When the user wants to reduce churn, or asks why customers cancel, whether to offer a discount or pause: cancellation flows, exit surveys, save offers, dunning and failed-payment recovery. For win-back emails, see emails. For in-app upgrade screens, see paywalls.

When the user wants to write or judge ad creative, or asks which ad, hook, or angle to run or kill: headlines, primary text, and variations for Google, Meta, LinkedIn, and TikTok, fresh angles when creative fatigues. Test hypotheses name the metric, comparison, and expected direction. No AI tells like 'it's not X, it's Y'. For campaign strategy, see ads. For landing pages, see copywriting.

Find out what is going wrong in LLM or agent traffic by reading sampled Phoenix traces, spans, or sessions, writing free-form notes (open coding), then grouping the notes into a few narrow annotations, one per failure dimension with a small label set and counts, that pick eval targets and fix priorities (axial coding). Use for "what's going wrong with this agent", "I just instrumented my app, where do I start", "review these traces", "the chatbot keeps losing context", "what kinds of mistakes is the model making", "what categories of failures do we have", "what should I build evals for", "how do I prioritize fixes", "group these notes", "MECE breakdown" — or any framing that needs observations or categories grounded in real traces rather than invented top-down, even without naming the technique.

Find out what is going wrong in LLM or agent traffic by reading sampled Phoenix traces, spans, or sessions, writing free-form notes (open coding), then grouping the notes into a few narrow annotations, one per failure dimension with a small label set and counts, that pick eval targets and fix priorities (axial coding). Use for "what's going wrong with this agent", "I just instrumented my app, where do I start", "review these traces", "the chatbot keeps losing context", "what kinds of mistakes is the model making", "what categories of failures do we have", "what should I build evals for", "how do I prioritize fixes", "group these notes", "MECE breakdown" — or any framing that needs observations or categories grounded in real traces rather than invented top-down, even without naming the technique.

Apply an emotional resonance lens to a UI that is technically correct but flat, prescribing changes at the copy, motion, and interaction layer. Use when a design tests fine but lands cold. For the polish-perception argument, use `aesthetic-usability` (ui-design).

Write rationale connecting decisions to user needs, business goals, and principles. Use when a decision needs defending in writing. For a live conversation, use `design-negotiation`.

Design a usability study — research questions, methodology, participant criteria, metrics, and facilitation guide. Use when planning the study as a whole. For writing the task scenarios inside it, use `test-scenario` (prototyping-testing).

GitHub Actions workflow patterns for React Native iOS simulator and Android emulator cloud builds with downloadable artifacts. Use when setting up CI build pipelines or downloading GitHub Actions artifacts via gh CLI and GitHub API.

Implements an accepted incremental brownfield migration from native iOS or Android to React Native or Expo using @callstack/react-native-brownfield. Use after the brownfield path has been selected, when setting up the integration, packaging XCFramework or AAR artifacts, or adding React Native surfaces to native hosts.

Retired. Reporting feedback and sharing a session are documented by the CLI itself: run `deepline feedback send --help` and `deepline sessions send --help`. Kept only so installed CLIs that still list this skill keep syncing.

Understand how code works, how a change was done before, and which repos are coupled — to answer a question, plan a code change, debug a regression, or scope a fix, using Qodo managed tools through MCP or the CLI. Use when a task needs to understand a codebase, its history, or how its repos relate — especially for a repo you don't have checked out or work spanning repos — "how does X work", "where is X defined", "who changed X", "explain this service", "plan the change for X", "what would changing X affect", "which repos depend on X", "why did X regress / when did it break", "has this been fixed before", "how did we solve X".

Understand how code works, how a change was done before, and which repos are coupled — to answer a question, plan a code change, debug a regression, or scope a fix, using Qodo managed tools through MCP or the CLI. Use when a task needs to understand a codebase, its history, or how its repos relate — especially for a repo you don't have checked out or work spanning repos — "how does X work", "where is X defined", "who changed X", "explain this service", "plan the change for X", "what would changing X affect", "which repos depend on X", "why did X regress / when did it break", "has this been fixed before", "how did we solve X".

Set up Qodo in a coding agent — install the CLI, sign in, and verify tools. Use after plugin installation, on setup requests, or when a Qodo skill finds a missing CLI or login.