Run the PinchTab optimization loop (Docker, 3 blind subagents on the runner's HIGH model, 108 steps across 47 groups) against chrome, cloak, ghost-chrome, or all three providers. Pass `setup` (optionally followed by a provider or `all`) to run only the setup test (native binary, single subagent forced to the runner's LOW model) that validates the fresh-install OOTB flow per provider. Use when asked to 'run optimization', 'run the opt loop', 'benchmark the agent', '/pinchtab-opt', '/pinchtab-opt cloak', '/pinchtab-opt ghost-chrome', '/pinchtab-opt setup', '/pinchtab-opt setup all', or 'test pinchtab agent'.

Use this skill when a task needs browser automation through PinchTab: open a website, inspect interactive elements, click through flows, fill out forms, scrape page text, reuse a dedicated automation profile with user approval, export screenshots or PDFs, manage multiple browser instances, or fall back to the HTTP API when the CLI is unavailable. Prefer this skill for token-efficient browser work driven by stable accessibility refs such as `e5` and `e12`.

KFL2 (Kubeshark Filter Language) reference. This skill MUST be loaded before writing, constructing, or suggesting any KFL filter expression. KFL is statically typed — incorrect field names or syntax will fail silently or error. Do not guess at KFL syntax without this skill loaded. Trigger on any mention of KFL, CEL filters, traffic filtering, display filters, query syntax, filter expressions, write a filter, construct a query, build a KFL, create a filter expression, "how do I filter", "show me only", "find traffic where", protocol-specific queries (HTTP status codes, DNS lookups, Redis commands, Kafka topics), Kubernetes-aware filtering (by namespace, pod, service, label, annotation), L4 connection/flow filters, time-based queries, or any request to slice/search/narrow network traffic in Kubeshark. Also trigger when other skills need to construct filters — KFL is the query language for all Kubeshark traffic analysis.

OpenTelemetry SDK initialization and configuration. Use when setting up or reviewing TracerProvider, MeterProvider, or LoggerProvider; choosing exporters, processors, or propagators; configuring OTLP transport; or extending an existing SDK setup for new signals. Use this skill whenever the task involves wiring up the OpenTelemetry SDK, even if the user only mentions "add tracing" or "set up metrics" without saying "SDK."

Ollygarden's recommended pattern for setting up OpenTelemetry in Java services. Covers the Javaagent vs Spring Boot Starter vs manual autoconfigure decision-making, the Maven BOM dependency pattern, and the setup checklist (no query strings in telemetry, startup DB span hygiene, declarative YAML config, standard OTEL_* env vars honored). Use when adding OTel to a Java project, choosing a setup path, or reviewing dependency declarations. Triggers on "java otel setup", "javaagent vs starter", "opentelemetry-bom", "url.query", "query parameter PII".

Universal OpenTelemetry auto-instrumentation planning and review guidance. Use when deciding whether agent/library instrumentation is sufficient, when tuning auto-instrumented boundaries, when avoiding duplicate manual spans, or when reviewing auto instrumentation for noise, PII, volume, and business value.

Create a Trellis migration manifest and matching docs-site changelogs for a target release by analyzing commits since the previous release. Use when preparing a patch, beta, rc, or minor release manifest.

Create EF Core PRs targeting servicing release branches (release/*). Use when working on a PR that targets a release branch, backporting a fix from main, or when the user mentions servicing, patch, or release branch.

Author Quarkdown (.qd) documents, a Markdown-superset typesetting language that compiles to HTML, PDF, or plaintext. Use when the user wants to write or edit .qd files, produce typeset output (articles, reports, slides, books, wikis, notes, static sites) beyond what plain Markdown offers, or share structured plans in a presentation-ready format. Quarkdown adds function calls (`.func {arg}`), variables, layouts, math, diagrams, and document types (plain/paged/slides/docs).

Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.

Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.

Specification for instrumenting an opik-backend workflow with operational OpenTelemetry metrics — per-stage throughput/latency/error counters and native histograms, dimensioned per-customer (workspace). Use when a pipeline (scoring, ingestion, experiments, jobs) needs per-stage visibility. Covers metric emission only; building the Grafana dashboard from these metrics is a separate skill. Distinct from analytics-instrumentation (PostHog product events).

Detect and redact secrets in text. Use when sharing logs, snippets, or pastes that may contain API keys, OAuth tokens, JWTs, AWS credentials, GitHub tokens, Slack tokens, database connection strings, or private keys.

Use when creating, changing, running, or debugging shell-based `but` CLI performance scenarios under `crates/but/tests/performance`, including Hyperfine runs, fixture setup, setup-to-test state, and output inspection.

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Generate a branded Qwen Code desktop package from the Tauri desktop shell using a minimal brandId and logo. Use when the user wants a custom, white-label, or rebranded desktop client, installer, DMG/EXE/AppImage/deb, or one-click brand build on top of packages/desktop.

Create, scaffold, customize, validate, and locally test Qwen Code extensions. Use when the user wants a new Qwen Code extension, needs help choosing an extension template, wants to add QWEN.md context, commands, skills, agents, MCP servers, settings, hooks, channels, or LSP servers, or asks how to link and test an extension locally. Invoke with `/extension-creator` followed by an extension path and optional template name.

Draft and post direct, unhedged GitHub PR reviews and issue comments — every item is a required change with a pointer and reason, PR reviews land as "request changes" or "approve" (never a bare comment), and nothing is marked optional or deferred to a follow-up. Use whenever understand-pr or understand-issue (or any review) is about to draft or post to GitHub.

Authoring playbook for building agents that read or write tabular data — Google Sheets, Microsoft Excel, CSV, Airtable, Notion databases, or any spreadsheet. Use this when the user wants an agent that updates rows, reads cells, computes totals, generates reports from sheets, syncs data between spreadsheets, or automates anything involving rows, columns, ranges, or worksheets.

Authoring playbook for building agents that triage and reply to customer messages — support tickets, email inquiries, chat questions, refund requests, or product issues. Use this when the user wants an agent that handles inbound customer questions, drafts replies, escalates hard cases, summarizes tickets, or follows a support playbook.