Build custom OpenTelemetry Collector distributions with OCB (OpenTelemetry Collector Builder). Use when authoring or debugging a builder manifest (builder.yaml), choosing component and provider versions, building a collector that bundles a custom or out-of-distribution component, setting up CI or Docker builds of a distribution, or troubleshooting OCB build failures. Triggers on "ocb", "collector builder", "custom collector distribution", "builder manifest", "builder-config", and version-mismatch errors from OCB builds.

OpenTelemetry in Ruby — SDK setup, manual tracing, experimental metrics and logs, Bundler integration, contrib instrumentation for Rails/Rack/Sinatra/Sidekiq and other gems, propagation, performance, and upgrades. Use when adding, reviewing, configuring, or troubleshooting OpenTelemetry in a Ruby or Rails service. Triggers on "setup otel in ruby", "ruby telemetry", "Rails OpenTelemetry", "OpenTelemetry::SDK.configure", "opentelemetry-instrumentation-all", "Ruby tracing", "Rack otel", "Sidekiq otel", or any Ruby-related OTel question.

OpenTelemetry in Python — SDK setup, declarative config, zero-code instrumentation (opentelemetry-instrument, opentelemetry-distro), contrib auto-instrumentation, manual API, performance tuning, breaking changes. Use when configuring or troubleshooting OpenTelemetry in a Python service. Triggers on "setup otel in python", "python telemetry", "python tracing", "opentelemetry-instrument", "opentelemetry-distro", "TracerProvider python", "MeterProvider python", "FastAPI/Flask/Django otel", "python logging bridge".

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.

Use when someone needs a LinkedIn plan rather than a post — content pillars, positioning for a career change or consulting or thought leadership, a sustainable posting cadence, or a newsletter decision. Triggers on "what should I post about", "how often should I post", "LinkedIn content strategy", "should I start a LinkedIn newsletter", "grow my following", "90-day plan". Validates the positioning brief, sizes the week against real hours and refuses a plan that will not survive week five, and gates a newsletter against eligibility and a six-month cadence commitment.

Use when someone wants to grow reach through comments, replies, groups, or outreach on LinkedIn — a commenting roster, a connection request note, a DM or InMail, a networking plan, or a check on whether their outreach volume is safe. Triggers on "who should I engage with", "write a connection request", "cold DM", "LinkedIn outreach", "networking strategy", "how many invites can I send". Builds a weekly comment roster inside a real time budget, assembles one message at a time and refuses templates, and caps volume against LinkedIn's limits. Nothing is ever sent.

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.

Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.

Set up or modernize code-quality tooling for JavaScript and TypeScript projects. Use when creating a project, adding code-health or CI quality checks, making a repository agent-ready, or consolidating dead-code, duplication, architecture, dependency, and changed-code analysis. Do not use for formatting-only, lint-rule-only, or TypeScript type-error tasks. Do not use to run an analysis of a project that is already set up; use the fallow skill for that.

Set up or modernize code-quality tooling for JavaScript and TypeScript projects. Use when creating a project, adding code-health or CI quality checks, making a repository agent-ready, or consolidating dead-code, duplication, architecture, dependency, and changed-code analysis. Do not use for formatting-only, lint-rule-only, or TypeScript type-error tasks. Do not use to run an analysis of a project that is already set up; use the fallow skill for that.

Runs the human-verification lane of an agent loop, and proves review happened before work is called done. Builds a single-file HTML review page, collects batched feedback as a structured artifact instead of chat prose, and runs a gate that refuses to close while a BLOCKER is open, the reviewer is unnamed, or nobody has reviewed at all. Use when a plan, spec, RFC, report, landing page, migration, or any irreversible action needs human sign-off before shipping, or on requests such as 'get sign-off', 'have someone check this', 'hold until reviewed', 'needs approval first'. NOT for making AI text sound human (use content-humanizer or behuman). NOT for reviewing code diffs (use md-review or code-reviewer).

Use when writing or reviewing asynchronous C++ networking code with Boost.Asio or standalone Asio — TCP/UDP servers and clients, SSL/TLS, timers, strands, io_context, co_spawn, awaitable, async_read/async_write, asio::spawn, yield_context, or pre-C++20 completion-handler callbacks.

Converts books, documentation folders, and source collections (PDF, EPUB, DOCX, HTML, Markdown, RST, AsciiDoc, RTF, MOBI/AZW) into structured agent skills — extracting named frameworks, principles, techniques, and anti-patterns into a master SKILL.md plus on-demand chapter files, a glossary, a patterns file, and a decision cheatsheet. Use when the user wants to study a document with an agent, apply an author's frameworks while working, turn internal docs or standards into a reusable knowledge base, or package a compiled book skill as a claude-skills plugin.

Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.

The meta-framework for how a company runs — the connective tissue between all C-suite roles. Covers operating system selection (EOS, Scaling Up, OKR-native, hybrid), accountability charts, scorecards, meeting pulse, issue resolution, and 90-day rocks. Use when setting up company operations, selecting a management framework, designing meeting rhythms, building accountability systems, implementing OKRs, or when user mentions EOS, Scaling Up, operating system, L10 meetings, rocks, scorecard, accountability chart, or quarterly planning.

Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset. NOT a tactical data engineering skill — strategic decisions only.

Phase 1 of building a Claude Managed Agent — interview the founder about the one job the agent should do, then produce a build sheet (CMA primitives table + v1/v2 deferrals + eval plan) WITHOUT needing their API key yet. Use when the user says "help me scope an agent", "I have an idea for an agent", "what should this agent be", or when the orchestrator routes phase=interview. Drives the six intake slots (job, trigger, inputs, actions, definition-of-done, recurrence) via AskUserQuestion, maps them to primitives with interview_planner.py, assembles build-sheet.json with build_sheet_builder.py, and validates limits with primitives_validator.py. Connectors are mockable in v0 (schema-true custom tools); real MCP servers become v1 deferrals. Distinct from stage-launch (which turns the sheet into payloads).

Use when investigating a specific named Positron e2e (Playwright) test that is failing or flaking -- in CI ("why is <test> failing on main", "this test is flaky in CI", "is this a test bug or a product bug") or on the engineer's machine ("this e2e test just failed locally", "debug this e2e failure"). From CI it surfaces the test's distinct failure modes from history and pulls evidence for one; locally it reads the run's own trace, snapshot, and logs. Either way it reasons to a falsifiable root cause with the engineer and lands on a test fix, a product-bug issue, or an accepted-flake note. Do NOT use for a test you are writing or currently editing (author-e2e-tests); a whole CI run's failures, or a run ID/URL (e2e-failure-analyzer); Vitest or extension-host failures.

Use when reviewing the documentation a branch changes — AGENTS.md, READMEs and pages under wiki/ — or sweeping the whole wiki with --full. Cross-checks every claim a page makes against the code, finds wikilinks and heading anchors the branch broke, checks frontmatter and page shape, and reports D-C/H/M/L findings in the four-field format.