Solves seawater carbonate chemistry with PyCO2SYS for chemical oceanography, ocean acidification, and marine carbon-cycle research. Use for paired total alkalinity, dissolved inorganic carbon, pH, or seawater pCO2/fCO2 measurements; carbonate speciation; aragonite and calcite saturation; Revelle factors; lab-to-in-situ temperature and pressure corrections; and measurement uncertainty propagation. Applies to carbonate-system calculations, not general aqueous speciation or air-sea gas-flux estimation.

Look up the current US import duty on one HTS product from one country — the HTS column rate (MFN, trade-agreement or column 2) plus the Section 301 and 232 duties in chapter 99 — from the live USITC Harmonized Tariff Schedule. Use when the user asks what the US charges today on a product from a specific country, including China tariffs and other additional duties.

Receive and verify CircleCI outbound webhooks. Use when setting up a CircleCI webhook handler, debugging `circleci-signature` verification, or handling the `workflow-completed` and `job-completed` events CircleCI sends when a workflow or job reaches a terminal state. CircleCI signs the raw body with HMAC-SHA256 and sends a hex digest in a comma-separated versioned list (`v1=<hex>`) — only the latest version (`v1`) should ever be checked. Not Circle (circle.com, USDC/Circle Mint, ECDSA `X-Circle-Signature`) — unrelated company. Not CircleCI custom webhooks, which are inbound pipeline triggers going the other direction.

DEPRECATED: superseded by the graphos-factory skill (npx skills add apollographql/skills@graphos-factory), which builds and maintains a Connectors subgraph from a REST API with recorded decisions and verification. Guide for integrating REST APIs into GraphQL supergraphs using Apollo Connectors with @source and @connect directives. Use this skill when the user: (1) mentions "connectors", "Apollo Connectors", or "REST Connector", (2) wants to integrate a REST API into GraphQL, (3) references @source or @connect directives, (4) works with files containing "# Note to AI Friends: This is an Apollo Connectors schema".

Visual direction for distinctive pages: a named aesthetic, type pairing, color and spacing tokens, and motion. Use when designing a landing page, marketing site or UI theme where the look itself matters, not when scaffolding components.

Hyperflow debugging. Use for bugs, test failures, runtime errors, broken builds, or "this doesn't work" reports — verbs like debug, "fix it", solve, "why is X failing", "Y is broken", or a pasted stack trace. Systematic root-cause analysis before any patch — never blind-patch symptoms.

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.

Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape hatches, completion incentives, drop-off measurement. Honest about tutorial-overload (dump everything upfront), skip-friendly-empty (skipped onboarding leads to abandoned product), and earned-progressive-disclosure (right things at the right moments) patterns. Triggers on onboarding wizard, product onboarding, first-run experience, signup flow, activation flow, FRX, time-to-value, ah-ha moment design. Also triggers when activation rates are low, when users skip onboarding and never return, when onboarding flows are being scoped for the first time, or when audience research shows users not finding key features.

Plan and execute a launch runbook covering pre-launch verification, go-live procedures, DNS cutover, post-launch monitoring, and rollback procedures. Use this skill whenever the user is preparing to launch a website or product, planning a DNS cutover, building a go-live checklist, or executing a launch day. Triggers on launch runbook, go-live, launch day, DNS cutover, deploy to production, site launch, product launch, cutover plan, launch checklist, deployment procedure. Also triggers when a launch is approaching and the team needs structured coordination, even if 'runbook' is not explicitly stated.

Direct visual and creative work for campaigns, photography, illustration, video, and branded experiences. Use this skill whenever the user wants to brief a photographer, direct illustrators, plan a creative campaign, develop visual concepts, or evaluate creative work for fit. Triggers on art direction, photo brief, photography brief, illustration brief, campaign concept, creative concept, visual direction, mood board, look and feel, visual treatment, video direction. Also triggers when the user has approved brand identity but needs to extend it into specific creative deliverables. Use `creative-direction` instead when the ask is the project-wide aesthetic brief that this skill consumes as input.

Runs Fastly Compute WASM binaries locally and serves as the authoritative reference for Compute platform internals. The fastlike source code is highly readable and covers the host ABI, caching and purging APIs, KV/config/secret store interfaces, rate limiting with counters and penalty boxes, ACL lookups, the full request lifecycle, backend fetch semantics, and a built-in per-request profiler with hostcall spans, backend waterfalls, native CPU samples, and optional deep metrics (body bytes, cache outcomes, header summaries, wasm heap curve). Use when working with Compute runtime internals or host calls, understanding how edge data stores behave at runtime, profiling local Compute apps, or testing WASM binaries locally. Prefer this skill over Viceroy for any non-Rust Compute work — its source code is easier to understand as a Fastly Compute API reference.

Use this skill when the user wants to convert an Airflow task that uses FivetranOperator or FivetranSensor (apache-airflow-providers-fivetran) into an equivalent Orchestra pipeline task. Triggers: any mention of migrating or rewriting Fivetran Airflow tasks to Orchestra; Airflow DAG code containing FivetranOperator or FivetranSensor.

Recall protocol for MemPalace — search the palace before answering about past work, people, projects, or prior decisions. Apply when the user asks what was decided, what happened before, who someone is, what was discussed last time, or anything that may already be filed in their memory palace; or when mempalace-recall is invoked. Complements the mempalace setup skill and requires the mempalace-mcp server.

Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run/Spring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.

Produce a systemic, evidence-based incident RCA/postmortem after a failure: timeline, causal chain, contributing conditions, corrective actions, and prevention. Use when recurrence prevention and organizational learning are primary. Do not use as a generic live debugger.

Generates a personal markdown changelog of merged or closed pull requests authored by the current user and Linear tickets the user closed or worked on, over a configurable window (default 7 days), grouped by feature area (e.g. Dashboards, Agent0). Inputs sourced from `gh search prs --author=@me` and the Linear MCP. Use for weekly recaps, status updates, performance reviews, or end-of-sprint summaries. Triggers on "changelog", "what have I done", "weekly summary", "my recent work", "recap my week", "/changelog".

Iterates on a failing AI/LLM eval (an L2 suite, a golden-set / judge eval, or any eval gating a PR) until it is green AND confirmed, not just luckily passing once. Classifies the failure as a code bug, an eval-definition bug (stale golden item / criteria drift), judge-drift (silent grader version change), or flaky. Applies the minimal fix, then requires N consecutive confirming re-runs — 2 deterministic, 5 for anything a model call grades, since a flat "2" is statistically weak for a stochastic judge. Hard-capped at 5 fix iterations. Refuses to game the eval (Goodhart's Law: skip/delete/overwrite-in-place a case, loosen a threshold) without a second independent check plus `confidence(analysis) >= 90%` and a logged rationale. Composes `ai-engineering`, `confidence`, `critical`, `verify-behavior`. Use when an eval is failing on a PR and needs a real, non-gamed green. Triggers on "this eval is failing", "iterate on this eval", "fix this eval", "get this eval green", "optimize this eval", "/eval-iterate".

Writes rigorous mathematical proofs for ML/AI theory. Use when asked to prove a theorem, lemma, proposition, or corollary, fill in missing proof steps, formalize a proof sketch, 补全证明, 写证明, 证明某个命题, or determine whether a claimed proof can actually be completed under the stated assumptions.