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Found 1,299 Skills
Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. Part of the skills-for-java project
Creates, modifies, or manages Salesforce Experience Cloud LWR sites via DigitalExperience metadata. Always trigger when the tasks involve LWR sites configurations, e.g. creating/modifying pages/routes/views/theme layouts/branding sets, previewing sites, or creating/modifying guest sharing rule (metadata type sharingGuestRules)/guest user access/sharing records to guest users, or when user provides a guest user ID (15 or 18 characters starting with 005). NEVER trigger for React or any other ui bundle framework. LWR sites ONLY.
Facilitates the fourth step of a proven ideal-customer (ICP) method: refining classified weaknesses into deal-breakers — the circumstances that disqualify the product outright, no matter how well everything else fits — and mapping the anti-market segments who therefore will never buy. Takes the weaknesses from a strengths chart (W1, W2, …) plus a keystones file (K1, K2, …), walks the weaknesses one at a time, records deal-breakers with their anti-market segments in DEALBREAKERS.md (D1, D2, …), then HONES the keystones: appending the qualifiers each deal-breaker forces ('…processing at least $5,000/mo'), editing KEYSTONES.md under its frozen numbers with a change log. Load when the user has keystones and weaknesses and asks who will never buy, what their anti-market is, or 'run the deal-breakers step.' Do NOT load to derive keystones from strengths (the previous step), to find inciting events (the next step), or to write the final ideal-customer definition.
Distills a person's or company's strengths, experiences, and obsessions down to their one or two VOTERS — the decisive, idiosyncratically extreme traits that win the contest because customers who value them accept every other trade-off. Runs each candidate through a hard gauntlet: extremity earned through obsession (not mere competence), rarity among peers, decisiveness (name the weaknesses it overpowers), and reverberation (it must force decisions in product, pricing, and market). Caps the answer at two, records survivors in VOTERS.md — plus the near-miss 'special strengths' to deploy — and delivers the honest zero-voter verdict when nothing is extreme yet. Takes a self-portrait file (M-numbered), strengths or keystones charts, or a live capture. Load when the user asks what makes them special, what to bet the strategy on, their unfair advantage or superpower, or to 'find our voters.' Do NOT load to build the full self-portrait (the previous step) or to design the whole strategy — this file feeds it.
Facilitates the second step of a proven ideal-customer (ICP) method: distilling raw company observations into the few deep-truth attributes that matter, then classifying each as a strength, a weakness, or deliberately both. Takes an observations list (O1, O2, … — file or pasted), proposes attributes one at a time with their supporting observations, kills generic ones with the Opposite Test ('we love our customers' dies), classifies each with a concrete rubric (a third of the market sees it that way, or some customers buy/refuse for it alone), and records the result in STRENGTHS-WEAKNESSES.md (S1, S2, … / W1, W2, …). Load when the user has raw observations and wants to distill them, asks 'what are our real strengths and weaknesses,' or wants to classify what they learned from the honest-look exercise. Do NOT load to gather the raw observations (the previous step), to derive keystones or deal-breakers from classified attributes (later steps), or for a person's individual strengths and weaknesses.
Builds a customer Needs Stack — the ladder in which every need is a means to the end one level up (buy infrastructure → set up a WordPress site → have a personal website → get a book deal). Anchors the level the user's product satisfies, phrased as the customer's own goal in the customer's own words, then walks downward (the steps the product makes obsolete) and upward (what the customer really wants), crystallizing every level — specific wording, a true means-to-an-end link, named real-world occupants — before moving on. Records the stack in NEEDS-STACK.md with the user's level marked and each level's positioning role: what you do, promise, reference as aspiration, or brag about obviating. Load when the user asks what their customer really wants, what level their product operates at, who their real alternatives (not just competitors) are, or to 'build our needs stack.' Do NOT load to rewrite marketing copy from a finished stack — that is a separate positioning task that consumes this file.
Facilitates the third step of a proven ideal-customer (ICP) method: refining classified strengths into keystones — the specific characteristics, behaviors, or circumstances that make a customer NEED an extreme version of a strength, badly enough to drive the purchase alone. Takes a strengths chart (S1, S2, … — file or pasted), walks the strengths one at a time asking who requires an extreme version of each, gates every candidate on naming a real market segment that typifies it (no segment = table stakes, cut), and records survivors in KEYSTONES.md (K1, K2, … with [S] references and example segments). Delivers the strategic verdict when few or none survive: the product isn't compelling yet. Load when the user has classified strengths and asks who needs them, who their target market is, or 'turn our strengths into keystones.' Do NOT load to classify strengths and weaknesses (the previous step), to derive deal-breakers or the anti-market (the next step), or to write the final ideal-customer definition (later).
Facilitates the first step of a proven ideal-customer (ICP) method: gathering raw, honest, specific observations about what a company and product actually are — before any judgment about strengths or weaknesses. Walks the user through twelve unsparing question categories (what customers praise, the complaint with no defense, what separates your most profitable customers, and more) — or processes a team's write-storm notes one observation at a time — and records the results in OBSERVATIONS.md (numbered O1, O2, …), vivid and unevaluated. For a company operating online, it first scans public reviews and press into an External Research section that seeds it. Load when the user wants to figure out their ideal customer, take an honest look at their company, run a strengths-and-weaknesses exercise from scratch, or says 'who is our Carol' or 'what are we actually good at.' Do NOT load to classify observations into strengths and weaknesses (the next step), or for personal self-reflection unrelated to a company.
Interrogates the user to discover who they actually are — what drives them, what drains them, and the natural strengths they can't see because they come so easily — using proven self-discovery prompts (even-as-a-kid, lost-in-the-work, pit-of-my-stomach dread), the anti-questions, and outside-in questions given as homework to people who know them (perfect scenario, personal hell, invisible strengths). Presses every self-flattering label into concrete episodes, welcomes socially unacceptable motives (money, fame, proving a point), and records each finding in WHO-ME.md as a non-judgmental fact — a strength in some contexts, a hindrance in others — with the contexts where it helps and hurts. Load when the user asks who they really are, what work fits them, what drives or drains them, why they keep burning out, or what to build given who they are. Do NOT load to inventory a company's or product's strengths or to distill the one or two decisive personal advantages — the voters step, which consumes this file.
Answer a merchant's **analytics and reporting** questions with **ShopifyQL** — Shopify's query language for aggregated store metrics that the Admin GraphQL API cannot compute. Choose this (not `admin`) whenever the ask is for **numbers, totals, trends, or breakdowns** rather than fetching or mutating individual records: including but not limited to total/gross/net sales and revenue, order counts, average order value, refunds, quantity sold, sessions, conversion rate, and traffic — sliced by product, channel, region, or customer, trended over time, or compared period-over-period. Examples: "total sales last 7 days", "orders by sales channel this month", "top products by revenue", "conversion rate this week", "sales this year vs last year". This topic covers writing the ShopifyQL query; if the merchant wants to run it against their store, execution is handed off to `use-shopify-cli`. Not for general Admin GraphQL record operations — fetching or mutating individual resources (use `admin`).
Modern C# language features for .NET 10 and C# 14. Covers primary constructors, collection expressions, the field keyword, extension members, records, pattern matching, spans, and raw string literals. Load this skill when writing any new C# code, reviewing existing code for modernization, using "modern C#", "C# 14", "primary constructor", "collection expression", "records", "pattern matching", "span", "field keyword", or "extension members". Always loaded as the baseline for all agents.
Refactor Flutter/Dart code to improve maintainability, readability, and performance. This skill applies Dart 3 features like records, patterns, and sealed classes, implements proper state management with Riverpod or BLoC, and uses Freezed for immutable models. It addresses monolithic widgets, missing const constructors, improper BuildContext usage, and deep nesting. Apply when you notice widgets doing too much, performance issues from unnecessary rebuilds, or legacy Dart 2 patterns.