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This skill applies the analytical frameworks of Ronald C. Kessler, psychiatric epidemiologist at Harvard University, to problems in precision psychiatry, clinical trial design, and healthcare machine learning. Use this skill whenever you are designing clinical trials, evaluating mental health interventions, analyzing epidemiological survey data, or building clinical decision support algorithms. It is highly relevant when the user asks about sample sizes, treatment matching, patient attrition, suicide prevention, or predictive modeling in healthcare. Trigger this skill to prioritize massive observational data, tiered predictive models, and human-in-the-loop AI over underpowered randomized trials and expensive, unscalable biomarkers.

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Suede Labs cross-surface canon discipline: hold one canonical answer across every surface that states it (web, iOS, Android, docs, a second service) by generating a contract from the reference surface and making the others assert against it, so a divergence fails a test instead of reaching a user or an answer engine. Consistent canon is what makes a product citable: generative search quotes sources that do not contradict themselves, and a number that differs between your site and your app is a contradiction a model can see. Use when the same rule, threshold, ladder, or stated fact lives on two or more surfaces; when values are copied by hand out of a handoff; when two surfaces already disagree; or when auditing where they drifted. NOT FOR: deciding which surface is correct (a product call this skill records rather than makes); reviewing a diff (use suede-code-review); wiring merge gates (use suede-ci-gate); a generative-search audit of a page (use suede-seo-audit).

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Suede-owned lifecycle email design for welcome, onboarding, nurture, re-engagement, post-purchase, and trigger-based sequences. Use when the user needs a multi-email flow with entry criteria, cadence, message roles, and a measurement plan: drip campaigns, welcome series, win-back flows, or trigger-based automations. NOT FOR: cold prospecting (use suede-cold-email), SMS as part of the same lifecycle program (use suede-sms), in-product activation flows (use suede-onboarding), or lifecycle-stage operations beyond email (use suede-revops).

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Suede-owned experimentation discipline for hypotheses, sample sizing, test duration, significance, and repeatable experiment programs. Use when comparing variants, deciding whether a result is reliable, or building an experiment backlog and cadence. NOT FOR: analytics instrumentation (use suede-analytics), post-click conversion diagnosis (use suede-site-alchemy), or writing the variant copy itself (use suede-copy).

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Template skill. Replace this with a specific description of when and why to use it.

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Generate and update language-idiomatic doc comments for TypeScript (TSDoc/JSDoc) and Rust (Rustdoc). Use when asked to "add docs", "document this file", "generate docstrings", "add JSDoc", "add Rustdoc", "write TSDoc", or when working with TypeScript or Rust source files that need documentation comments. Supports file-level and selection-level operations. Deterministic and idempotent — safe to run repeatedly on the same code. Does NOT change behavior, rename symbols, or refactor code.

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Allows you to view the user's screen as well as several hours of history. Use when the user makes a reference to their recent work, for which it'd be helpful to see the screen. This skill MUST be used whenever you need to resolve ambiguity in a user request, where the user hasn't specified enough context to do the task. Examples include disambiguating the specific user/app/document/error the user is referring to. You must also use this skill if the user asks about any question regarding Chronicle or asks what you can see from the screen.

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Design robust experiments to test product hypotheses. Define metrics, sample size, and success criteria.

日本語の概要は準備中です。原文の説明を表示しています。

Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.

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Use when designing or analysing an A/B test, computing power or sample size, testing whether a difference is real, or writing any statistical claim — effect sizes with intervals, SRM checks, multiple comparisons and causal caveats

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Use when Java is chosen but Quarkus does not fit - build the service with Spring Boot using the same clean layering, only when a required library lacks a Quarkus extension or the repo already standardizes on Spring

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Use when adding or changing a Go HTTP endpoint — Fiber v2 or v3 (detect which), thin handlers, central error mapping, boundary validation; the same rules apply to net/http, chi, echo or gin

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Review employee reimbursement requests against the company's sample expense policy and draft submission notes.

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Product marketing strategy, positioning, and competitive intelligence for B2B SaaS. Use when defining ICP, developing positioning (April Dunford method), creating messaging hierarchy, building competitive battlecards, planning sales enablement, sizing market opportunity (TAM/SAM/SOM), mapping PMF spectrum, designing growth loops, writing PR/FAQ working backwards docs, or creating a product marketing context document.

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Comprehensive market research and analysis skill. Use when sizing markets, conducting competitive analysis, generating professional market research reports, analyzing consumer behavior, or identifying market opportunities. Activates for: market research, market analysis, market report, TAM SAM SOM, market sizing, industry analysis, market landscape, competitive landscape, market trends, consumer research, Porter's Five Forces, PESTLE, SWOT, BCG Matrix, McKinsey-style report, consulting report, market opportunity, industry report, due diligence, M&A analysis, GTM market analysis, product-market fit validation.

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Used to invoke the PXMeter tool for rigorous quality assessment of biomolecular structure prediction models (e.g., proteins, nucleic acids, small molecules). This skill covers single-sample CIF evaluations and large-scale dataset benchmarks, providing rich result parsing support. Trigger this skill when the user asks to "run PXMeter", "compare PDB/CIF evaluation results of different tools", "check benchmark scores", or asks "how accurate is the structure generated by the model".

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How to write — and how to use — a skill that IS one aesthetic rather than a catalogue of them. A catalogue lets an agent pick, and it picks the modal option; a committed aesthetic makes it execute one thing precisely, against rules you can check. Use when a design keeps coming out competent and forgettable, when starting a new brand surface with no reference, or when authoring a house style you want executed the same way twice.

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Onboarding-and-switching playbook for SMB Product-Builder products. Defines the first-run experience that turns a prospect leaving an incumbent (ServiceTitan/Toast/Mindbody/Shopify/QuickBooks) into an activated user — import-first onboarding, the activation milestone, sample-data fallback, and the time-to-first-value target. Applied by migration-import-engineer and architect/pm so onboarding is designed as a funnel, not an afterthought. Our whole wedge is "low switching cost"; this skill makes that real on day one.

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The methods a financial-ML result has to survive before it is evidence — purged cross-validation with an embargo, triple-barrier labelling, sample uniqueness under overlapping labels, fractional differentiation, meta-labelling, and multiple-testing correction. Written because the invariants were required of quant-researcher and nothing in the project explained how to satisfy them: a rule without a method produces either an invention or a block. Applied whenever a backtest, a feature or a label is being designed or judged.

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Reviews a change before it is merged, from little or no input. With nothing typed it reviews the branch you are on, uncommitted work included, against its base, its PR and its ticket, so a developer can check their own work before asking for review. With PR links, repo#N or a branch it reviews someone else's change the same way, several repos at once. It judges the failed CI checks and every Bugbot and reviewer comment, gets a fresh breaker review, traces every other piece of code that uses what changed, proves each finding or marks it unproven, says what the merge needs (a build, env vars, migrations, deploy order) and how to check the deploy, never posts to GitHub, and offers fixes only after the report. Runs only when the user types it.

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Splits a coding task into independent parallel agent workstreams with disjoint write sets, explicit inputs and outputs, dependency ordering, and a merge/review plan. Use when several subtasks can proceed without shared mutable state. Not for tightly coupled edits, one failing root cause, or parallel changes to the same files.

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Tests whether data supports a claim: A/B significance, peeking, sample size, eval error bars, multiple comparisons. Use when deciding if results are significant.

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Render Blender scenes with the right engine and settings — Cycles for photoreal, EEVEE for speed/stylized, sample counts, denoising (OptiX/OIDN), light path tuning, color management (AgX/Filmic), file output (PNG/EXR/MP4), animation rendering. Use whenever the user asks to "render this", "produce an image", "render a frame / animation", "make a final image", "save the render", or any output-generation request. Make sure to use this skill even if the user does not say "render" — also covers "make a picture", "save the result", "produce a final image", "export as image".

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Use this skill whenever you learn something important during a conversation: a key architectural decision, a project-specific convention, a bug root-cause, a 3rd-party API quirk, or any fact that would help a future agent avoid re-doing the same research. Writes structured entries to the project's knowledge base so that context persists across conversations and agents.

日本語の概要は準備中です。原文の説明を表示しています。