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Calculate and interpret the PREVENT-ASCVD risk score (10-year and 30-year) for primary prevention of atherosclerotic cardiovascular disease per the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers inputs required, risk-category thresholds (low <3%, borderline 3–<5%, intermediate 5–<10%, high ≥10%), when to use the 30-year calculation (adults 30–59), and how PREVENT differs from the older Pooled Cohort Equations (lower risk estimates by 40–50%, race-agnostic). Use when a clinician asks what the 10-year ASCVD risk is, how to calculate PREVENT, PCE vs PREVENT, risk category for this patient, does my patient need a statin, 30-year risk calculation, primary prevention risk assessment. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.

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Cycle-end retrospective converting recurring judgment failures into durable enforcement. Reviews the cycle's friction — repeated user corrections, guard trips, RCA themes, integrate failures — and turns each recurring one into a landmines.md entry, then proposes graduation candidates up the enforcement funnel (landmine → advisory hook → hard gate). Pairs with standup: standup says what shipped and what's next; retrospective says what kept going wrong and how to stop it structurally. Writes only memory entries and a report — graduation proposals are advisory and require the normal seed.md amendment path to become enforcement. Invoke at the end of a release cycle, an epic, or any stretch of work with repeated friction.

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Coordinate complex engineering work across bounded subagents while protecting the main context, preventing edit conflicts, and measuring whether delegation was worthwhile. Use when the user asks to delegate, parallelize, orchestrate, use subagents, fan out a review, or handle a large task with independent workstreams; do not use for small, tightly coupled, or inherently sequential tasks.

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Plan and implement infrastructure, CI/CD, container, deployment, observability, and operational configuration changes with least privilege, staged validation, and rollback awareness. Use for pipelines, infrastructure as code, Kubernetes, containers, release automation, monitoring, and production-readiness work; do not use to perform destructive production actions without explicit authorization.

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Select the smallest useful Codex engineering workflow from this repository's existing skills. Use when a task spans multiple engineering concerns, the user is unsure which skill to invoke, or loading every specialist would waste context; return a recommendation only and do not execute the engineering task.

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Design, implement, and evaluate risk-based software tests across unit, integration, contract, end-to-end, and regression layers. Use when adding tests, reproducing bugs, improving coverage, diagnosing flaky tests, or defining a test strategy; do not use to change production behavior unless the user also requests implementation.

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Assign confidence scores (0-100) to every claim in a response. Helps users understand which parts are verified facts and which are educated guesses. Use when the user needs to know how much to trust each part of the answer.

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How to write JSDoc on @rhinestone/sdk public symbols, which is autogenerated into the published SDK Reference. Use when adding or editing doc comments on entry points, the account instance, actions, or utils in sdk/src.

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Splits scarce time, budget or traffic across competing products, channels, campaigns or variants by treating them as a multi-armed bandit - Thompson sampling over beta posteriors, with optimism for anything not yet tried enough to judge. Use when deciding what to work on next, how to divide a marketing budget, which product to prioritise, whether to kill something that is underperforming, or when picking a winner among test variants.

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Answer questions about the Paseo product and app, including setup, configuration, connectivity, providers, workspaces, updates, logs, and troubleshooting. Use when a user inside Paseo asks how Paseo works, how to configure it, or why something is broken; use the paseo skill instead to operate agents and workspaces through MCP or the CLI.

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Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"

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Legacy. Use only when images (photos, illustrations, screenshots) must reach TypeSafe's Jev (System One) model as text, because the request can't send the `TypeSafe-Danger-Zone-Images` header that lets Jev read images directly. Triggers on "turn this photo into a Jev state", "describe these images for Jev", "decode images for System One", or a Jev project whose decision depends on what a picture shows. Handles 1 to about 100 images; produces a neutral, complete decode and a JSON state, checked against Jev.

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Automate terminal UI (TUI) apps with agent-tui for testing, inspection, demos, and scripted interactions. Use when automating CLI/TUI flows, regression testing terminal apps, verifying interactive behavior, or extracting structured text from terminal UIs. Also use when asked what agent-tui is, how it works, or to demo it. Do not use for web browsers, GUI apps, or non-terminal interfaces.

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Fetch dependency source code to give AI agents deeper implementation context. Use when the agent needs to understand how a library works internally, read source code for a package, fetch implementation details for a dependency, or explore how an npm/PyPI/crates.io package is built. Triggers include "fetch source for", "read the source of", "how does X work internally", "get the implementation of", "opensrc path", or any task requiring access to dependency source code beyond types and docs.

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Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser session.

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Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel.

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Attach a self-contained Excalidraw status diagram to a GitHub epic — read the epic + its sub-issue tree, render boxes-coloured-by-status with dependency arrows to a committed PNG that (a) renders on the GitHub mobile app and (b) has the editable Excalidraw scene embedded inside it, post it as a sticky comment, and round-trip human edits back in from a comment. Use when asked to "diagram an epic", "draw the ticket tree", "show the epic on mobile", "add a status graph to

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Day-to-day operation runbook for nuxt-crouton apps — boot any app/POC locally with auth working, seed and inspect the local database (including the miniflare-vs-.data split that makes seeded data "disappear"), turn on the review overlay / eruda, observe deployed staging (tail, smoke, row counts), and capture screenshots. Use when asked to "run the app", "boot velo/fanfare/a poc locally", "why can't I log in locally", "seed the database", "the seed data doesn't show up", "look at the local db", "check what's on staging", "screenshot the app", or "where do the artifacts land".

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When a bug or regression is reported, the FIRST step — before fixing — is to research how and when it was introduced (git archaeology), then record that finding on the tracking issue/PR. Use the moment a bug, error, broken build, or "this used to work" is reported, before writing a fix. Produces a first-bad-commit (or "not a code regression") note you paste onto the issue.

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Render a crouton collection schema (the field-definition JSON) into a human-readable field table + relationships sketch (HTML + PNG + a terse Markdown table), so a human can sign off on the DATA MODEL before any code is generated. Use when an agent drafts or changes a collection schema (before `crouton config` / `generate_collection`), or when asked to "review a schema", "show the data model", "check these fields".

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Plan, run, and measure an adversary engagement operation using the MITRE Engage framework so that deployed deception is driven by strategy instead of deployed ad hoc. Covers the Engage Matrix (Prepare, Expose, Affect, Elicit, Understand), the 10-Step Operational Process, mapping engagement Activities to the ATT&CK techniques they expose, and defining measurable Goals and Operational Objectives. Use when a team has honeypots, honeytokens, or canary tokens but no coordinating strategy, when leadership asks "should we engage attackers and how", when building a deception/denial program, when writing an adversary engagement operation plan, or when deciding which deception Activities to deploy against a specific threat actor. Keywords: MITRE Engage, adversary engagement, cyber deception strategy, denial and deception, Engage Matrix, EAC, EGO, Expose Affect Elicit, deception program, honeypot strategy, engagement operation.

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Analyze the current repo structure, build system, test setup, and conventions to provide a practical onboarding guide. Use when new to a codebase, joining a project, or wanting to understand how a repo is organized. Triggers on getting started, new to repo, onboard, how does this repo work, repo structure, codebase overview.

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Knowledge base from the FAA System Safety Handbook (Dec 30, 2000). Use for system safety as a specialty within systems engineering: the five-step safety risk management process (mandated by FAA Order 8040.4 and the AMS), the severity-by-likelihood risk matrix and acceptance bands, the Safety Order of Precedence, system-description models (5M, SHEL(L)), the hazard-analysis activities (PHL/PHA/RHA/SSHA/SHA/O&SHA/HHA), integrated system hazard analysis, the analysis techniques (FTA, FMEA/FMECA, Fault Hazard, Common Cause, Sneak Circuit, Energy Trace), closed-loop hazard tracking (Safety Action Record), and the specialty domains the handbook covers — acquisition-lifecycle safety and contracting (OSA/CSA/SSPP/ISSPP), software safety (control-capability ranking, SHCM, DO-178B), test & evaluation safety, facilities safety, commercial launch safety (AST licensing, expected casualty, MPL/DAMP), system safety training, and Operational Risk Management (ORM) with human/organizational factors. Scope limits: this is a year-2000 FAA how-to; it predates the modern FAA/ICAO Safety Management System (SMS) and Just Culture regulatory frameworks, and many cited orders, advisory circulars, and launch-licensing rules have since been restructured. It is FAA/NAS-centric, leans on MIL-STD-882C, and does not define the SE life-cycle processes themselves (use nasa-npr-7123 / dau-se-guidebook) or reproduce third-party standard text (MIL-STD-882, DO-178B, ISO/IEC/IEEE).

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Knowledge base from the DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of truth, and the coordinate→plan→pilot→sustain rollout. A vision-and-policy strategy, deliberately non-prescriptive. Does NOT provide a how-to method, tool tutorials, MBSE/SysML mechanics, or implementation-plan content; thin on metrics, acquisition-policy detail, and step-by-step procedure. Excludes the CAC-gated Digital Engineering Body of Knowledge (DEBoK).

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