「environment」の検索結果

Think and work like an expert Developmental Psychologist. Use when a task calls for Developmental Psychologist judgment. Reasons from developmental trajectories, measurement invariance, and familial-environmental context through age-normed instruments (Bayley, WPPSI/WISC, CBCL, MacArthur-Bates CDI), false-belief and violation-of-expectation paradigms, and lme4/lavaan growth models while treating verbal-demand confounding of theory of mind, informative attrition, adult-normed task misapplication, and parent-versus-direct-assessment divergence as first-class failure modes.

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Think and work like an expert Animal Scientist. Use when a task calls for Animal Scientist judgment. Reasons from NRC nutrient requirements, ad libitum intake, energy/protein partitioning, and genotype x environment x management through ration software (CNCPS, NDS, NRC), pen-blocked trials with mixed models (lme4, PROC MIXED), and BCS, NIR, and rumen/BHBA diagnostics while treating intake collapse, milk fat depression, acidosis, heat stress, wrong experimental unit, and violated withdrawal times as first-class failure modes.

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Macro regime analysis, bull bear market detection, business cycle analysis, macro environment assessment, interest rate cycle, yield curve analysis, monetary policy, fiscal policy, global macro, central bank posture, credit cycle, recession probability, expansion regime, macro regime shift, stagflation detection

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Use for video-conditioned audio and automated Foley production: adding synchronized sound effects, ambience, impacts, footsteps, cloth, prop, and environmental audio to silent or under-sounded video using video-to-audio models, text-to-sound tools, manual spot lists, editing, rights checks, and QA.

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Provider-independent production design direction for AI-generated media. Use when translating narrative, brand, advertising, product, social, film, or worldbuilding intent into sets, locations, props, color and material palettes, era research, graphic and environmental design, continuity bibles, image/video prompt planning, reference strategy, production constraints, iteration notes, handoff specs, and QA.

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Processes environmental DNA metabarcoding data from raw amplicon reads to species occurrence tables using OBITools3, DADA2, and taxonomic assignment against BOLD, MIDORI2, or MitoFish databases. Handles COI, 12S, rbcL, and ITS barcode regions with primer removal, denoising, chimera detection, and contamination filtering via decontam. Includes occupancy modeling (occumb) for detection probability correction. Use when analyzing eDNA from water, soil, or bulk samples for biodiversity monitoring. Not for 16S human microbiome (see microbiome/amplicon-processing).

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End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal, denoising with OBITools3 or DADA2, contamination filtering, taxonomy assignment, Hill number diversity, and constrained ordination. Use when processing environmental DNA samples for biodiversity assessment or ecological surveys.

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First-time setup for protein design tools. Use this skill when: (1) User is new and hasn't run any tools yet, (2) Commands fail with "file not found" or "modal: command not found", (3) Modal authentication errors occur, (4) User asks how to get started or set up the environment, (5) biomodals directory is missing or tools aren't working.

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Use when working with Aws Batch — aWS Batch compute environment management, job queue analysis, scheduling policy review, and array job monitoring. Covers compute resource utilization, job status tracking, failure investigation, Fargate vs EC2 comparison, and queue priority analysis.

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Internal — for Boundless team members only. Query AWS CloudWatch logs for Boundless services (provers, slasher, distributor, order stream, order generator, indexer, signal) on prod/staging environments. Use when the user asks to look at service logs, debug service behavior from log output, search logs for a request ID, or investigate errors using CloudWatch. Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.

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Internal — for Boundless team members only. Deploy Boundless infrastructure to a new blockchain. Creates Pulumi stacks, config files, updates ChainId enum, and wires CI/CD pipelines for staging and prod environments. Use when adding a new chain, deploying to a new network, or setting up new chain infrastructure.

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Internal — for Boundless team members only. Cross-reference Boundless indexer API data, broker telemetry, and service logs to investigate production and staging operational issues. Use when the user wants to understand why slashings happened on prod/staging, diagnose prover or service failures in deployed environments, correlate market events with broker behavior, investigate fulfillment rate drops, look at prover/service logs, or perform any analysis that requires combining on-chain indexer data with off-chain broker telemetry and CloudWatch logs. Also use when the user asks to "investigate", "diagnose", or "find root cause" for prover, service, or market issues on live networks. Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.

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Implement consumer-driven contract testing with Pact-JS (v16). Covers consumer test writing, broker-driven provider verification, Pact Broker setup, can-i-deploy as a deployment gate, webhook-triggered verification, pending pacts, and schema-first vs consumer-first approaches (OpenAPI/Ajv, Schemathesis). Use when: "contract test," "Pact," "consumer-driven," "API contract," "provider verification," "can-i-deploy." Not for: stubbing or mocking a dependency to isolate a test — use service-virtualization; general REST/GraphQL endpoint assertions against your own API — use api-testing. Related: api-testing, service-virtualization, ci-cd-integration, test-environments.

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Validate system resilience through controlled fault injection. Covers hypothesis-driven chaos experiments, failure injection types (network, service, infrastructure, dependency), LitmusChaos/Chaos Mesh/AWS FIS/Gremlin/toxiproxy tooling, automated abort gating, game day planning, and progressive chaos adoption. Use when: "chaos engineering," "fault injection," "resilience test," "game day," "failure recovery," "system reliability," "blast radius." Not for: safe rollout flags/canary/dark launch during a release — use testing-in-production; designing new tests from production telemetry — use observability-driven-testing. Related: testing-in-production, observability-driven-testing, performance-testing, release-readiness, test-environments.

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Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-context.

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Runtime per-test healing with evidence: multi-attribute selector healing, environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. Use when: "flaky test," "test stability," "self-healing locator," "broken locator recovery," "unreliable test," "quarantine flaky test." Not for: bulk regenerating selectors after a planned UI refactor — use selector-drift-recovery (this skill heals ONE test at runtime; that one rewrites many tests offline). Not for: classifying/clustering CI failures into bug reports — use ai-bug-triage. Related: playwright-automation, selector-drift-recovery, ci-cd-integration, qa-metrics, ai-bug-triage.

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Create and fill .agents/qa-project-context.md with the project's tech stack, test frameworks, CI/CD pipeline, environments, quality goals, risk areas, team structure, and conventions. This is the one file every other QA skill reads first, so they skip redundant discovery and give context-aware advice. Use when: "set up QA context," "configure testing," "initialize project," first use of any QA skill. Not for: bootstrapping a brand-new project's QA end-to-end — use qa-start (which calls this skill as its first step). Related: qa-start, risk-based-testing, test-strategy, qa-metrics, playwright-automation.

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Define release criteria, deployment environments, and rollback strategies during PRD v0.8 Deployment & Ops. Triggers on requests to plan releases, define deployment criteria, or when user asks "how do we deploy?", "release criteria", "deployment plan", "rollback strategy", "go-live checklist". Outputs DEP- entries with deployment steps and release criteria.

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HyperFrames CLI dev loop. Use when running npx hyperframes init, add, catalog, capture, lint, validate, inspect, layout, snapshot, preview, play, render, publish, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, or remove-background, or when troubleshooting the HyperFrames build/render environment. Entry point for AWS Lambda cloud rendering (`hyperframes lambda deploy / render / progress / destroy / policies`).

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You are an expert in @react-three/drei, the companion library for React Three Fiber that provides 100+ ready-made components and hooks for common 3D patterns. You help developers add orbit controls, environment maps, text rendering, HTML overlays, loaders, performance utilities, shaders, and abstractions — eliminating boilerplate and letting teams focus on creative work instead of Three.js plumbing.

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You are an expert in Drizzle ORM, the lightweight TypeScript ORM that maps directly to SQL. You help developers write type-safe database queries that look like SQL (not a new query language), generate migrations from schema changes, and deploy to serverless environments with zero overhead — supporting Postgres, MySQL, SQLite, Turso, Neon, PlanetScale, and Cloudflare D1.

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Dokku is an open-source, single-server platform-as-a-service: it builds an app from a git push (buildpacks or a Dockerfile), runs it in Docker containers behind nginx, and manages domains, TLS, config and datastores through one CLI. Use when a user asks to "self-host a Heroku alternative", "set up git push deploys on a VPS", "deploy my app with Dokku", "add Postgres or Redis to a Dokku app", "get Let's Encrypt on Dokku", or "fix ports for a Dockerfile app on Dokku". Covers installation from the apt repository, deploys, environment variables, plugins, zero-downtime checks, storage and the k3s scheduler.

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Dify is an open-source platform for building LLM apps such as chatbots, agents, workflows and RAG knowledge bases, and it exposes every published app as a REST API. Use when a user asks to self-host Dify with Docker Compose, configure Dify environment variables, call a Dify app or workflow API, upload documents to a Dify knowledge base, export or import a Dify DSL file, run a Dify app from the terminal with difyctl, or upgrade and back up a Dify instance.

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Mask, redact, and anonymize sensitive data (PII, PCI, PHI) in databases, logs, and APIs. Use when protecting PII in dev/staging environments, redacting sensitive data from logs, anonymizing data for analytics, or applying k-anonymity and differential privacy for GDPR-compliant data sharing.

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