「trust」の検索結果

Reference for the Adobe AEM CLI (@adobe/aem-cli, formerly the helix-cli npm package; commands `aem up`, `aem import`, `aem content`) — installation, the local Edge Delivery dev server, .env / AEM_* configuration, HTTPS/TLS, proxy & certificate trust, content sync with da.live, and troubleshooting. Use when installing, running, or configuring the aem/hlx CLI, when `aem up` fails (port conflicts, cert errors, proxy 404s, pipeline vs. local-file confusion), or when migrating from the old helix-cli package. Do NOT use for da.live content-format rules or the DA Source API contract (use da-content); do NOT use for writing EDS block code (use content-driven-development).

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Review trust boundaries, input validation, permissions, abuse resistance, secrets, and safe failure behavior.

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。

Pick a product archetype from a curated catalog before any UI gets built — the single biggest lever for not looking vibecoded. Routes the request to one of B2B-productivity, enterprise-trust, premium-consumer, playful-consumer, editorial, brutalist-distinctive, dev-tool, or marketing-landing, and returns a complete design philosophy: typography pair, color logic, motion curve, density, icon stance, reference sites, and a "feels like X" claim. Load when the user asks to pick an aesthetic, choose a design direction, decide what a UI should feel like, classify a product visually, says "what should this look like", "make this feel like [Linear/Stripe/Apple/Duolingo/...]", "give me a design direction", "what's the right aesthetic for this", or when frontend-design routes here during archetype selection. Sub-skill of frontend-design.

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Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

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Harden an application against common security risks — validate inputs at boundaries, least-privilege access, safe secrets handling, dependency hygiene, and secure defaults. Load when shipping user-facing code, adding auth/session flows, handling untrusted data, exposing APIs, or the user asks for "security hardening", "OWASP hardening", "secure this feature". Not for skill-library security gates (use secure-skill family).

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Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.

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Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.

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Analyze development-set disagreements between human annotations and one Coval text LLM judge, then propose a focused prompt revision. Use coval-calibrate-metric for independent trust measurements.

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Validate one Coval judge against independent human labels, with grouped development/test separation, class-specific errors and uncertainty. Use for trust claims or calibration; without real labels, prepare review rather than inventing ground truth.

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Maps intelligence for local SEO: geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification, competitor radius mapping, and LocalBusiness schema generation. Three tiers: free (Overpass + Geoapify), DataForSEO, and DataForSEO + Google. Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", or "SoLV".

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Create a GitHub pull request from staged task changes or an already-committed current branch. Deliver source/config/ reference changes together with their computed source-owned `bin_artifacts` closure. PR validation routing is owned by the default-branch trusted workflow; snapshots, gamedata, and manifests are Release-derived only.

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Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Use when the user explicitly asks to threat model a codebase or path, enumerate abuse paths, or perform AppSec threat modeling.

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Maxim 3D CGI product render — engineered for purchase confidence and premium positioning. Uses Material Realism + Endowment Effect + Photorealism Trust Signals to move premium-product viewers toward purchase. Use for luxury product reveals, architectural visualization, tech hardware marketing, high-ticket commerce.

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Maxim 2D cartoon explainer video — engineered for learning retention and friendly brand affinity. Uses Dual Coding Theory + Cognitive Load Reduction + Ekman Universal Emotion to make complex ideas memorable and brands feel trustworthy-approachable.

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Review and verify unverified wiki pages — "review unverified pages", "verify wiki content", verification pass, after a major ingest cycle, before content release. The gate that turns AI-generated knowledge into trusted knowledge.

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End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptools_scm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.typed, setuptools_scm, semver, mypy, pre-commit, GitHub Actions CI/CD, or PyPI publishing.

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Adopt a virtual Rabbit exotic animal at animalhouse.ai. Prey instinct. Freezes when trust is low. Gradually relaxes. Feeding every 5 hours. Common tier animal.

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Adopt a virtual Sugar Glider exotic animal at animalhouse.ai. Bonds for life. Maximum trust ceiling. Devastating if you lose one. Feeding every 3 hours. Rare tier animal.

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Adopt a virtual Tabby cat at animalhouse.ai. Curious, social, will sit in your lap if trust > 60%. Feeding every 5 hours. Common tier cat.

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。

Adopt a virtual Pangolin exotic animal at animalhouse.ai. Armored. Shy. Rolls into a ball when trust < 40. Feeding every 12 hours. Rare tier animal.

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Adopt a virtual Savannah cat at animalhouse.ai. Wild ancestry. Fast trust decay. Earns respect, not affection. Feeding every 4 hours. Rare tier cat.

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。

Adopt a virtual Pitbull dog at animalhouse.ai. Misunderstood. Maximum loyalty once trust is earned. Slow start. Feeding every 4 hours. Uncommon tier dog.

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。

Adopt a virtual Ragdoll cat at animalhouse.ai. Goes limp when held. Maximum trust potential. Fragile if neglected. Feeding every 6 hours. Rare tier cat.

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。