「ept」の検索結果

AI-PLC Stage 2 Inception。IntentとContextからGoalを分析し、BacklogとSub-Agent Registryを生成する。

Add or refine depth through elevation systems, raised and inset surfaces, layered overlap, and meaningful shadow behavior so interfaces feel structured instead of flat. Use when the user mentions depth, elevation, shadows, layering, flat cards, pressed states, drag feedback, or inset controls.

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Generate 3D models from text or images via 5 providers: Meshy (text-to-3D, texturing, batch), fal.ai Hunyuan3D Pro ($0.225, PBR), WaveSpeedAI Rapid ($0.0225, bulk), Trellis 2 (poly control), and Local Depth (free offline ONNX depth maps + mesh). Includes GLB viewer. Triggers on 3D model, image to 3D, GLB, depth map, text to 3D, texturing, 3D asset, Meshy, Hunyuan.

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Generate Rocaille-style domain warping shaders, mouse-reactive liquid logo effects, depth-map parallax heroes (Astryx-style hover-reactive statues), and stable fluid simulations over live DOM. Four modes — rocaille, liquid-logo, astryx-statue, fluid-dom. Triggers on shader art, depth parallax, hover-reactive depth map, statue hero, fluid sim, liquid logo, WebGL visualization, procedural background, pointer-reactive fluid.

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Extract the conceptual essence of a repository as skills, agents, and teams — the project's roles, procedures, and coordination patterns expressed as agentskills.io-standard definitions. Reads an arbitrary codebase and produces generalized definitions that capture WHAT the project does and WHO operates it, without replicating HOW it does it. Use when onboarding to a new codebase and wanting to understand its conceptual architecture, when bootstrapping an agentic system from an existing project, when studying a project's organizational DNA for cross-pollination, or when creating a skill/agent/team library inspired by a reference implementation.

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NestJS module structure, dependency injection, lifecycle, configuration, exception handling, and logging conventions. Apply when implementing or modifying NestJS backend code. Use this skill to: - Structure feature modules (one feature = one module). - Wire DI correctly (constructor injection, scopes, custom tokens). - Set up ConfigModule, ValidationPipe, exception filters in main.ts. - Use lifecycle hooks for init and graceful shutdown. - Pick the right exception class for each error case. Do NOT use this skill for: - Decorator-specific patterns (see decorator-patterns). - ORM/data access (see nest-data-layer). - GraphQL/WebSockets/Microservices (see nest-advanced). - Testing (see nest-testing).

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Create acceptable use policies for AI tools within organizations. TRIGGERS - Use when user needs help with ai-acceptable-use-policy related tasks.

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Write clear acceptance criteria for features. TRIGGERS - Use when user needs help with acceptance-criteria related tasks.

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Write acceptance speeches for awards and honors. TRIGGERS - Use when user needs help with acceptance-speech related tasks.

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。

Create acceptable use policies. TRIGGERS - Use when user needs help with acceptable-use-policy related tasks.

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Post-review intake closing skill. After an intake is resolved (whether accepted via review/meta PR merge or rejected/deferred at triage/evidence review), run this skill to post standardized, personalized closing comments on the PR (if any) and each intake issue. Comments include: evidence pipeline findings with /trust-appraise TM output, per-row artifact scores, decisions rationale, path-to-promotion/acceptance guidance, and a mandatory badge status note including the universal Powered by Gaia badge. Use after curation completes, before or immediately after merge for accepted intakes, or immediately upon rejection.

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Use when a task needs the judgment of an Art Director — commissioning and choosing between divergent creative concepts for a campaign, triaging client feedback into concept vs. execution notes, pressure-testing a concept against a production budget/timeline before it's locked in, or managing a shoot/production budget across a multi-deliverable campaign.

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Generate a User Acceptance Testing plan with scenarios and sign-off criteria. Use when the user says "UAT plan", "acceptance testing", "user testing plan", "how do we test this with users", "test scenarios for business validation", "sign-off criteria", "go/no-go for release", "acceptance test cases", "validate with stakeholders" - even if they don't explicitly say "UAT".

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Create source-grounded paid-ad campaign concepts, messaging, copy, creative briefs, and production plans from a validated brand profile, campaign objective, platform requirements, and optional audit evidence. Triggers on: campaign brief, campaign concepts, create a campaign, ad concepts, ad copy, ad messaging, creative brief, headlines, descriptions.

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Use this skill when designing the human-executable UAT test cases that prove a Salesforce feature works for a specific persona — the per-persona scripts with preconditions, data setup, permission setup, ordered steps, and pass/fail evidence that close out an RTM row. Trigger keywords: UAT test case salesforce, user acceptance test script salesforce, manual test salesforce feature, UAT script per persona, UAT precondition data setup, UAT permission set assignment for testing, negative path UAT case. NOT for high-level UAT planning, defect classification, regression strategy, or sign-off (use admin/uat-and-acceptance-criteria). NOT for the Given/When/Then technique used to write the AC the case is derived from (use admin/acceptance-criteria-given-when-then). NOT for Apex test method generation (use agents/test-class-generator). NOT for test data factory patterns (use templates/apex/tests/TestDataFactory.cls). NOT for performance or load testing.

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Use when a Lightning record page, home page, or app page is slow to load or render — covers Experienced Page Time (EPT) analysis, component count reduction, progressive disclosure via tabs and conditional rendering, Lightning Experience Insights diagnostics, and DOM/XHR minimization strategies. Triggers: 'Lightning page is slow', 'EPT is high', 'record page takes too long to load', 'too many components on page', 'Lightning Experience Insights shows slow page', 'how to optimize Lightning page performance'. NOT for Visualforce page performance (separate concern). NOT for Apex or SOQL query optimization (use apex/apex-cpu-and-heap-optimization or data/soql-query-optimization). NOT for report or dashboard performance (use admin/report-performance-tuning).

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Author and own ONE system's design document (the A–K SYSTEM GDD) at .unikit/gamedesign/systems/SYS-<slug>.md — the depth layer of the GDD, single-system scope. Create the skeleton and walk the collaborative section-cycle to write its parameters, rules, formulas, and acceptance criteria ("detail the combat system", "write the GDD for the inventory system", "fill in the rest of this system doc", "spec out the parameters of X"), AND revise it after approval under the delta discipline ("raise the damage by 10%", "tune the economy", "rework the status system", "nerf X", "change this rule") — a number change is Tuning, a small rule a Tweak, restructuring a Rework; each bumps the version and logs the delta. To add a NEW system or restructure the system map use /unikit-gd-spec; to edit GAME.md content use /unikit-gd-spec; the moment an edit also touches a second system, a content type, a flow, or GAME.md (two or more zones) use /unikit-gd-apply; to invent a brand-new game concept use /unikit-gd-brainstorm.

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Audit and maintain the learnings in `docs/solutions/` against the current codebase — fix drifted paths and links, merge overlapping docs, replace guidance the code no longer supports, delete obsolete docs, reconcile the `CONCEPTS.md` glossary, then report and commit. Also bootstraps `CONCEPTS.md` on request and, when asked, culls docs whose reasoning the code already explains. Use for "refresh the learnings", "audit docs/solutions", "are our solution docs still accurate", "clean up / prune the learnings", "create a CONCEPTS.md", or after a refactor, rename, migration, or dependency upgrade that likely invalidated documented solutions. Not for capturing a new learning — use `learn-with-docs`.

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Drafts a standalone API Acceptable Use Policy (AUP) for incorporation by reference into a master API license or terms-of-service agreement. Produces a publication-ready template with prohibited-use matrix, developer security checklist, graduated enforcement framework, AI/ML training restrictions, and versioning playbook. Trigger when a user needs to draft or update an API AUP, separate behavioral rules from core API terms, define prohibited API uses, add enforcement or change-management mechanics, or mentions acceptable use policy, developer security requirements, or API enforcement.

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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Recommends contraception when pregnancy is not desired for individuals with diabetes who could become pregnant. Triggered by discussions about pregnancy planning, contraception needs, or when pregnancy is not desired.

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This skill suggests medical therapy to block aberrant hormone receptors for bilateral macronodular adrenal hyperplasia (BMAH) when surgery is not preferred or feasible. Consider this approach in patients with confirmed BMAH and evidence of aberrant hormone receptor expression (e.g., GIP, LH/hCG, serotonin) driving cortisol excess.

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Decide whether a kidney from a brain-dead deceased donor (DBD) with diabetes mellitus and/or hypertension is suitable for transplantation, using the ISOT 2025 Delphi consensus and Table 4 eligibility checklist. Trigger when a clinician asks "can I use a kidney from a diabetic deceased donor", "is this DBD with HTN/DM acceptable", "ECD donor with diabetes — accept or decline", "DBD eligibility for hypertensive donor", or shares a deceased donor with diabetes or hypertension and needs an accept/borderline/decline decision.

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。