'layers' 검색 결과

OCRs scanned or image-only corpora with vision-language models in four phases. Triage inventories the scans, identifies script and language, and shortlists models by script, hardware, and data constraints. Evaluate selects a system by measured CER or WER against ground truth, using born-digital twins, a second reader, and decision rules set in advance. Run builds the production pipeline. Clean removes model artifacts, audits what every cleaning rule deletes, corrects with rule-based and LLM passes, and records provenance. Use when choosing an OCR model for a corpus, measuring OCR accuracy, transcribing scans at scale, or correcting raw OCR. Born-digital documents with text layers go to doc-to-markdown; runs on Leiden's ALICE cluster add vlm-ocr-alice.

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Development harness plugin documentation index. Use when looking up SAM pipeline, backlog lifecycle, SDLC layers, task/plan schema, plan artifacts, quality gates, impact analysis, or dispatch schema documentation.

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Evaluate and iterate on the SDLC Layer Separation Architecture implementation. Runs validation checks (cross-references, doc completeness, layer metadata, integration points), produces a findings report, and supports iterative fixes. Use when validating first-pass implementation, before claiming layer work is complete, or when improving layer docs/schema.

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Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

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Builds an ideal customer profile from evidence: case studies, testimonials, customer logos, solutions pages, reviews and any sales notes you have. Produces market sizing layers, firmographic segments, a champion and an economic buyer persona, a negative ICP, intent signals, customer proof points and a voice-of-customer section in the buyers' own words, every claim sourced. Triggers: "ICP research", "ideal customer profile", "who are our customers", "customer segments", "buyer personas", "who should we target". Run after /company-context. Not for competitor analysis (use /competitor-research) or call transcript patterns (use /win-loss).

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Multi-step reasoning engine for complex analysis and systematic problem solving. Use when: (1) Complex debugging scenarios with multiple layers, (2) Architectural analysis and system design, (3) Problems requiring hypothesis testing and validation, (4) Multi-component failure investigation, (5) Performance bottleneck identification. Triggers: "--think", "--think-hard", "--ultrathink", "analyze step by step", "break down this problem", "systematic analysis". IMPORTANT: Do NOT use for simple single-step tasks.

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Test an uncertain Anabasis v2 path at the smallest real layer that can decide it: one fact from a log or a dry run, a walk of one value across the layers, a replay through this tree's readers, a round or campaign rehearsed on a seed with scripted seats and no Referee, or one live seat from a seed. Use to test a join, revisit a recorded position, size a comparison, or check what a kill or stop leaves. Not for reading a finished run (whole-run-investigation) or starting a paid one (run-management).

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Manage Alibaba Cloud Function Compute (FC) 3.0 using the @alicloud/fc20230330 TypeScript SDK. Use when working with serverless functions on Alibaba Cloud, including function CRUD, invocation, versions, aliases, triggers (HTTP/Timer/OSS/CDN/MNS), async invocation, concurrency and scaling configs, provisioned instances, custom domains, layers, VPC bindings, sessions, and resource tagging. Covers all 67 APIs of the FC 20230330 version.

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Use when developing, reviewing, packaging, debugging, or answering questions about DeepSeek Harness (DSH) plugins — the plugin-based agent harness on vendored Cordis. Applies the official plugin-development constraints (plugin contract, cordis.yml layers, services/events/effects, tool DSL, bundles/profiles) backed by the dsh-plugin-guide knowledge base.

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Progressive Disclosure skill index — efficient skill loading in 3 layers. Layer 1 (index, ~100 tokens) always loaded. Layer 2 (summary, ~300 tokens) loaded on context need. Layer 3 (full SKILL.md) loaded on execution only.

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Generates social media graphics and OG images dynamically via Bannerbear REST API. Manages template modifications, font layers, and signed URL generation for on-the-fly image personalization.

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AgentQL is TinyFish's web automation and extraction toolkit that layers a natural-language query system on top of live sites. Its Python and JavaScript SDKs integrate with Playwright and let agents target data and interface elements without depending on brittle CSS selectors alone.

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Create and manage Architecture Decision Records (ADRs). Use when making architectural changes (refactoring, API versioning, new domain layers, schema changes), when reviewing recent commits for documentation needs, or when the user mentions ADR, architecture decisions, or documenting technical decisions.

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Use when keeping imp's docs and config examples coherent after a change - updating docs/internals/ARCHITECTURE.md / README / docs/GOAL.md / docs/MODELS.md / docs/ROADMAP.md / imp.conf.example / CHANGELOG / MISSION_JOURNAL, or "is this doc stale", "document this change", "the example config is out of date", "the README says X but the code does Y", "add a roadmap ledger row". Do NOT use for layer/frontmatter/lint questions (docs-layers), structural code audits (codebase-audit), measuring perf or refreshing the perf baseline (benchmark-cuda), or the agent's private memory (MEMORY.md).

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Turn a KOL roster + subreddit brand-fit analysis into a deployable creator network. Splits creators into KOL (authority) and KOC (grassroots) layers, matches each to a main + secondary subreddits by real gear/shooting-type ∩ community fit, and emits an account×subreddit deployment_matrix (with a ≤3-per-sub ceiling + no-cross-interaction rule) plus per-creator persona_cards. Route A disclosed-compliance baked into every card (FTC disclosure required on any material connection). Consumes reddit-kol-finder's kol_roster.json natively. READ-ONLY, deterministic, no LLM key needed.

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Use when a change request lands on a project that already has `.hedgehog/` and no build in flight — a finished build being adjusted, or an adopted repo's next piece of work. Triggers on any "change this", "fix this", "add this" on such a project. Sizes the request against the installed core's own layers and routes it to one of three exits: a tweak made and committed here, change-work through `hedgehog intent add` and the core's loop, or a re-plan. Not for a build still in progress — that is the core's loop skill's own job.

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Deep receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.

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Scaffold a new puzzle type across all 10+ pipeline integration points in jigsawR. Creates the core puzzle module, wires it into the unified pipeline (generation, positioning, rendering, adjacency), adds ggpuzzle geom/stat layers, updates DESCRIPTION and config.yml, extends the Shiny app, and creates a comprehensive test suite. Use when adding a completely new puzzle type to the package or following the 10-point integration checklist to ensure nothing is missed end-to-end.

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This skill should be used when the user asks to "make an isometric animation", "build a 2.5D isometric scene", "create an isometric infographic", "animate an exploded diagram", "do an isometric city/stack build", "show stacked layers in iso", "extrude blocks with depth", or "add a gentle camera drift to an iso scene". Covers CSS 3D transforms, SVG iso planes, and Three.js OrthographicCamera for true 3D iso — no build step.

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Expert coding skill for the full MapTiler platform — Cloud REST APIs, MapTiler SDK JS (built on MapLibre GL JS), native mobile SDKs, on-premise infrastructure, and vector tile schemas. USE WHEN the user wants to add a map to a web or mobile app, show locations or routes, display geographic data, build a store locator, create data visualizations on maps, add geocoding or address search, do reverse geocoding, look up elevation, do IP geolocation, fetch weather data, transform coordinates between CRS/EPSG, embed static map images, work with vector tilesets (Planet v4, Buildings, Contours, Outdoor, Ocean, Landcover, Cadastre), write MapLibre style expressions or data-driven styling, render 3D terrain, globe view, or 3D buildings extrusion, use satellite imagery, add markers, popups, heatmaps, or clustering, integrate maps in React, Vue, Svelte, Angular, Next.js, Leaflet, OpenLayers, Cesium, or deck.gl, build native maps on iOS (Swift), Android (Kotlin), Flutter, or React Native, self-host with MapTiler Server or generate tiles with MapTiler Engine CLI, or upload/manage tilesets via the Admin API. Also USE WHEN the user mentions MapLibre GL JS — the SDK extends MapLibre with built-in cloud services, helpers, and session billing.

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Design human-visible, machine-readable website layers for AI assistants, RAG, and agentic search. Use this skill when the user asks for llms.txt, LLM discovery plans, visible TLDR or direct-answer blocks, source trails, entity pages, markdown alternates, AI crawler policy, topic-to-URL maps, assistant-readable public content, or LLM-friendly architecture that must preserve canonical SEO strategy without hidden bot-only facts or duplicate story bodies.

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Audits and optimizes the five global Claude layers (User Preferences, Styles, Global Memory, Skills, MCP Connectors) using the Global Layer Scorecard (six dimensions, anchored 1-5 rubrics). Detects eight cross-layer failure modes and produces evolutionary recommendations (Promotion, Demotion, Codification, Skill Extraction). Use when user says "audit my global setup," "optimize my preferences," "review my Claude configuration," "check my cross-project setup," "are my preferences working," "clean up my global memory," or "what should be in my preferences vs my project." Also use when a user has 3+ Projects and wants to improve their shared foundation. Do NOT use for single-Project audits, Project Memory optimization, or full-stack audits (use rootnode-project-audit, rootnode-memory-optimization, or rootnode-full-stack-audit respectively, if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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Detects seven structural anti-patterns in Claude Projects that cause unpredictable output, ignored instructions, and degraded quality. Diagnoses Monolith, Orphan File, Echo Chamber, Phantom Conversation, Kitchen Sink, Misaligned Hierarchy, and Blurred Layers. Use when user says "what's wrong with my project," "Claude ignores my instructions," "diagnose my project," "why is output inconsistent," "review my project setup." Also trigger on symptom-phrased: "Claude doesn't follow my rules," "my instructions keep getting overridden," "my Project isn't behaving as designed." Use alongside rootnode-project-audit if available for deeper structural analysis. Activate whenever the user describes symptoms of unreliable, inconsistent, or degraded Claude Project output, even if they do not name a specific pattern. Do NOT use when the user's primary request is Memory-layer rebalancing (use rootnode-memory-optimization if available) or scoring a single prompt (use rootnode-prompt-validation if available).

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Use when asked to explain or teach a subsystem, module, pattern, or change in progressive layers from purpose to code depth. Not for design rationale: use why. Not for source or remote-system changes.

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