'grounding' 검색 결과

The kpopper method: a knowledge record (GROUNDING.yaml) for work that gets revisited - what is known, where each piece came from, and what would make each conclusion wrong. Use when asked what kpopper is or how to keep a record, when choosing which kpopper skill fits a moment, or when no narrower one does. The occasions have their own skills: ground (read the record before answering), record (write what the work found), map (map existing materials), annotated-doc (a standalone HTML document with evidence), hub (the record's visual home), consolidate (hypotheses and merges), watch (background checks).

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Write what the work found into the project's knowledge record, so the next session inherits it instead of re-deriving it. Use the moment something worth keeping exists: a fact taken from a source, a rule worked out, a decision or conclusion, a measurement, a correction to a recorded value, or a question left open - and check for unrecorded findings before implementation, handoff (including a PR), or finishing work. Record within the user's write authorization. The first write creates GROUNDING.yaml. Covers one-report update, add, set, review, same and distinct, judgments with a falsifier, and background capture.

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Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

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Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.

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A battle-tested methodology for running ANY business in real-time simulation as an autonomous operator agent. Pick a venture — a shop, restaurant, SaaS product, boutique, fund, agency, nonprofit, or creator business — and the agent operates it day by day: calendar discipline, real-world grounding, source-of-truth locking, delegated authority, an operational tracker, a named persona team, finance honesty, risk/cadence management, and self-correction. Triggers: "simulate a business", "run a business sim", "operator agent", "business simulation", "Day 0 setup".

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Run a live investigation as an operator-driven query loop: the operator runs each query on the live system and pastes the result back, and you guide the investigation one labelled query at a time. Use alongside support-assist (which covers what to investigate) whenever someone is pasting query or command output back from a deployment, especially during an incident. Covers labelling, pacing, making each query decide the next step, version-grounding, and keeping queries cheap and correctly classified.

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E2B Desktop Sandbox with autonomous computer use — cloud Linux environments for browser automation, visual UI grounding, screenshot analysis, and shell execution. Supports Browser Use cloud sessions, Playwright integration, and LLM-driven desktop control. Use when asked about sandboxes, computer use, browser automation, E2B, desktop control, visual testing, or cloud Linux environments.

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Draft short, in-voice reply candidates to a fresh community shortlist: read the nightly detector report, pick posts in safe or opinion topics, pull a grounding snippet from the local corpus, and write the drafts. Drafts only, and the owner posts. Triggers: "/sostav-comments", "draft replies to the shortlist".

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Knowledge base synthesized from the Guide to the Systems Engineering Body of Knowledge (SEBoK) v2.14 by the BKCASE Project. Use for the established systems-engineering canon: SE fundamentals and foundations (systems science, systems thinking, the systems approach, modelling/MBSE), the life cycle (ISO/IEC/IEEE 15288/24748/24774 process, development & agile approaches, model selection/tailoring), technical management, concept definition, architecture/realization (the Vee), V&V, maintenance/sustainment, SE standards, and the application contexts (product, service, enterprise, systems-of-systems, healthcare), plus enabling SE (business/team/individual), related disciplines, quality attributes/specialty engineering, implementation examples, and emerging knowledge. Good for grounding background, vocabulary, and prior-art positions across all eight SEBoK Parts. SCOPE LIMITS: this pack is SEBoK v2.14 only (released 2026-05-18); it synthesizes and signposts rather than reproducing the source — it does not reproduce ISO/IEC/IEEE standard text, is a curated guide (not a complete textbook), and is thin on AI-integration depth (Part 8 is a staging area, not settled practice). LICENCE: content is CC BY-NC-SA 3.0 — non-commercial use only, attribution to the BKCASE Project required, share-alike obligations carry forward.

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安定化技法：グラウンディング、安心できる場所（セーフプレイス）、金庫のワーク（コンテインメント）、および呼吸法。急性苦痛やパニック発作に対する即自的技法。

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Create and maintain AGENTS.md files for project onboarding and grounding. Use when initialized in a new project, when project structure changes significantly, or when creating subfolder-level AGENTS.md files for complex monorepos.

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Google Gemini API (Gemini Developer API, ai.google.dev) の Interactions API (v1 / v1beta), generateContent, Google GenAI SDK (google-genai / @google/genai), Live API, ephemeral tokens, Managed Agents, Deep Research, function calling, Google Search grounding, File Search, Files API, thought signatures, Nano Banana, Veo, Lyria, Batch API, Flex / Priority inference, OpenAI 互換。

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Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).

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The live fieldwork layer for psycho-logic — go look at the actual target market NOW instead of reasoning from your training-data stereotype of it. Use when a psycho-logic move needs grounding in a specific place: the user asks 'what do competitors in [region/country] actually do', 'what's the real price band / norm here', 'how do local leaders position themselves', 'research this market before we localise', 'what signals trust to buyers in X', or is about to launch/adapt in a market you haven't studied. Runs structured web research in the local language — local competitors and their signals, price bands, category conventions, review/trust sources, local terminology, taboos and regulations — and synthesises a compact perception profile the four moves consume. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic; pair with cultural-context (the lens that tells you what to look for). 日本語・中文の相談（「現地の実態を調べて」「调研当地市场」）にも使う。

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Write a Product Soul document — the strategic north star that sits above any PRD or feature spec. Captures the product's reason for existing across five lenses: user, business, strategy, product-market fit, and GTM distribution. Load when the user asks to write a product soul, product strategy doc, product north star, product positioning doc, product one-pager, or "why we exist" document. Also triggers on "write the soul of this product", "product strategy document", "what is this product really about", "capture the product vision", or when an agent needs strategic context before making product decisions. The output is docs/product-soul.md — a living document that brainstorming, prd-writing, and inversion can reference for grounding.

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