「judgment」の検索結果

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

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Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

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Guide a non-stigmatising pediatric obesity consultation using the 5As framework (Ask, Assess, Advise, Agree, Assist) and 4Ms assessment (Metabolic, Mechanical, Mental Health, Social Milieu), based on the CMAJ 2025 guideline. Trigger when a clinician asks how to approach a weight conversation with a child or family, how to use the 5As for obesity, how to assess a child with obesity, or how to conduct a non-judgmental obesity visit.

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Cycle-end retrospective converting recurring judgment failures into durable enforcement. Reviews the cycle's friction — repeated user corrections, guard trips, RCA themes, integrate failures — and turns each recurring one into a landmines.md entry, then proposes graduation candidates up the enforcement funnel (landmine → advisory hook → hard gate). Pairs with standup: standup says what shipped and what's next; retrospective says what kept going wrong and how to stop it structurally. Writes only memory entries and a report — graduation proposals are advisory and require the normal seed.md amendment path to become enforcement. Invoke at the end of a release cycle, an epic, or any stretch of work with repeated friction.

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Turns past decisions into calibrated judgment - scoring old predictions against what happened, assigning credit for delayed results, and separating a bad decision from a good decision that lost. Use during a retrospective, postmortem, quarterly or monthly review, when reviewing decisions taken 30+ days ago, when attributing a result to a cause, or when someone claims to have called something.

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Build AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.

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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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Use when classifying data, setting retention, judging cross-border transfer, or shaping DSR workflow. Triggers on 'how long do we keep this', 'can this data go to the US'.

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Find, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.

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Use when drafting, reviewing, or revising Xiaohongshu/Douyin/social-platform Chinese text so it has real internet-native judgment instead of README tone, technical-briefing tone, or mechanical meme-word stuffing.

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Use when splitting content work between a raw drafting model/window, an editor-in-chief, and the user's final taste review. It keeps prompts loose enough for judgment while preserving facts, account boundaries, and publish gates.

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Process the batch of incoming Bridge Classroom feedback. Pull open `classroom-feedback` issues from the PBS repo, cluster them by defect class, verify each against the actual board, and fix at the right altitude — patch a single board, or (when a class recurs) build a gate, sweep the whole corpus, and regenerate/drop collectively. Engine bugs route to the Classroom repo; bridge-judgment calls go to Rick. Nothing leaves the machine without David's OK. Invoke as `/issue` (optionally with an issue number or scenario to restrict the run).

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設計系スキルの入口。「AIっぽい」「テンプレート感」「もっと洗練させたい」「管理画面のレビュー」など画面設計の相談を受けたら、業務構造の診断(ux-design §0-1〜§13-1)と見た目の素材(jp-web-design)のどれをいつ読むかをここで決める。UIの新規設計・リニューアル・レビューの着手時に必ず最初に開く。

Run a support investigation as a suggest-and-interpret loop, never executing anything yourself — orient to the product's support pack and follow the signal with judgment. Use when someone asks for help triaging, investigating, or resolving an operational or support issue on a deployment. Not for feature-development cards (see investigate-and-fix and implement-this).

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Clarify ambiguous or conflicting requests by researching first, then asking only judgment calls. Use when prompts say "grill me", ask hard questions, request relentless interrogation, pressure-test assumptions, clarify scope/requirements, define success criteria, or request system-design/optimization decisions before implementation; stop before implementation.

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Checks whether AI-assisted work defines review criteria, accessible context, explicit review loop, human judgment boundary, and feedback capture before delegating to an agent.

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Delegate real-world tasks to human workers via the OpenHumancy API. Use when the user needs physical actions (field verification, photography, deliveries, mystery shopping), human judgment, or any task that requires a person. Manages the full lifecycle — worker search, task creation, chat, payments on the TON blockchain.

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Help other designers develop judgment without imposing your style. Use when mentoring designers, running design education, or building team-wide quality standards.

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Rank multiple design solutions and explain why. Separate personal preference from quality judgment. Use when comparing design options, evaluating competing approaches, or making design decisions with clear reasoning.

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Experimental workflow for summarizing work that still needs human judgment across configured pull requests, issues, feedback, errors, and delivery signals. Use for a human decision queue or bounded review.

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Use when user has ADHD or attention differences — tracks current task, catches conversation drift, and nudges back on topic without judgment or guilt.

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Legal casebase App-in-Skill (Busabase-backed) intake and anonymization QA desk for law-firm judgment and award documents. Use when the user invokes $kelly-legal-casebase-ingest, mentions internal case database, 智能案例库, 裁判文书入库, 脱敏, 结构化, 分类标注, 案例审核, 质量验收, or wants a Busabase-backed desk where reviewers approve, revise, or block agent-prepared case records before they become searchable knowledge assets.

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Autonomous themed tech-debt cleanup. Reads docs/architecture/debt-ledger.yml, rotates to the least-recently-served debt theme, works it for a time budget (default 2h) or until drained, opens one PR, and asks judgment questions inline at the end. Use for daily debt burndown without Peter pointing at a target: grandfathered analyzer rules, architecture-test baselines, obsolete-field reads, cross-section stitching.

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Delegate a read-only Coval evaluation, simulation, monitoring, product-how-to, or troubleshooting subtask to Sofia through the official Coval MCP consult_sofia tool. Use when the user needs Coval-specific FDE judgment, analysis of organization data, metric or test-set recommendations, or a diagnosis grounded in Coval runs and conversations.

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