「progressive-disclosure」の検索結果

Use when the user wants to create, author, write, or design a new Agent Skill (a SKILL.md) — for OpenKnowledge or for their editors — including requests like 'help me write a skill', 'make a skill that…', 'turn this workflow into a skill', or improving an existing skill's triggering and discipline. Also use when capturing reusable agent guidance that should live as an installable skill rather than a one-off prompt. Covers choosing scope (project vs global), authoring inside a plugin or skills-distribution repo (write in the repo's own layout, never install), the SKILL.md frontmatter contract, progressive-disclosure structure, evaluating the skill, and installing it into the user's editors.

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。

Optimizes or restructures CLAUDE.md/AGENTS.md with progressive disclosure and zero information loss. Use when the user asks to optimize, audit, 精简/瘦身/重构, or split instruction files, asks for their best practices (最佳实践), sees Memory files taking a large share of /context, reports rules in instruction files being ignored, or a task starts moving instruction sections. Not for generic task drift unless instruction files are in scope; not for moving memory entries into docs (use claude-code-ops-router).

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。

Generates several distinct, clickable HTML interaction prototypes for one product surface into a Design Board and collects selection/remix feedback before implementation. Use for multiple UI versions, interaction alternatives, progressive-disclosure options, or when styling is fine but hierarchy/workflow/interaction feels off. Not for static visual-style calibration (use design-style-picker) or a settled CSS tweak.

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。

Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape hatches, completion incentives, drop-off measurement. Honest about tutorial-overload (dump everything upfront), skip-friendly-empty (skipped onboarding leads to abandoned product), and earned-progressive-disclosure (right things at the right moments) patterns. Triggers on onboarding wizard, product onboarding, first-run experience, signup flow, activation flow, FRX, time-to-value, ah-ha moment design. Also triggers when activation rates are low, when users skip onboarding and never return, when onboarding flows are being scoped for the first time, or when audience research shows users not finding key features.

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。

Split an oversized skill, CLAUDE.md, or spec into an entry file plus files that load only when they're needed.

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。

Implements hub-and-spoke lazy loading to minimize token usage in large skills. Use when building multi-module skills that need conditional on-demand loading.

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。

Produces a progressive-disclosure overview of unfamiliar code or a pull request's changes with code-overview and publishes the resulting overview to a user-specified Confluence location. Use when the user wants code or a PR explained, oriented, or made sense of AND the overview posted to a Confluence space or page. Requires a configured Atlassian MCP server. Does not produce the overview to a local file only — use code-overview. Does not publish an arbitrary existing markdown file — use markdown-to-confluence. Does not document an already-understood feature to Confluence — use project-documentation-to-confluence. Does not root-cause a bug to Confluence — use investigate-to-confluence. Does not plan or specify a new feature to Confluence — use plan-a-feature-to-confluence. Does not publish to Jira — use work-items-to-jira.

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。

Builds a new Claude Code skill from scratch through a relentless, evidence-based interview that walks the skill's design tree decision-by-decision — entity fit, use cases, name, description, workflow steps, tools, and progressive-disclosure layout — then reviews the finished skill against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new skill or slash command. Does not build an agent or subagent — use agent-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.

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。

Produces a human-readable, progressive-disclosure overview of unfamiliar code or a pull request's changes — why it exists (the real problem it solves or goal it serves for the business or a user), and from there what it does, how it flows, and where to start — so you can get up to speed before working on or reviewing it. Use when you want to understand, get oriented in, make sense of, explain, or get up to speed on a chunk of code, a file, a directory, a symbol, or a PR's changes. Writes the overview to a scratch file and changes no code. Does not review code quality or raise findings — use code-review for auditing changes or post-code-review-to-pr for posting them. Does not produce durable feature or system documentation — use project-documentation. Does not assess architecture or structural risk — use architectural-analysis. Does not diagnose bugs or root-cause failures — use investigate. Does not pace a person through the code one step at a time in conversation — use code-walkthrough.

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。

Creates or improves an Agent Skill by defining a precise trigger boundary, concise procedural instructions, progressive-disclosure references, deterministic helpers, routing evals, and validation evidence. Use when authoring or reviewing SKILL.md, skill metadata, bundled references, or skill packaging. Not for ordinary project documentation or application code.

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。

Generate accurate, runnable quantitative strategy code for the JoinQuant (聚宽) platform. Use this skill when the user asks to write a stock/ETF/futures backtest, paper-trading, or live-trading strategy on JoinQuant — the skill provides progressive-disclosure access to the complete JoinQuant API knowledge base (14 categories, 294KB official docs), 5 production-ready strategy templates (single-stock MA, multi-factor, ETF rotation, momentum, mean-reversion), and a lint tool that catches hallucinated APIs, future-function violations, and unset price-mode/slippage/commission. Chinese-friendly. Code generated by this skill should paste directly into the JoinQuant online editor without modification.

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。

Refactor an over-long or poorly structured SKILL.md by extracting examples to references/EXAMPLES.md, splitting compound procedures, and reorganizing sections for progressive disclosure. Use when a skill exceeds the 500-line CI limit, when code blocks dominate the skill body, when a procedure step contains multiple unrelated operations, or after a content update pushed the skill over the line limit.

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。

Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.

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。

Optimize oversized agent instruction files using progressive disclosure. Handles CLAUDE.md (Claude Code), AGENTS.md (OpenAI Codex / GitHub Copilot agents), and copilot-instructions.md (GitHub Copilot). Auto-detects all three formats in your repo, analyzes content tiers, creates sub-documents using each format's native mechanism, and rewrites the main file with zero information loss. Triggers: optimize CLAUDE.md, AGENTS.md too long, reduce copilot-instructions.md, agent instructions file too large, shrink context file, apply progressive disclosure, instructions file bloated

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。

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).

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。