「raft」の検索結果

Design decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).

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。

Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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。

Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

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。

Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

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。

Calculate the Maryland Aggregate Pathology Index (MAPI) on a deceased donor procurement kidney biopsy to predict graft survival and decide single vs dual vs discard allocation. Trigger when a clinician asks "what's the MAPI score", "interpret donor kidney biopsy", "should I use this marginal kidney", "MAPI cutoff for transplant", "procurement biopsy scoring", or has histology results from a deceased donor kidney and needs an objective grading.

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。

Standard operating procedure for producing a documentation page end to end — gather source material, classify, draft against the measured corpus profile, then run the reader-level and humanizer passes and both gates. Use for any page on a documentation surface, or when rewriting a page that reads as machine-written. Orchestrates technical-writing, reader-level and humanizer in a fixed order.

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。

Draft a Root Cause Analysis for an incident, outage, or repeated test failure. Unlike intake/spec/brd, RCA is not a workflow phase — it's a standalone postmortem artifact that often precedes a bugfix intake. Output lives at `docs/rca/<slug>.md`.

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。

Draft a Workflow Phase 1 intake document capturing a new request — the problem, the desired outcome, constraints, and testable acceptance criteria. Use when a user brings a new feature, change, or investigation that doesn't yet have a spec. The output lives at `docs/intake/<slug>.md` and feeds `/scout`, `/research`, and `/spec` downstream.

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。

Cross-consistency review of a drafted spec's diagrams. Verifies that C4 components appear in the dependency graph, class-diagram changes have matching DDL, every AC resolves to a concrete sequence, and the dependency graph is acyclic. Read-only. Run after `/spec-lint` passes and before implementation.

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。

PM-mode brainstorm helper. Captures the requirement via Socratic dialogue before any entry phase (`/intake`, `/spec`, `/tdd`) drafts its artifact. Stage 0 skip-check, Stage 1 gap-analysis, Stage 2 probe-loop, Stage 3 confirm-and-persist. Output lives at `docs/brief/<slug>.md`. Never proposes solutions — Stage 2 dialogue discipline is structurally enforced via `discipline.mjs`.

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。

Draft a Business Requirements Document (BRD) for cross-functional or stakeholder-heavy work that needs more structure than an intake. Use after `/intake` when the request spans multiple systems/teams, carries regulatory weight, or needs formal sign-off. Output lives at `docs/brd/<slug>.md`.

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。

Draft a Workflow Phase 4 technical spec from an intake (and optionally a BRD + scout + research memo). The spec defines how the system will change: design (C4 + UML + dependency graph in PlantUML), data, APIs, tests, rollout, rollback. Output lives at `docs/specs/<slug>.md`. Never self-approves — approval happens via `/approve-direction`.

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。

Preflight a spec draft without saving. Runs the same checks as the write-boundary hooks — PlantUML syntax, required diagram presence, AC-to-sequence traceability, Design calls quality floor, and System delta row resolution — and prints a compact pass/fail table. Use while iterating so the hooks don't bite on save.

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。

Dev-only check that a drafted spec for THIS baseline repo won't ship dev-tree references to consumer installs. Catches three failure modes — shipped SKILL.md prose that references paths under `src/`, `tests/`, `scripts/`, `obj/` as runtime invocations (in ```bash fences``` OR `inline backticks`, plus shipped `.mjs`/`.js`/`.sh`/`.py` helper-file imports); new Python helpers added under `.claude/skills/<slug>/` (shipped helpers must be `.sh` or `.mjs`/`.js` going forward); and imports of modules that aren't in `obj/template/.claude/manifest.json` (consumer won't have the file). The aggregate scanner (`scan-shipped-skills.mjs`) walks only baseline-owned skill dirs (via `owner: baseline` frontmatter) at top level — `references/` and other subdirs are documentation, not runtime. BLOCKER findings hard-block implementation entry; ADVISORY surfaces but doesn't block. Read-only — surfaces a punch list; maintainer edits the spec and re-runs until CLEAN.

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。

Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.

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。

他の開発者のGitHub PRを下読みしてレビューコメント案を作り、人間が承認した内容だけを保留(PENDING)レビュー経由で投稿する。自作PRの修正ループには codex-pr-review-loop / claude-pr-review-loop を使う。

Rewrite AI-sounding prose so it reads as considered human writing — cut the tells (em-dash pile-ups, "it's not X, it's Y" antitheses, inflated vocabulary, uniform rhythm, empty uplift) while preserving every fact, claim, and the author's own voice. Use when the user says "make this not sound like AI", "de-AI this", "humanize/humanise this", "this reads like ChatGPT", "remove the AI tells", "it sounds robotic/generic", or shares a draft (post, email, essay, docs, cover letter) to sharpen into a more human voice. Ships a stdlib scanner that flags the mechanical tells.

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 foreign-trade operator already has a small customer list and needs conservative customer grouping, outreach angles, English subject lines, first-email drafts, follow-up cadence, and human review points before any sending happens.

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。

OpenClaw-native SIEGER-aligned customer intelligence. Use a structured evidence bundle to produce a bilingual report with claim-level evidence, decision gates, industry-specific analysis, and proposed sales angles that require explicit approval before email drafting.

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。

OpenClaw-native version of the foreign-trade outreach email skill. Use structured operator input plus customer-intel source context to generate conservative first-touch or follow-up email drafts without overstating inferred facts or inventing recent customer events.

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。

Run or resume a deliverable active-outreach workflow across lead discovery, screening, customer intel, and outreach email. Use when an operator or system pipeline needs durable run state, visible intermediate artifacts, evidence gates, report-bound human approval, and a final editable email draft that is never auto-sent.

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。

Build bilingual, evidence-backed, SIEGER v2 customer intelligence reports for foreign-trade leads. Use when OpenClaw or a sales operator needs claim-level citations, decision gates, an industry-specific analysis lens, a non-fabricated verdict card, and sales angles that require approval before downstream outreach drafting.

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。

维护每个功能一份给人看的「需求设计文档」（docs/modules 下，一个功能一份）：待拍板置顶，按需写背景、目标、流程、布局、状态、交互细节、业务规则、召回、AI 生成（prompt）、效果、成本、系统，先图后字，只写当前设计，末尾记关键改动，附出图与上线前检查。新建、更新、同步、补充这份文档都用它，别直接手改——它有固定写法和检查。用户说「出设计文档」「PRD 太长看不懂」「把设计文档同步一下」「重拍截图」「上线前看文档跟上没有」时用；用户抱怨看不到功能现在的设计、不知道最近改了什么时也用；forge 各阶段改了规则、文案、交互、布局、阈值、prompt、模型、架构后收尾也用，哪怕用户没明说。不处理写给 AI 的 PRD/DESIGN（forge-prd、forge-design）、骨架上定布局（forge-design-lab）、改 prompt 本身（prompt-craft）、进度（forge-status）。

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。

Draft release summaries, changelog summaries, version PR notes, and changeset-friendly package release notes for npm packages or monorepos. Use this whenever the user asks to write a release summary, 发版说明, release notes, changelog entry, version PR summary, package publish notes, or wants the agent to summarize what will be released from git history, diffs, or changesets, especially for ui libraries, design systems, and multi-package workspaces.

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。