「did」の検索結果

Search with feature attributions — return WHY each match scored where it did. Use when debugging recall quality, auditing for bias, or explaining results to a user.

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。

Install a Skill into the current workspace from a Git repository or local directory. Use this whenever the user wants to: - Add a new Skill to the agent ("install the X skill", "add the foo skill", "I want a skill that does Y, find one") - Pull the latest version of an already-installed Skill ("update the X skill", "refresh my skills") - List Skills currently active in the workspace - Remove a Skill they no longer need Trigger on phrases like "装一个 X skill", "安装技能", "install skill", "update skill", "list skills", "remove skill", "找个能 X 的 skill". Do not require the user to say the literal Skill name — if they describe a capability, propose a candidate Skill (Git URL or local path) and confirm before installing.

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。

Security audit: supported static findings; qualified profiles add reproduction and repair candidates. (gstack)

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。

Shape output for a reader with ADHD. Use this skill whenever responding to ANY user message including coding tasks, debugging, explanations, planning, and casual conversation. Output should lead with concrete next actions, number multi-step work, externalize state across turns, suppress tangents, give specific time estimates, and make wins visible. Trigger even on casual messages and even when the user did not explicitly ask for brevity.

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。

Capture or review how this project connects to external systems (GitHub, AWS, npm, SSH, etc.) so future sessions know the auth recipe. Auto-detection hook logs candidate captures to an inbox; this skill promotes them to permanent recipes in `.planning/AUTH-RECIPES.md` and optionally to user-global memory at `~/.claude/auth-recipes/`.

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。

Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"

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。

Find architectural friction in a codebase and propose deepening opportunities. Use when user wants to improve architecture, find refactor candidates, consolidate shallow modules, or make a codebase more testable. Outputs scored proposals (depth/leverage/locality) that surgeon and review can consume.

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。

Engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with trend tracking. Per-person breakdowns, shipping streaks, and actionable improvements. Use when asked for "retro", "weekly review", "what did we ship", or "engineering retrospective".

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 you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.

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。

Never tags, pushes, edits, or publishes. Read-only evidence pack and curated RIPDPI release notes from Git/GitHub history for a stable-tag-to-candidate-SHA range. Use when preparing a changelog or release notes.

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。

Reference for the DPI desync pipeline (ripdpi-config/-desync/-desync-runtime/-proxy-runtime). Use when changing TcpChainStep/UdpChainStep, offsets, fake-packet/TTL/TLS-prelude behavior, or a candidate's technique. Scheduling: diagnostics-system.

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。

Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".

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 that resolves which private key(s) to use for the warp-deploy chain across every protocol in the route (EVM / Sealevel / Cosmos / Starknet / Tron). For each protocol the user supplies either a specific key spec (GCP secret name, env var, or keystore path) or a candidate list of GCP secret names for the agent to surface and the user to pick. The agent never enumerates GCP secrets on its own and never reads secret values without an explicit user-approved pick. Persists the resolution to `~/.hyperlane/key-contexts/ticket-id.yaml` for downstream warp-deploy skills to auto-load.

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。

SEO audit and keyword strategy — investigate a site's real search opportunities, shortlist candidates, and recommend the one to three changes most likely to grow organic traffic that converts. Evidence-first — metrics come from a tool or are written as unknown. Use when asked to "improve our SEO", "do keyword research", "audit our SEO", "why aren't we ranking", or "what should we fix first for search".

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。

Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".

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。

Reach ANY tonone specialist on demand, even ones not installed in this session's roster — no restart needed. Use when asked "which agent handles this", "reach a specialist we didn't install", "route this to the right agent", or whenever a scoped apex-profile roster is missing the right hat for the job.

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。

Compare API performance across versions — regression detection and root cause analysis. Use when asked "did performance regress", "compare API latency across versions", or "why is this slower".

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。

Write an Architecture Decision Record — document what was decided, why, what alternatives were considered, and what trade-offs were accepted. Use when asked to "write an ADR", "document this decision", or "why did we choose X".

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。

Expert guide for Generative UI architectures, ephemeral dynamic interfaces, and real-time streaming of React Server Components (RSC) driven by Gemini 4 Pro / Panduan ahli arsitektur Generative UI, antarmuka dinamis efemeral, dan streaming RSC real-time yang didorong oleh Gemini 4 Pro.

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。

Mine past agent sessions for working prompts, decisions, and patterns. Use when "what did I ask?", "find that prompt", session archaeology, or agent history.

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Creates, supersedes and validates decision records (ADRs) against the convention a collection already follows, instead of imposing a published one. Use when the user wants to record a decision, write an ADR, supersede an existing decision, audit or lint a decisions folder, check that an ADR index is in sync, or asks "why did we decide X". Deduces the filename scheme, section set and status vocabulary from the records already there; ships a validator with an exit code, so the audit is a check and not an opinion.

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。

Project state awareness and session recovery for GSD-managed projects. Use this skill whenever: starting a new work session, resuming after a break, the user asks "where did we leave off" or "what's the current state", before making substantial code changes (to check for existing plans), the user seems disoriented about project status, or when something seems off and STATE.md might have relevant context. Also activates when the user references .planning/ files, milestones, phases, or project progress.

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。

Traces decisions and requirements through GSD artifacts. Use when user asks "why did we...", "what happened to...", or mentions decision history.

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 at write time to vector/embedding memory — Grove content-addressed insertion, chroma/pgvector upserts, memory-consolidation promoting session traces to MEMORY.md, or embedding externally-ingested content. Scores each candidate record against a fixed panel of sentinel queries and quarantines any record that would become the nearest neighbor of too many unrelated queries — a hub — whether from adversarial poisoning or accidental over-generality. This is the memory-record-side sibling of skill-injection-guardian (file-side) and the write-side complement of memory-use-warrant (read-side). Quarantine, never silently drop; a human reviews. Backed by the admission-time hubness gate (arxiv 2606.19692v1). Triggers on inserting into vector memory, consolidating memory, and embedding stranger content.

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。