「fea」の検索結果

Advanced AgentDB: QUIC sync, multi-database, hybrid search, MMR, context synthesis.

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。

Gay MCP skill at invocation 25

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。

当用户需要用"先让读者感到痛，再递上解决方案"的方式激发购买欲时，调用此 skill。核心操作是痛苦场景（具体、清晰）+ 严重后果（难以承受）两步法。特别适用于省事型、预防型和治疗型产品。不适用于享乐型/体验型产品（如零食、旅游、娱乐），也不适用于品牌形象广告。关键触发信号：用户提到"怎么让读者意识到问题的严重性""怎么制造紧迫需求""产品解决一个痛点但读者不够重视"。

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。

Adhoc workflow for small targeted changes to existing behavior or appearance, without the full story ceremony. Use when the orchestrator encounters a bug with a clear root cause and surgical solution, OR a small enhancement to something already built (a different icon, wording, spacing, color). Triggers on: 'fix this', 'this is broken', 'the alignment is off', 'that button doesn't work', 'quick fix', 'patch this', 'tweak this', 'change the icon', 'adjust the wording', post-story corrections, or any situation where the write gate blocks a small targeted change. Acts NOW - a defect to file and fix later belongs in /craft:bugs, other deferred work in the notebook. Do NOT use for new features, design exploration, or changes requiring creative spark.

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。

This skill should be used when the user asks to "brainstorm", "explore options", "get creative", "what if we...", or selects "Let's get creative" during story or cycle creation. NOT for a bare vague feature idea where generating options would mean inventing the premise first - that routes to /craft:riff (riff first, options after). Generates 3-4 creative options with trade-offs, visual direction for UI stories, and wireframes.

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。

Routes Python engineering tasks to specialist skills by matching trigger patterns before any architecture, plan, or code is written. Use when working with Typer CLI frameworks, Rich or Textual terminal UIs, CLI UI/UX design, questionary prompts, FastMCP/MCP servers, ty type checker, uv package manager, Hatchling build backend, TOML editing, pre-commit/prek hooks, async Python, PyPI packaging, complex linting, technical debt modernization, testing workflows, feature development, or stdlib-only scripting.

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 building CLI applications with Typer — creating commands, defining arguments and options with enum restrictions, path validation, date and UUID types, composing subcommands, testing with CliRunner, or using advanced features like colored output, progress bars, shell autocompletion, and version callbacks.

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。

Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.

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 implementing a Python feature, adding CLI commands, writing pytest suites, reviewing Python code, debugging, or refactoring. The primary Python engineering workflow orchestrator — classifies the task and delegates through this plugin's own specialist agents (architect → implement → test → review), sized to the task. Delegates to python-cli-architect (implementation), python-pytest-architect (tests), code-reviewer (review), python-cli-design-spec (architecture). Triggers on any Python task requiring specialist agent coordination or multi-agent execution.

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。

Provides agent selection criteria, workflow patterns (TDD, feature addition, code review, refactoring, debugging), quality gates, and python-cli-architect vs stdlib-scripting routing for Python engineering tasks. Activated by python-engineering:orchestrate at Step 1 before any task is routed. Also activates when an orchestrator needs to select the correct Python specialist agent or chain agents across a multi-step Python workflow.

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 executing a task — reads the task and its orchestrator response from the work ledger, loads task-level skills, and implements against acceptance criteria. A dispatch-owned attempt records divergences and the completion report, then closes; a direct invocation returns its task result and material divergence disposition without runner persistence or closure. Triggers on task execution within the implement-feature loop or when an agent picks up a specific task from a plan.

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 creating or updating the project skill discovery config — generates or regenerates .dh/skill_discovery.yaml by scanning the repo to infer tech stack, inventorying installed skills via npx skills list, loading candidate skill content before suggesting, and writing a config-driven skill injection file. Triggers on /dh:setup-skill-discovery invocations and programmatic --auto calls from add-new-feature Phase 3.

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。

Certifies that a feature achieves its original objectives via goal-backward verification (SAM Stage 7). Use when all tasks pass forensic review — starts from expected outcomes, works backwards to verify each was achieved, and returns CERTIFIED or NOT_CERTIFIED with specific gaps.

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。

Audit documentation claims against implementation and authoritative repository evidence. Use when checking whether README, architecture, configuration, API, usage, or feature documentation is stale, incomplete, contradicted by code, missing implemented behavior, or inconsistent with a normative contract.

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。

Register a plan artifact via the MCP backlog server. Use when you produce a document or report that downstream agents or worktree-isolated environments need to retrieve — feature-context, codebase-analysis, architect, T0-baseline, TN-verification, or research artifacts. Triggers include "store an artifact", "register a plan artifact", "write a report to the backlog", "upload artifact 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。

You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or when users request help with ideation, marketing, and strategic planning. Explores user intent, requirements, and design before implementation using research-validated prompt patterns.

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。

When writing a git commit message. When task completes and changes need committing. When project uses semantic-release, commitizen, git-cliff. When choosing between feat/fix/chore/docs types. When indicating breaking changes. When generating changelogs from commit 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。

Pick up open GitHub issues from collaborators and create PRs to solve them. Use when triaging and resolving reported bugs or feature requests.

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。

Triage a GitHub issue filed against skillsaw — classify it (bug, feature, docs, question, other), verify its claims against the codebase, enrich it with missing details, and post one structured triage comment. Use when triaging and labeling incoming issues, not to fix them.

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。

This skill should be used when the user asks to "write a test", "run tests", "add a test for", "test this function", "check test coverage", "run the test suite", or when writing pytest tests for StreamFlow features or bug fixes.

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 an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.

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。

Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.

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。

Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.

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。

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

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。