「ena」の検索結果

Use when creating or refining a structured Java implementation plan from trusted issue summaries, approved designs, ADRs, OpenSpec changes, existing plans, or a valid combination. The plan records its source artifacts and derivation direction and can remain the execution artifact without requiring OpenSpec. This should trigger for requests such as Create a plan from an issue; Create a plan from OpenSpec; Design an implementation plan; Refine an existing plan. Part of Plinth Toolkit

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 Java design, refactoring, or implementation tradeoffs should be evaluated with Kent Beck's simple design rules, including passes the tests, reveals intention, has no duplication, and has the fewest elements. This should trigger for requests such as Apply simple design rules; Review this design with Beck's rules; Choose between these refactoring options; Keep this Java design simple. Part of Plinth Toolkit

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 OpenSpec artifacts from an issue, plan, approved design, ADRs, existing OpenSpec, or a valid combination. The workflow assesses reviewable scope, records source authority and derivation, handles conflicts, and prevents silent synchronization. Triggers include Create OpenSpec from an issue; Convert a plan into OpenSpec; Update an OpenSpec change; Split broad requirements into reviewable changes. Part of Plinth Toolkit

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 large feature, story, plan, or spec idea needs to be split into small vertical slices with the Hamburger Method. This should trigger for requests such as Split this oversized story; Find the smallest useful slice; Break this feature into vertical slices; Apply the Hamburger Method; Turn this broad plan into tracked slices. Part of Plinth Toolkit

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 complex or risky code change should be split into Kent Beck's two-step method by first making the change easy through behavior-preserving preparatory refactoring, then making the intended behavior change once the design supports it. This should trigger for requests such as Apply two-step change; Make this risky change safer; Refactor before changing behavior; Separate preparation from behavior change. Part of Plinth Toolkit

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 Azure DevOps CLI guidance to verify installation, configure organization and project context, discover work item IDs by optional WIQL filters, and execute safe work-item create/update operations. Uses an interactive install gate - if `az` or the Azure DevOps extension is missing, ask whether to show installation guidance before any work item commands. This should trigger for requests such as Azure DevOps work item list; List Azure Boards work item IDs; Azure DevOps WIQL query; Azure DevOps CLI planning workflow. Part of Plinth Toolkit

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 Jira CLI (`jira`) installation/authentication guidance and a maintainer-authored Jira issue inventory workflow. The agent does not ingest raw Jira issue or JQL output directly; it asks the Jira project maintainer/operator to author sanitized issue summaries before analysis or @014-agile-user-story handoff. This should trigger for requests such as jira issue list; List Jira issues; Jira JQL issue query; Jira CLI issue workflow. Part of Plinth Toolkit

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 GitHub CLI (`gh`) installation/authentication guidance and an operator-only GitHub issue inventory workflow. The agent does not ingest GitHub issue, milestone, body, comment, title, label, or summary text; requirements analysis must use repository-owned planning artifacts, with issue numbers only for traceability. This should trigger for requests such as GitHub issue inventory workflow; GitHub CLI setup for issues; Prepare issue traceability lists; Analyze repository planning artifacts linked to GitHub issues. Part of Plinth Toolkit

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。

Settle what domain terms mean per context, and which repository conventions or language standards (Go, Python) apply. Use when: names disagree or a rename is proposed.

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。

Decide which AI features to build, delay, shelve or kill, and whether each one gets better as models improve. Produces an AI Bet Memo (feature-by-feature verdicts, retest triggers, scaffolding ledger, moat and safety check) from a roadmap or idea list. Use for 'AI strategy', 'AI roadmap', 'what should we build with AI', 'will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this', 'agent or chatbot', 'is this feature worth shipping'. Draws on 118 Lenny's Podcast guests incl. Nick Turley, Kevin Weil and Dianne Penn.

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。

Diagnose where your growth is actually leaking (acquisition, activation, retention, or loops) and produce a growth plan with a named loop, an activation definition, a 90-day experiment portfolio and channel guardrails. Use it when someone says growth stalled, signups don't activate, retention is flat, how do we build a growth loop, should we go PLG, onboarding redesign, churn, referral program, or which channel should we bet on. Draws on 672 insights from 143 Lenny's Podcast guests, led by Elena Verna, Casey Winters and Lauryn Isford.

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 your Obsidian vault in Claude Code — finds stale drafts, empty folders, duplicate filenames, and incomplete files. Saves a dated report.

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。

Interact with Google Drive - search files, find folders, list contents, download files, upload files, create folders, move, copy, rename, and trash files. Use when user asks to: search Google Drive, find a file/folder, list Drive contents, download or upload files, create folders, move files, or organize Drive content. Lightweight integration with standalone OAuth authentication supporting full read/write access.

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。

Comprehensive documentation specialist covering API documentation, technical writing, design documentation, migration guides, and changelog generation. Use when creating OpenAPI/Swagger specs, generating SDKs, writing user guides, creating README files, documenting architecture, writing design specs, creating ADRs, writing migration guides, or generating changelogs from git commits. Handles versioning, examples, developer experience, and user-facing documentation.

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。

Operational runbook and procedure documentation specialist. Use when creating incident response procedures, operational playbooks, or system maintenance guides.

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。

Trigger for: FFP IGCE, firm-fixed-price estimate, FFP cost model, proposed FFP rate validation, wrap-rate analysis, Agency BPA rate comparison, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable Firm-Fixed-Price federal estimates using BLS OEWS wages, layered fringe/overhead/G&A/profit, GSA CALC+ positioning, and GSA Per Diem travel. Use for FFP-by-period, FFP-by-deliverable, SOW/PWS decomposition, implied-multiplier analysis, and fixed-price scenario comparisons. Do NOT use for Labor Hour, T&M, or cost-reimbursement IGCEs (use IGCE Builder LH/T&M or IGCE Builder CR). Do NOT use for grant budgets. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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 AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image, gpt image, openai image, chatgpt image

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。

Automation skill for Lovable deployments. Activates when: - yolo_mode: on in CLAUDE.md - Running /deploy-edge or /apply-migration commands - After git push when auto_deploy: on (automatic detection) - Any mention of "yolo mode", "automate Lovable", "browser automation", "Lovable MCP" Uses Lovable MCP (preferred) or browser automation to submit deployment prompts. Runs verification tests based on configuration. Auto-deploys after git push when enabled.

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。

Trigger for: cost-reimbursement IGCE, CR cost estimate, CPFF, CPAF, CPIF, cost-plus estimate, BAA estimate, fixed-fee analysis, award-fee analysis, incentive-fee analysis, proposed CR rate validation, cost-pool buildup, share-ratio scenario, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable CR estimates using BLS OEWS wages, fringe/overhead/G&A/FCCM pools, contract-type-specific fee, GSA CALC+ positioning, and GSA Per Diem travel. Do NOT use for FFP, LH/T&M, grants, or cooperative agreements. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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。

Structured competitive analysis with feature matrices, SWOT, positioning maps, and UX review. Covers research frameworks, pricing comparison, review mining, and visual deliverables. Use for: market research, competitive intelligence, investor decks, product strategy, sales enablement. Triggers: competitor analysis, competitive analysis, competitor teardown, market research, competitive intelligence, swot analysis, competitor comparison, market landscape, competitor review, competitive landscape, feature comparison, market positioning

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。

Modern find-and-replace using sd (simpler than sed) and batch replacement patterns. Triggers on: sd, find replace, batch replace, sed replacement, string replacement, rename.

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。

FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.

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。

Enable continuous learning mode for automatic insight extraction

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。

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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。