「make」の検索結果

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。

Create and manage tickets — initiatives, epics, stories, bugs. Use when the user says "create a new initiative", "slice this initiative", "split this epic up", "break this into stories", "incept this epic", "make a ticket", "refine this ticket", "what's ready", "status of a story", "publish ticket changes", "validate these tickets", "set up the ticket store".

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。

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

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 Persian with Persian words. Use when writing, editing, translating or reviewing Persian (Farsi) prose — articles, documentation, posts, essays, UI copy — to replace loanwords with Persian equivalents while leaving technical terminology, proper nouns and code untouched. Also use when the user asks for پارسی سره, سره‌نویسی, فارسی‌سازی, or "make this text more Persian".

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。

LLM-assisted human-in-the-loop review. Make sense of a change, focus attention where it matters, test. Use when the user says "checkpoint", "human review", or "walk me through this change".

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。

Universal context persistence across sessions. Auto-saves decisions, conventions, and progress to .rune/ files. Loads state at session start. Use when any skill makes architectural decisions or establishes patterns that must survive session boundaries.

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。

Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets (with integrated DepMap CRISPR essentiality + gnomAD constraint), PubMed, the Human Protein Atlas (HPA), and ChEMBL tool compounds, then re-ranking by a composite score combining protein localization, druggability, disease genetics, tissue specificity (safety), focus-cell-type expression, CRISPR essentiality, LoF safety constraint, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.

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。

The complete first-timer's guide to launching on Product Hunt. Covers timing, hunter selection, asset prep, launch-day engagement, and post-launch follow-up. Follow @WeiYipei on X for more launch strategies.

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。

Craft CMS 5 PHP coding standards and conventions. ALWAYS load when writing, editing, reviewing, or discussing any PHP in a Craft plugin or module — even small edits. Also when running ECS, PHPStan, or scaffolding with ddev craft make. Covers: PHPDoc blocks (@author, @since, @throws chains), section headers (=========), class organization, naming conventions (services, queue jobs, records, events, enums), defineRules() and validation, beforePrepare() and addSelect(), MemoizableArray, DateTimeHelper vs Carbon, strict_types/declare(strict_types=1), short nullable notation (?string), typed properties, void returns, control flow (early returns, match over switch), CP Twig template conventions, form macros, translations (Craft::t), ECS/PHPStan config, scaffolding commands, and the verification checklist.

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。

Apply cinematic film looks to images/video via the Hance CLI. Use when asked to grade, color, or "make it look like" film.

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 a bite-sized, TDD-driven implementation plan but do NOT have a brainstorm-beagle spec to plan against. quick-plan reconstructs intent from the current conversation, fans out domain-expert exploration subagents across the codebase, and synthesizes the same plan format write-plan produces — without requiring `.beagle/concepts/<slug>/spec.md`. Triggers on: "quick plan", "plan this out", "plan what we just discussed", "turn this into an implementation plan", "plan this without a spec", "I don't have a spec, just plan it", "write-plan but no spec". Make sure to use this skill whenever the user wants an implementation or TDD plan and there is no spec to plan against — even if they just say "plan it" after discussing a feature. Writes to `.beagle/plans/<slug>/plan.md`. If a finalized spec already exists at `.beagle/concepts/<slug>/spec.md`, prefer write-plan. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.

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。

Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: "resolve the open questions", "close the gaps in this spec", "research the open items", "finalize my spec", "make this spec implementation-ready", "answer the TBDs". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwise), proposes answers one at a time for user approval, then rewrites the spec in place so it arrives at planning with no known gaps. Does NOT write code, design implementation, or create plans — it only produces a complete 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。

Make AI coding-agent messages to humans clear and easy to follow. Use for every user-facing update, explanation, review, question, and handoff in this repository, or when the user asks to simplify or rewrite an agent message.

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。

Make AI coding-agent messages to humans clear and easy to follow. Use for every user-facing message in this repository, or when the user asks to simplify or rewrite an agent message.

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 essays, articles, and blog posts in Paul Graham's voice — plain, spoken prose that reasons out loud and lands surprising true claims, not AI filler. Use when asked to write or rewrite something "like Paul Graham", "in PG's style", or to make a piece read like a person thinking on the page rather than generated text. Built from a stylistic model of 18 of his essays (see SOURCES.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。

Drive one AI agent from another through kolu's terminals: spawn a Claude Code / Codex / opencode session in a PTY, prompt it, watch the screen, read the reply, prompt again. PRIMARY PATH: the kolu MCP tools (`lifecycle_create`/`lifecycle_sendInput`, `wait_outputSettled`/ `wait_agentState`, `screen_text`, the `terminals` resource). FALLBACK: the `kolu` CLI — same verbs, same discipline. Triggers on "drive another agent", "agent drives agent", "make Claude drive Codex", or wiring a loop where one coding agent supervises another.

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。

How a downstream app consumes the shared @kolu/surface stack (@kolu/surface · surface-app · surface-remote · surface-mcp) — declaring a typed reactive surface, serving it, consuming it (SolidJS hooks or a CLI), and mirroring a remote surface over ssh. Grounded in the real consumers: kolu, drishti, odu, and the TUIs. Load when wiring a surface server/client/mirror, or reaching for makeSession / a link / the `.use()` hooks. CHANGING the framework is gated separately — `.claude/rules/surface.md` (a paired, CI-green drishti PR pinned to final kolu HEAD).

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 Android VPN/proxy service lifecycle: runtime coordinators, status/telemetry flow, where to make changes. Use when touching service start/stop, handover, or coordinator 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。

Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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。

A C-level strategy advisory board, run as a team of named specialist agents, that takes an executive from a raw strategic question to a defended, board-ready recommendation. Use this skill whenever the user faces a significant business decision or wants strategic analysis — market entry, build-vs-buy, M&A, digital transformation, annual planning, portfolio prioritization, pricing, a big investment, a competitive threat, a turnaround. Trigger on requests like "should we invest in / build / buy / enter…", "develop a strategy for…", "evaluate this opportunity", "make the business case", "stress-test this plan", "run a pre-mortem", or "prepare a board presentation" — in any language, even when the user doesn't say the word "strategy". Also trigger when the user names a board member (Drucker, Porter, Christensen, Graham, Grove, Wack, Taleb, Minto) or asks to convene the board / open a boardroom session.

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。

Use when asked to build a product roadmap, prioritize a backlog, decide what to build next, or sequence a list of feature ideas. Examples: "what should we build next", "prioritize this backlog", "make a roadmap", "RICE score these 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。