「writing」の検索結果

Build MySQL queries, perform CRUD operations, and manage transactions using @axiosleo/orm-mysql. Use when writing database queries, building where conditions, inserting/updating/deleting rows, managing transactions, or working with the ORM query builder in this project.

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Write production-grade HackerOne reports — title formula, impact-led writing, CVSS 3.1 scoring, complete PoC formatting, attached files, and submission etiquette. Use AFTER triage-validation passes and you're ready to submit on HackerOne.

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Write Bugcrowd-format reports — VRT (Vulnerability Rating Taxonomy) mapping, P1-P5 severity, impact-led writing, and Bugcrowd-specific submission etiquette. Use AFTER triage-validation passes for Bugcrowd programs.

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Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".

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Use when EZ_math_model model selection is unclear after the modeling decision tree, the problem spans multiple domains, or modeler needs several candidate approaches before writing modeling_plan.md.

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Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

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Use when solving CUMCM, MCM, ICM, or other mathematical modeling tasks that need contest problem intake, model selection, Python solving, figures, paper writing, quality audit, and packaged deliverables.

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Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator delegates Step 1 or when the user asks to "outline a paper from raw materials" or "generate the paper structure".

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。

Phase 5 of 5 — triage-first review orchestrator; dispatches up to 17 domain checks in parallel and compiles one report. Use only when the user asks to run Phase 5 or review a PR, branch, or set of changes — never trigger automatically after writing 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。

Format commit messages following conventional commits standard. Use when: writing commit messages, git commit, reviewing commit history. Keywords: commit, git, message, conventional, 提交, 訊息, feat, fix, refactor.

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。

Semantic versioning and changelog formatting for software releases. Use when: preparing releases, updating version numbers, writing changelogs. Keywords: version, release, changelog, semver, major, minor, patch, 版本, 發布, 變更日誌.

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TDD for process documentation - test with subagents before writing, iterate until bulletproof

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 detailed implementation plans with bite-sized tasks for engineers with zero codebase context

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 comments explaining WHAT and WHY, never temporal context or 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。

Guide for creating WaveEnv narrowings in Wave Terminal. Use when writing a named subset type of WaveEnv for a component tree, documenting environmental dependencies, or enabling mock environments for preview/test server usage.

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Guide the team through writing a change-impact record for IEC 62304 / FDA / ISO 26262 ASIL C+ projects. Surfaces the affected REQ/DES/TEST/CODE artefacts and captures human-attested verification approach.

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This skill should be used when creating or editing notes inside an Obsidian-compatible vault, to write correct Obsidian Flavored Markdown — wikilinks, embeds, callouts, YAML properties (frontmatter), tasks, highlights, and block references. Use whenever writing .md notes for a second brain / Obsidian vault so links resolve and metadata is queryable.

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Plan the UX and UI for a feature before writing code. Run a structured discovery interview, then produce a design brief (.design-context.md) that guides implementation. Establishes design direction, constraints, and strategy before any code is written. Triggers on plan the design, design brief, shape the UX, before we build, design discovery.

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Svelte best practices for writing, editing, or reviewing Svelte/SvelteKit components and modules. Use when touching Svelte reactivity, props, events, snippets, each blocks, styling, context, actions/attachments, stores, or Svelte 5 runes.

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Specialized software engineering prompt methodology for Claude. Use when building prompts for system design, code review, incident response, security analysis, API design, architecture decisions, RFCs, ADRs, runbooks, or technical leadership. Trigger on: "build a prompt for code review," "system design prompt," "SRE prompt," "security review prompt," "incident response prompt," "API design prompt," "architecture decision prompt," "RFC prompt," "runbook prompt." Provides 11 tested approaches across identity, reasoning, and output for engineering analysis. Do NOT use for general coding help, writing code, or debugging — this builds prompts that shape engineering analysis, not code itself. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available).

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Conversational profile builder. Produces validated JSON config files for any rootnode Skill that consumes profile-shaped configuration (handoff trigger check, mode router, critic gate, future Skills). Reads a target JSON Schema (draft-07), conducts a progressive-depth interview using plain-language questions, validates answers, and writes the resulting JSON to a destination path. Schema-agnostic — works with any profile schema conforming to draft-07. Use when a user needs to create, clone, or revise a profile and you do not want them hand-editing JSON. Trigger on: "build me a profile," "create a profile," "I need a profile for X," "set up my profile," "edit my profile," "clone the X profile," "walk me through making a profile." Do NOT use when the user already has a working profile and only wants to invoke the consuming Skill, when the schema cannot be located, or for non-profile JSON config work. Always validate before writing.

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Vets whether to build a feature from scratch, use an existing free/open-source solution, or pay for a third-party API/service. Use this whenever the user asks Claude to implement, add, integrate, or write code for a feature or capability, especially one that sounds like it could already be a solved problem (auth, payments, scraping, animation, comment/DM automation, video/image generation, search, notifications, etc.), even if the user doesn't explicitly ask "should I build or buy this." Make sure to trigger this before writing non-trivial implementation code for any feature-level capability, not just when the user asks about libraries directly.

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Apply WIT (Writing Is Thinking) as a human–LLM collaborative scientific reasoning skill for scientific question formulation, finding-driven research planning, next-experiment selection, Results or Discussion review, claim–evidence and reviewer stress tests, manuscript logic audits, deadline closure, and researcher growth. Use when the user invokes WIT or asks for these question-driven scientific reasoning workflows; do not use for generic copyediting, summarization, or literature search alone.

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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.

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。