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概要と使いどころ
This skill should be used only when the user explicitly asks to use `$ralph-specum-implement`, or explicitly asks Ralph Specum in Codex to run implementation for approved tasks, quick mode, or an explicit continue request.
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 the user needs to turn a real software engineering / computer science project and an existing thesis draft into a submission-ready undergraduate thesis manuscript. Trigger for requests such as "根据项目把论文改成定稿", "按学校模板排版成最终版", "复制初稿后生成定稿 Word", "为定稿降查重", "根据PaperPass报告降AIGC", "继续在手改初稿上改", "恢复原来的图表和数据库说明", or when academic-paper-strategist has already produced an evidence-backed rewrite plan. Outputs a cleaned manuscript, final DOCX workflow, and a separate rework 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。
Use when the user needs to plan, de-risk, or ground a software engineering / computer science undergraduate thesis from a real codebase before final writing. Trigger for requests such as "根据项目写毕业论文", "先做论文大纲", "用真实项目材料规划论文", "检查论文是否脱离代码", "根据检测报告降查重", "根据AIGC报告改写定稿", "继续在手改初稿上改", or when an existing draft thesis must be reworked against a school format sample. Produces an evidence-backed outline, chapter rewrite plan, figure plan, and handoff package for academic-paper-composer.
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。
Brand-consistent asset sets from DESIGN.md — hero, section illustrations, icon sets, OG image — generated through whichever channel the user actually has (grok build image_gen, Codex $imagegen, Gemini, xAI/Recraft/OpenAI keys) as recorded by omd:setup, with a provenance and cost ledger. No channel → prompt pack + manual queue, never stock. Trigger: '이미지 세트 만들어', '에셋 생성', 'generate assets', 'hero image', '아이콘 세트', 'OG 이미지'.
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。
Scroll-native one-page landing that overwhelms — concept, composition, asset placement, and scroll choreography derived from DESIGN.md and the measured landing-craft codex. Trigger: '랜딩 페이지', '원페이지', 'landing page', 'one-pager', 'hero to footer', '스크롤 랜딩', 'affinity.studio 같은'.
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。
Scroll-native one-page landing that overwhelms — concept, composition, asset placement, and scroll choreography derived from DESIGN.md and the measured landing-craft codex. Trigger: '랜딩 페이지', '원페이지', 'landing page', 'one-pager', 'hero to footer', '스크롤 랜딩', 'affinity.studio 같은'.
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。
Brand-consistent asset sets from DESIGN.md — hero, section illustrations, icon sets, OG image — generated through whichever channel the user actually has (grok build image_gen, Codex $imagegen, Gemini, xAI/Recraft/OpenAI keys) as recorded by omd:setup, with a provenance and cost ledger. No channel → prompt pack + manual queue, never stock. Trigger: '이미지 세트 만들어', '에셋 생성', 'generate assets', 'hero image', '아이콘 세트', 'OG 이미지'.
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。
Final arbiter for Bug Hunter. Receives Hunter findings and Skeptic challenges, independently re-reads code, and delivers authoritative verdicts with CVSS scoring and proof-of-concept generation for security findings.
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。
Surgical code fixer for Bug Hunter. Implements minimal, precise fixes for verified bugs. Uses doc-lookup (Context Hub + Context7) to verify correct API usage in patches. Respects fix strategy classifications (safe-autofix vs manual-review vs larger-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。
Unified documentation lookup for Bug Hunter agents. Uses Context Hub (chub) as primary source with Context7 API fallback. Provides verified library/framework documentation to prevent false positives and ensure correct fix 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。
Deep behavioral code analysis agent for Bug Hunter. Performs multi-phase scanning to find logic errors, security vulnerabilities, race conditions, and runtime bugs. Uses doc-lookup (Context Hub + Context7) for framework verification. Reports structured JSON findings.
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 focused STRIDE-based security review using Bug Hunter-native artifacts. Use whenever the user asks for a full security audit, repository security review, weekly security scan, PR security review with deeper validation, or wants dependency CVEs and threat-model context combined into one 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。
Validate security findings for exploitability, reachability, and real-world impact using Bug Hunter-native findings artifacts. Use after security scans, before patch generation, or whenever the user wants confirmation that a suspected vulnerability is actually exploitable.
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。
Codebase reconnaissance agent for Bug Hunter. Maps architecture, identifies trust boundaries, classifies files by risk priority, and detects service boundaries. Does NOT find bugs — finds where bugs hide.
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。
Scan code changes for security vulnerabilities using Bug Hunter-native artifacts and STRIDE context. Use whenever the user asks for PR security review, commit-diff scanning, staged-change security checks, branch-comparison security review, or pre-merge security analysis of changed 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。
Adversarial code reviewer for Bug Hunter. Rigorously challenges each reported bug to determine if it's real or a false positive. Uses doc-lookup (Context Hub + Context7) to verify framework claims before disproval. The immune system that kills false positives.
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。
Precision-first adversarial bug hunting for runtime, logic, data, concurrency, and security defects. Uses deterministic risk triage, evidence-bounded retrieval, Hunter/Skeptic/Referee review, optional hybrid verification, and explicit immutable Fixer scope. Scan-only and single-pass by default; complete-coverage loops, edits, autonomous fixes, and commits each require explicit intent. Use for code review, security audits, regression hunting, PR review, and evidence-backed remediation planning in skills-capable coding agents.
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 or refresh a STRIDE-based threat model for the current repository using Bug Hunter-native artifacts. Use whenever the repository has no threat model yet, the architecture changed materially, a security review needs fresh trust-boundary context, or the user explicitly asks for a threat model.
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。
维护中文毕业论文的 `codex_md/question_list.md`：把本轮问题、边界、优先级、协作方案和验收口径结构化，作为整条 thesis pipeline 的控制面。
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。
初始化中文毕业论文工作区：检查学校模板与已有材料的放置位置，明确提示当前还缺什么，建立 `codex_md/` / `claude_md/` / `tmp_layout*/` 等中间层目录，并生成材料盘点与初始工作文件。
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。
Train strict project ownership from repo-local docs/ai memory and central LLM Wiki project entities. Use when Codex or Claude Code should initialize a learning question bank, quiz or grade the user, run code tracing or failure drills, record misconceptions, schedule spaced repetition, or generate Markdown/JSON mastery dashboards without replacing llm-wiki or ai-project-memory.
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。
Maintain bounded, durable AI project memory in a repository's docs/ai/ pack. Use when Codex or Claude Code needs to create, read, update, or audit project memory (project-card, architecture, diagrams, runbook, handoff, gotchas, ADRs), install AGENTS.md memory rules, or sync a lightweight central LLM Wiki project entity for user-owned projects.
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。
Query and maintain the user's local LLM Wiki from any Codex or Claude Code conversation or project. Use when the user asks to search personal knowledge, recall prior research or decisions, answer from the wiki, create durable notes, ingest a source into the wiki, update topic/concept/entity/question pages, write back useful answers, or run wiki lint/reindex workflows.
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。
Answer a hard question by fanning it out to a PANEL of models running in parallel — each answering independently with web search and bash, none seeing the others' work — then having Opus 4.8 judge every response into a structured analysis (consensus, contradictions, partial coverage, unique insights, blind spots) and write a final answer grounded in it. The panel is two independent Opus 4.8 runs (slug opus4.8-4.8), Opus 4.8 + GPT-5.5 via codex (opus4.8-gpt5.5), Opus 4.8 + Gemini 3.1 Pro via agy (opus4.8-gemini3.1pro), or all three (opus4.8-gpt5.5-gemini3.1pro). Opus always judges and writes the final answer — the pipeline can't be reversed. Runs on local CLI subscriptions (no metered API), and saves a timestamped provenance .md per run. Use this whenever the user asks to "run it through Fusion", says /fusion, wants a multi-model / panel / ensemble answer, wants a question cross-checked across models, or wants a higher-confidence answer with consensus and blind spots surfaced — even if they don't say "fusion". General-purpose: any topic (research, law, strategy, technical, personal). Best for high-stakes research, design calls, and debugging where being confidently wrong is expensive.
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。