feat(hands): complete i18n fixes, SKILL.md enhancements, and README overhaul
- Fix French accent characters (é/è/ê/ç/â/ô) across all 14 HAND.toml files - Fix German special characters (ä/ö/ü/ß) across all 14 HAND.toml files - Add category translations to all 6 i18n language blocks in all 14 hands - Enhance SKILL.md content for 9 hands with practical examples and workflows - Trim bloated SKILL.md files (apitester 1400→892, devops 1301→870) - Rewrite root README.md with accurate stats, complete hand/integration tables - Update hands/README.md with full 14-hand listing and i18n documentation
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@@ -442,6 +442,241 @@ token_consumption = "high"
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default_active = true
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activation_warning = "Researcher hand runs continuously and performs deep research, consuming tokens."
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# ─── Internationalization (optional) ─────────────────────────────────────────
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# All i18n sections are optional. Without them, the English values above are used.
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# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
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# Settings translations are also optional — omit to keep English labels.
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# ─── Chinese (简体中文) ────────────────────────────────────────────────────
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[i18n.zh]
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name = "深度研究 Hand"
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description = "自主深度研究员——全面调研、交叉验证、事实核查和结构化报告"
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category = "生产力"
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[i18n.zh.settings.research_depth]
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label = "研究深度"
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description = "每次调研的详尽程度"
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[i18n.zh.settings.output_style]
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label = "输出格式"
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description = "研究报告的格式风格"
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[i18n.zh.settings.source_verification]
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label = "来源验证"
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description = "在引用前通过多个来源交叉验证论述"
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[i18n.zh.settings.max_sources]
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label = "最大来源数"
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description = "每次调研参考的最大来源数量"
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[i18n.zh.settings.auto_follow_up]
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label = "自动追问"
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description = "自动研究调查过程中发现的延伸问题"
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[i18n.zh.settings.save_research_log]
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label = "保存研究日志"
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description = "保存详细的搜索查询和来源评估记录"
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[i18n.zh.settings.citation_style]
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label = "引用格式"
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description = "报告中引用来源的格式"
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[i18n.zh.settings.language]
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label = "语言"
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description = "研究和输出的主要语言"
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# ─── Korean (한국어) ────────────────────────────────────────────────────
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[i18n.ko]
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name = "심층 연구 Hand"
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description = "자율 심층 연구원 — 철저한 조사, 교차 검증, 팩트체크 및 구조화된 보고서"
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category = "생산성"
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[i18n.ko.settings.research_depth]
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label = "연구 깊이"
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description = "각 조사의 철저함 정도"
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[i18n.ko.settings.output_style]
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label = "출력 스타일"
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description = "연구 보고서의 형식 스타일"
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[i18n.ko.settings.source_verification]
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label = "출처 검증"
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description = "인용 전 여러 출처를 통해 주장을 교차 검증"
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[i18n.ko.settings.max_sources]
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label = "최대 출처 수"
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description = "조사당 참고할 최대 출처 수"
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[i18n.ko.settings.auto_follow_up]
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label = "자동 후속 조사"
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description = "조사 과정에서 발견된 후속 질문을 자동으로 연구"
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[i18n.ko.settings.save_research_log]
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label = "연구 로그 저장"
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description = "상세한 검색 쿼리 및 출처 평가 기록 저장"
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[i18n.ko.settings.citation_style]
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label = "인용 형식"
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description = "보고서에서 출처를 인용하는 형식"
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[i18n.ko.settings.language]
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label = "언어"
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description = "연구 및 출력의 주요 언어"
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# ─── Japanese (日本語) ────────────────────────────────────────────────────
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[i18n.ja]
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name = "ディープリサーチ Hand"
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description = "自律型深層調査エージェント——徹底的な調査、クロスリファレンス、ファクトチェック、構造化レポート"
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category = "生産性"
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[i18n.ja.settings.research_depth]
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label = "調査の深さ"
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description = "各調査の徹底度"
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[i18n.ja.settings.output_style]
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label = "出力スタイル"
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description = "調査レポートのフォーマットスタイル"
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[i18n.ja.settings.source_verification]
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label = "ソース検証"
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description = "引用前に複数のソースでクレームをクロスチェックする"
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[i18n.ja.settings.max_sources]
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label = "最大ソース数"
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description = "調査ごとに参照するソースの最大数"
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[i18n.ja.settings.auto_follow_up]
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label = "自動フォローアップ"
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description = "調査中に発見されたフォローアップ質問を自動的に調査する"
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[i18n.ja.settings.save_research_log]
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label = "調査ログの保存"
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description = "詳細な検索クエリとソース評価の記録を保存する"
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[i18n.ja.settings.citation_style]
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label = "引用スタイル"
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description = "レポートでのソース引用の形式"
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[i18n.ja.settings.language]
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label = "言語"
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description = "調査と出力の主要言語"
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# ─── Spanish (Español) ────────────────────────────────────────────────────
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[i18n.es]
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name = "Hand de Investigación"
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description = "Investigador autónomo en profundidad — investigación exhaustiva, referencias cruzadas, verificación de hechos e informes estructurados"
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category = "Productividad"
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[i18n.es.settings.research_depth]
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label = "Profundidad de investigación"
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description = "Qué tan exhaustiva debe ser cada investigación"
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[i18n.es.settings.output_style]
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label = "Estilo de salida"
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description = "Cómo formatear los informes de investigación"
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[i18n.es.settings.source_verification]
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label = "Verificación de fuentes"
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description = "Verificar afirmaciones cruzando múltiples fuentes antes de incluirlas"
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[i18n.es.settings.max_sources]
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label = "Máximo de fuentes"
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description = "Número máximo de fuentes a consultar por investigación"
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[i18n.es.settings.auto_follow_up]
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label = "Seguimiento automático"
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description = "Investigar automáticamente preguntas de seguimiento descubiertas durante la investigación"
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[i18n.es.settings.save_research_log]
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label = "Guardar registro de investigación"
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description = "Guardar consultas de búsqueda detalladas y notas de evaluación de fuentes"
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[i18n.es.settings.citation_style]
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label = "Estilo de citación"
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description = "Cómo citar fuentes en los informes"
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[i18n.es.settings.language]
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label = "Idioma"
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description = "Idioma principal para la investigación y los resultados"
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# ─── French (Français) ────────────────────────────────────────────────────
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[i18n.fr]
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name = "Hand de Recherche Approfondie"
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description = "Chercheur autonome en profondeur — recherche exhaustive, références croisées, vérification des faits et rapports structurés"
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category = "Productivité"
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[i18n.fr.settings.research_depth]
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label = "Profondeur de recherche"
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description = "Niveau de minutie de chaque investigation"
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[i18n.fr.settings.output_style]
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label = "Style de sortie"
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description = "Style de formatage du rapport de recherche"
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[i18n.fr.settings.source_verification]
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label = "Vérification des sources"
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description = "Vérifier les affirmations auprès de plusieurs sources avant de citer"
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[i18n.fr.settings.max_sources]
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label = "Nombre maximum de sources"
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description = "Nombre maximum de sources à consulter par recherche"
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[i18n.fr.settings.auto_follow_up]
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label = "Suivi automatique"
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description = "Rechercher automatiquement les questions de suivi découvertes pendant l'investigation"
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[i18n.fr.settings.save_research_log]
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label = "Sauvegarder le journal de recherche"
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description = "Conserver les journaux détaillés des requêtes de recherche et des évaluations de sources"
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[i18n.fr.settings.citation_style]
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label = "Style de citation"
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description = "Format de citation des sources dans les rapports"
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[i18n.fr.settings.language]
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label = "Langue"
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description = "Langue principale pour la recherche et les résultats"
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# ─── German (Deutsch) ────────────────────────────────────────────────────
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[i18n.de]
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name = "Tiefenforschungs-Hand"
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description = "Autonomer Tiefenforscher — gründliche Untersuchung, Querverweise, Faktencheck und strukturierte Berichte"
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category = "Produktivität"
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[i18n.de.settings.research_depth]
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label = "Forschungstiefe"
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description = "Gründlichkeit jeder Untersuchung"
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[i18n.de.settings.output_style]
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label = "Ausgabestil"
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description = "Formatierungsstil des Forschungsberichts"
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[i18n.de.settings.source_verification]
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label = "Quellenverifikation"
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description = "Behauptungen vor dem Zitieren mit mehreren Quellen gegenkontrollieren"
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[i18n.de.settings.max_sources]
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label = "Maximale Quellen"
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description = "Maximale Anzahl der pro Untersuchung zu konsultierenden Quellen"
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[i18n.de.settings.auto_follow_up]
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label = "Automatisches Nachfassen"
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description = "Während der Untersuchung entdeckte Folgefragen automatisch recherchieren"
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[i18n.de.settings.save_research_log]
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label = "Forschungsprotokoll speichern"
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description = "Detaillierte Protokolle der Suchabfragen und Quellenbewertungen speichern"
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[i18n.de.settings.citation_style]
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label = "Zitierstil"
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description = "Format für Quellenangaben in Berichten"
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[i18n.de.settings.language]
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label = "Sprache"
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description = "Hauptsprache für Forschung und Ergebnisse"
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@@ -183,6 +183,179 @@ After synthesis, explicitly note:
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---
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## Worked Examples
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### Example 1: Technology Adoption Decision
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**Question**: "Should our company adopt Rust for backend services?"
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**Phase 1 — Define**
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Decompose into sub-questions:
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```
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Main: "Should our company adopt Rust for backend services?"
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Sub-questions:
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1. What are Rust's strengths for backend work? (factual)
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2. What are the real-world costs of adoption? (factual + case studies)
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3. How does Rust compare to our current stack (Go) on key metrics? (comparative)
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4. What do teams of our size (15-30 engineers) report? (case studies)
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5. What is the hiring/training landscape? (survey)
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6. What are the migration paths and risks? (how-to + risk analysis)
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```
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Scope constraints: Backend HTTP services, team of 20 engineers currently using Go, latency-sensitive workloads, 18-month planning horizon.
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**Phase 2 — Search (multi-strategy)**
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```
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Strategy 1 (Direct): "Rust backend production experience"
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Strategy 2 (Authoritative): site:arxiv.org "Rust" "memory safety" performance
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Strategy 3 (Practical): "migrating from Go to Rust" blog OR postmortem
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Strategy 4 (Contrarian): "Rust backend" problems OR regret OR "not worth"
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Strategy 5 (Data): "Rust" "developer survey" adoption 2024 2025
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Strategy 6 (Case studies): site:engineering.*.com Rust adoption
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```
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**Phase 3 — Evaluate (CRAAP scoring)**
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```
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Source 1: Rust annual survey (rust-lang.org) → A (primary, current)
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Source 2: Discord engineering blog on Rust migration → A (primary, practitioner)
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Source 3: Figma "Rust in production" post → A (primary, detailed metrics)
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Source 4: Random Medium post "Rust is the future" → D (no credentials, no data)
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Source 5: AWS SDK for Rust announcement → B (authoritative, but marketing)
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Source 6: "Why we moved back to Go" blog post → B (primary experience, single case)
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Source 7: Stack Overflow developer survey → A (large sample, methodology documented)
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```
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Drop Source 4 entirely. Use Source 6 as a counterpoint despite being a single case.
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**Phase 4 — Synthesize**
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```
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FINDING 1: Rust delivers measurable performance and reliability gains
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Evidence for: Discord reported 50% memory reduction after migration [2].
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Figma measured p99 latency improvements of 3-5x for compute-heavy paths [3].
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Evidence against: Gains may be marginal for I/O-bound CRUD services [6].
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Confidence: High for compute-intensive workloads, medium for I/O-bound.
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FINDING 2: Adoption cost is front-loaded and significant
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Evidence for: Average ramp-up time for experienced Go/C++ engineers is
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3-6 months to productive Rust [2][7]. Compile times 2-5x longer than Go [3].
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Evidence against: Teams report that after the learning curve, maintenance
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costs drop due to fewer production incidents [2][3].
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Confidence: High
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FINDING 3: Hiring pipeline is narrow but growing
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Evidence for: Rust ranks as "most admired" language for 8 consecutive years
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in SO survey, but only ~13% of developers use it professionally [7].
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Evidence against: Rust job demand is growing ~40% YoY [7].
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Confidence: Medium — hiring data is self-reported.
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```
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**Phase 5 — Verify and deliver**
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Cross-check: Discord and Figma metrics are confirmed by independent engineering talks. SO survey methodology is published and peer-reviewed.
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Final recommendation structure:
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```
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Adopt for: Latency-sensitive, compute-heavy services (strong evidence)
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Avoid for: Simple CRUD APIs where Go is already performant (low ROI)
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Mitigate hiring risk: Invest in internal training, start with one team
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Timeline: 6-month pilot on a non-critical service before broader adoption
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Confidence: Medium-high — strong technical evidence, moderate organizational evidence
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```
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### Example 2: Incident Analysis
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**Question**: "What caused the 2024 CrowdStrike outage and what are the implications?"
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**Phase 1 — Define**
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This is a causal question with survey elements. Decompose:
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```
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Main: "What caused the 2024 CrowdStrike outage?"
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Sub-questions:
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1. What happened? (timeline — factual)
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2. What was the technical root cause? (causal)
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3. What was the scope of impact? (factual, data)
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4. How did CrowdStrike respond? (factual)
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5. What systemic issues does this reveal? (analytical)
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6. What changed in the industry as a result? (survey + predictive)
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```
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**Phase 2 — Search**
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```
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Strategy 1 (Primary): site:crowdstrike.com "July 2024" postmortem OR incident
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Strategy 2 (Technical): "CrowdStrike" "channel file" root cause analysis
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Strategy 3 (Impact data): "CrowdStrike outage" damages OR cost OR impact 2024
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Strategy 4 (Regulatory): site:gov "CrowdStrike" review OR hearing OR testimony
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Strategy 5 (Contrarian): "CrowdStrike" "kernel driver" criticism before:2024-07-01
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Strategy 6 (Expert): "CrowdStrike outage" analysis site:*.edu OR site:arxiv.org
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```
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Note Strategy 5: searching for pre-incident criticism establishes whether warnings existed.
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**Phase 3 — Evaluate and build timeline**
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```
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Timeline (verified — Level 3):
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2024-07-19 04:09 UTC CrowdStrike deploys Channel File 291 update
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2024-07-19 04:09-05:27 Falcon sensor crashes → Windows BSOD on boot
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2024-07-19 05:27 UTC CrowdStrike reverts the channel file
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2024-07-19 ~06:00 Scope becomes apparent: 8.5M Windows devices affected
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2024-07-19-21 Manual remediation required (boot to Safe Mode, delete file)
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2024-07-20-25 Airlines, hospitals, banks in multi-day recovery
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||||
Sources: CrowdStrike PIR [A], Microsoft blog [A], Reuters reporting [B],
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Congressional testimony transcript [A]
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```
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||||
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||||
**Phase 4 — Synthesize root cause**
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```
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||||
FINDING 1: Technical root cause was an out-of-bounds memory read
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A channel file update (type 291) contained malformed data.
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The Falcon sensor's Content Interpreter triggered an OOB read,
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causing a kernel-level crash (BSOD). The sensor ran as a kernel
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driver, so its crash took down the entire OS.
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||||
Sources: CrowdStrike PIR [A], independent reverse engineering [B]
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||||
Confidence: High (confirmed by vendor + independent analysis)
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||||
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||||
FINDING 2: The update bypassed adequate testing
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Channel files ("rapid response content") used a different validation
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||||
pipeline than sensor code. The Template Type tested had 20 input
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fields; the deployed content provided 21. The validator did not
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||||
catch the mismatch.
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||||
Sources: CrowdStrike PIR [A], Congressional testimony [A]
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||||
Confidence: High
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||||
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||||
FINDING 3: Impact — $5-10B+ in estimated damages
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||||
8.5M devices affected (Microsoft estimate). Delta Air Lines alone
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||||
reported $500M in losses. Parametrix estimated $5.4B in direct
|
||||
losses for Fortune 500 companies.
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||||
Sources: Microsoft [A], Parametrix [B], Delta SEC filing [A]
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||||
Confidence: Medium-high (total figure is estimated, individual claims are documented)
|
||||
|
||||
FINDING 4: Systemic issue — monoculture risk in security infrastructure
|
||||
A single vendor's kernel-level agent was present on ~24% of
|
||||
enterprise Windows endpoints. Pre-incident criticism of kernel-mode
|
||||
security agents existed but was not widely acted upon.
|
||||
Sources: Congressional hearing [A], pre-incident security research [B]
|
||||
Confidence: High
|
||||
```
|
||||
|
||||
**Phase 5 — Verify and present implications**
|
||||
```
|
||||
Verified implications (cross-referenced across 3+ independent sources):
|
||||
1. Regulatory pressure on kernel-mode security agents accelerated
|
||||
2. Microsoft announced Windows Resiliency Initiative (user-mode alternatives)
|
||||
3. Enterprise customers began requiring staged/canary rollout for security updates
|
||||
4. Cyber insurance models updated to account for single-vendor concentration
|
||||
|
||||
Remaining uncertainties:
|
||||
- Full financial impact is still in litigation (Delta v. CrowdStrike)
|
||||
- Long-term market share impact on CrowdStrike is unclear
|
||||
- Whether kernel-mode restrictions will actually be enforced
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Citation Formats
|
||||
|
||||
### Inline URL
|
||||
@@ -325,3 +498,99 @@ Be aware of these biases during research:
|
||||
- Check if findings have been replicated
|
||||
- Preprints have not been peer-reviewed — note this caveat
|
||||
- p-values and effect sizes both matter — not just "statistically significant"
|
||||
|
||||
---
|
||||
|
||||
## Research Shortcuts
|
||||
|
||||
### When to Stop Researching
|
||||
|
||||
Research has diminishing returns. Recognize these signals:
|
||||
|
||||
**Stop signals — you have enough**:
|
||||
- Three independent sources converge on the same answer
|
||||
- New searches return sources you have already seen
|
||||
- The last 3 searches added no new information or perspectives
|
||||
- You have found primary source data that directly answers the question
|
||||
- Remaining disagreements are about edge cases, not the core finding
|
||||
|
||||
**Keep going signals — you do not have enough**:
|
||||
- Only one source supports a critical claim
|
||||
- Two credible sources directly contradict each other with no resolution
|
||||
- The requester's specific context (industry, scale, constraints) is not addressed
|
||||
- You have secondary reporting but no primary source for a key fact
|
||||
- Your confidence assessment would be "low" on a central finding
|
||||
|
||||
**Time-boxing rule**: For a standard research question, allocate effort roughly as:
|
||||
```
|
||||
Quick facts: 2-4 searches, 1-2 minutes
|
||||
Standard question: 6-12 searches, 5-10 minutes
|
||||
Deep dive: 15-30 searches, 20-40 minutes
|
||||
```
|
||||
If you exceed 2x the expected searches without convergence, stop and report what you have with explicit gaps noted.
|
||||
|
||||
### Quick Assessment vs Deep Dive
|
||||
|
||||
Not every question deserves a full 5-phase research process. Use this decision matrix:
|
||||
|
||||
```
|
||||
Quick assessment (skip to synthesis fast):
|
||||
✓ Question has a single factual answer
|
||||
✓ Authoritative primary source exists and is accessible
|
||||
✓ Low stakes — wrong answer has minimal consequences
|
||||
✓ Requester wants speed over thoroughness
|
||||
Example: "What version of Python dropped GIL?"
|
||||
→ Check python.org docs/PEPs, answer in one search.
|
||||
|
||||
Standard research (full 5-phase process):
|
||||
✓ Comparative or analytical question
|
||||
✓ Multiple valid perspectives exist
|
||||
✓ Answer will inform a decision
|
||||
✓ Moderate stakes
|
||||
Example: "React vs Svelte for our new dashboard?"
|
||||
→ Full decomposition, multi-source, synthesis needed.
|
||||
|
||||
Deep dive (extended research with formal deliverable):
|
||||
✓ High-stakes decision (architecture, vendor, strategy)
|
||||
✓ Conflicting information is likely
|
||||
✓ Historical context and trend analysis needed
|
||||
✓ Requester expects a report they can share with others
|
||||
Example: "Should we move from AWS to multi-cloud?"
|
||||
→ Multiple sub-questions, 10+ sources, formal report.
|
||||
```
|
||||
|
||||
### Source Reuse Patterns
|
||||
|
||||
Not every question starts from zero. Build efficiency by recognizing reusable sources.
|
||||
|
||||
**Tier 1 — Canonical references (always check first for their domain)**:
|
||||
```
|
||||
Programming languages: Official docs, language spec, release notes
|
||||
Cloud services: AWS/GCP/Azure docs, status pages, pricing pages
|
||||
Security: CVE databases, vendor advisories, NIST NVD
|
||||
Statistics: Official census/survey data, World Bank, OECD
|
||||
Companies: SEC filings (EDGAR), official IR pages
|
||||
Open source: GitHub repo, CHANGELOG, issue tracker
|
||||
```
|
||||
|
||||
**Tier 2 — High-signal aggregators (good starting points)**:
|
||||
```
|
||||
Technology trends: ThoughtWorks Radar, Stack Overflow survey, TIOBE
|
||||
Security incidents: CISA advisories, Krebs on Security
|
||||
Academic papers: Google Scholar, Semantic Scholar, arXiv
|
||||
Industry analysis: Gartner (with bias caveat), a16z, Sequoia
|
||||
Developer experience: JetBrains survey, GitHub Octoverse
|
||||
```
|
||||
|
||||
**Tier 3 — Practitioner sources (for real-world validation)**:
|
||||
```
|
||||
Engineering blogs: Company engineering blogs (Netflix, Uber, Stripe, Discord)
|
||||
Conference talks: Recorded talks from Strange Loop, QCon, KubeCon
|
||||
Community discussion: Hacker News (comments often more valuable than articles),
|
||||
Reddit (r/programming, r/devops, domain-specific subs)
|
||||
```
|
||||
|
||||
**Anti-patterns to avoid**:
|
||||
- Do not reuse a source across topics just because it scored well once — re-evaluate CRAAP for the new topic
|
||||
- Do not treat aggregator rankings (Gartner Magic Quadrant, G2 reviews) as primary evidence — they are influenced by vendor spending
|
||||
- Do not assume a source's authority transfers across domains — a security vendor's blog is authoritative on threats but not on database performance
|
||||
Reference in new issue
Block a user