置信度校准(元认知监控)。追踪L1文档和L2碎片中「AI生成/推断」的内容是否事后得到了验证,定期向用户提问「这段内容当时是AI推断,现在有没有实际证据?」。触发词:「验证历史内容」「校准知识置信度」「哪些内容还没验证」「cognitive-calibrate」。也被 cognitive-consistency-check 内部调用(月度维护)。
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Skills that do not fit the categories above (12.2K skills)
置信度校准(元认知监控)。追踪L1文档和L2碎片中「AI生成/推断」的内容是否事后得到了验证,定期向用户提问「这段内容当时是AI推断,现在有没有实际证据?」。触发词:「验证历史内容」「校准知识置信度」「哪些内容还没验证」「cognitive-calibrate」。也被 cognitive-consistency-check 内部调用(月度维护)。
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联想激活(启动效应等效)。不被用户直接触发,作为「能力层工具函数」由其他Skill调用。给定一个概念,在知识图谱中激活语义相邻的概念网络,返回Top-5邻居摘要给调用方。被 cognitive-ask Step 2c 和 cognitive-capture-fragment Step 2.5 调用。
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检测L1文档之间或文档内部的矛盾,基于L1.5原则提出消解方案。触发词:「矛盾」「不一致」「这里有问题」「检查一致性」「[文档A]和[文档B]是否一致」「有没有冲突」「逻辑漏洞」。
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为L1文档创建新的x.0版本快照,标志一次重大变更的里程碑。触发词:「创建新版本」「打一个快照」「这是v2.0了」「这次改动很大」「记录一下这个版本」。
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捕捉碎片化思考,结构化写入认知结构L2层。触发词:「碎片」「记录一下」「想法」「新洞见」「我发现」「我觉得」「随手记」「想记一个」。
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基于用户自己的认知文档回答问题,严格引用来源,标注置信度,暴露矛盾,识别知识空白。触发词:「基于我的文档」「根据我的想法」「我之前怎么想的」「我说过什么关于X」「我对X的看法是什么」「帮我回忆」「我的认知里怎么说」「我的框架里有没有」「从我的角度」「我如何看待」。
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受控更新L1系统性文档:含影响范围预分析、历史版本备份、矛盾检测、级联写入。触发词:「更新[文档名]」「完善[文档名]」「修改[文档名]」「我要改[内容]」「在[文档]里加上」「[文档]需要更新」。
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引导用户进行自我认知反思,结构化写入自我反思记录,并与历史模式做跨时间比对。触发词:「反思」「我发现我有个习惯」「我注意到」「我又犯了」「这个模式」「我有个问题」「感觉自己」。
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记忆巩固(Headless,睡眠期巩固等效)。不被用户直接触发,由后台调度脚本每7天执行一次。将积压的成熟L2碎片批量整合进L1,conflict类碎片记录到待完成清单由用户决策。触发词(人工触发时):「执行记忆巩固」「批量整合积压碎片」「cognitive-consolidate」。
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Divides a lead list into useful commercial segments. Use when the user provides a combined lead list and wants segments with criteria, sizes, recommended treatment, and priority.
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Converts inconsistent job titles into standardized role, department, and seniority labels. Use when the user provides records with free-text titles and wants consistent role family, department, and seniority fields.
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Ranks companies or accounts by strategic relevance and opportunity. Use when the user provides accounts and wants a priority score, tier, rationale, risks, and next action.
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Assesses the quality, composition, and usefulness of a lead list. Use when the user provides a lead list and wants completeness, distribution, quality issues, and recommended next actions.
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Evaluates potential channel, integration, affiliate, or strategic partners. Use when the user provides partner profiles plus partnership goals and wants fit scoring, partnership type, and outreach priority.
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Identifies likely duplicate profiles or companies using available record evidence. Use when the user provides a list and wants duplicate groups, match confidence, and merge recommendations. Describe records as likely duplicates unless there is an exact deterministic match.
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Evaluates candidate profiles against a job description and hiring criteria. Use when the user provides candidate profiles plus a role definition and wants consistent screening, matched requirements, and an interview recommendation.
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Assesses potential suppliers against defined commercial, operational, and compliance requirements. Use when the user provides supplier records plus sourcing requirements and wants a consistent shortlist with strengths and risks.
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OCM/OpenClaw envs: create, clone, upgrade, runtimes, services, logs, cleanup.
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Kova/OpenClaw validation: benchmarks, release gates, scenarios, perf reports.
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Use the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.
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Pull Request作成・更新時に使用。タイトルと説明文を自動生成・更新する。
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Safely clean regenerable clutter from a project folder by scanning for build artifacts and caches, backing up every removed path into a timestamped archive with a BACKUP.md manifest, zipping the backup to save space, and only deleting originals after explicit confirmation. Use when the user asks to clean a project directory, remove node_modules, clear build output, reclaim disk space, or tidy a repo before archiving.
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Use when choosing, reviewing, or implementing SF Symbols in an Apple-platform app — picking meaningful symbols, keeping the same concept on the same symbol everywhere, and handling variants, rendering modes, and accessibility.
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Before improving anything, show the spread of what can concretely be done on this specific input — a short menu of 3-5 interventions from lightest to heaviest touch, then wait for the user to pick. Domain-agnostic; works on any material the user hands over. Trigger when the user says "spread", "show me my options", "what can you do with this", or asks to improve or fix something without specifying how. Do NOT trigger when the user has already named a specific fix — then just do the fix.
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