OpenClaw/clawhub-security-signals huggingface dataset:
Skill: moshiii/agent-core-extractor (version 1.0.0)

--- name: agentpearl-exporter version: 1.0.0 description: Export the agent core from supported framework repositories into a small source-only zip for AI migration or cross-framework analysis. Use when the user wants to detect supported agent repos, extract only agent-defining files, produce a portability pack, or study identity, instruction, runtime, capability, multi-agent, and prompt-composition layers. metadata: license: MIT author: moshiwei homepage: https://github.com/moshiwei/AgentPearl tags: - agents - portability - migration - export - prompt-engineering openclaw: emoji: shell bins: zip: Optional but recommended for building export archives. --- # AgentPearl Exporter Use this skill to build a pure agent-core export from one or more supported repositories. ## When To Use It Use this skill when the user wants to: - extract only the files that define an agent - package agent-core files into a clean zip - detect which supported framework a repo uses - prepare source material for AI-driven migration into another agent framework - study the identity, instruction, runtime, capability, multi-agent, or composition layers of an agent repo ## Supported Frameworks The bundled exporter detects these framework signatures: - `nanoclaw-ts-bootstrap` - `nanobot-py-templates` - `nullclaw-zig-bootstrap` - `zeroclaw-rs-config-prompt` - `openfang-rs-manifests` - `codex-rs-builtins` ## Workflow 1. Confirm the target repositories exist under a common base directory. 2. Run `scripts/export-agent-core-pack.sh`. 3. Inspect the resulting zip, `README.txt`, and `MANIFEST.txt`. 4. If the user needs migration guidance, read `references/AGENT_CORE.md` to map the exported files into agent-core layers. ## Commands Export the default supported repositories under `~/Documents/GitHub`: ```bash ./scripts/export-agent-core-pack.sh ``` Export a subset of repositories: ```bash ./scripts/export-agent-core-pack.sh --repos nanobot,openfang ``` Choose a base directory, output directory, and archive name: ```bash ./scripts/export-agent-core-pack.sh \ --base-dir ~/Documents/GitHub \ --output-dir ./out \ --name agent-core-snapshot \ --repos codex,nullclaw ``` ## Rules - Keep the archive source-only. - Include only files that directly define agent behavior. - Exclude tests, build artifacts, dependency folders, unrelated app code, and target-framework metadata. - If a repository does not match a supported signature, stop and report it instead of guessing. ## References - Read `references/AGENT_CORE.md` when you need the layer model for interpreting exported files.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: miromiraclemiro-ux/sutang-ecom-copywriter (version 1.0.0)

--- name: 电商详情页生成器 description: 专为小电商卖家设计，输入产品信息，一键生成淘宝/抖音/小红书/拼多多全平台适配的详情页文案和带货脚本。解决小卖家写不完文案的痛点。 --- # 电商详情页生成器 > 一键生成能卖货的电商详情页 · 淘宝/抖音/小红书/拼多多全平台适配 --- ## 核心能力 本工具专为**1-5人小电商团队**设计，解决以下痛点： - 每个新品上架要写10个平台的文案 → **5分钟搞定** - 不知道什么文案能转化 → **内置转化逻辑框架** - 文案风格不匹配平台调性 → **一键生成多版本** --- ## 使用方式 ### 第一步：提供产品信息 请提供以下信息（越详细，生成越精准）： ``` 产品名称：[填写] 产品核心特点：[填写，3-5条] 目标用户：[填写，如"25-35岁职场女性"] 价格区间：[填写] 独特卖点/差异化：[填写，与竞品相比的优势] ``` ### 第二步：选择需要生成的平台 - 淘宝/天猫 详情页 - 抖音 带货脚本 - 小红书 种草笔记 - 拼多多 商品描述 - 以上全部 --- ## 生成框架（内部逻辑） ### 淘宝详情页文案结构 ``` 【首图吸睛文案】 → 痛点开场 + 产品核心承诺（10字以内） 【痛点共鸣区】 → 描述用户买这类产品前的3个常见困扰 → 用"你是不是也..."句式引发共鸣 【解决方案区】 → 产品如何解决上述痛点 → 列出3个核心卖点，每个配场景描述 【信任背书区】 → 材质/成分/工艺说明（让用户觉得值） → 用户评价/销量数据（让用户觉得安全） 【行动号召区】 → 限时优惠/库存紧张（制造紧迫感） → 售后保障/退换承诺（消除顾虑） 【关联销售区】 → 搭配推荐，提升客单价 ``` ### 抖音带货脚本结构（15秒版） ``` 0-2秒：开场钩子（痛点/好奇/冲突） 2-10秒：产品展示 + 核心卖点（场景化） 10-13秒：价格对比/优惠信息 13-15秒：行动号召（点击购买/左下角） ``` ### 抖音带货脚本结构（60秒版） ``` 0-5秒：黄金开场（制造悬念或引发共鸣） 5-20秒：痛点放大（让用户觉得"说的就是我"） 20-35秒：产品介绍（场景化展示，不是念参数） 35-45秒：使用体验/对比效果（真实感） 45-55秒：价格/优惠说明（限时限量制造紧迫感） 55-60秒：收尾号召（信任背书 + 行动引导） ``` ### 小红书种草笔记结构 ``` 【标题】：痛点+解决方案+情绪词（emoji加分） 【开头】：真实使用场景，引发共鸣 【正文】：个人真实体验，包含： - 使用前/使用后对比 - 3个真实使用场景 - 1个意外惊喜/缺点诚实说 【结尾】：互动引导（评论区见/私信问） + 相关话题标签 ``` ### 拼多多商品描述结构 ``` 【第一行】：核心卖点 + 价格锚点（让人觉得值） 【第二行】：材质/规格/数量（核心参数） 【第三行】：发货/售后保障 【第四行】：促销信息（如有） ``` --- ## 转化心理学要点 生成文案时自动遵循以下原则： 1. **痛点→快乐**：先描述不买会怎样痛苦，再给解决方案 2. **具体>模糊**："3秒上手" > "简单易用" 3. **数字说服**："已帮助1000+卖家" > "很多人用" 4. **场景化**："做饭时单手开盖" > "设计人性化" 5. **紧迫感**："仅剩30件" > "库存有限" 6. **消除顾虑**："30天无理由退换" > "品质保证" --- ## 适用产品类型 ✅ 适合：服饰鞋包、美妆护肤、家居日用、食品特产、母婴用品、数码配件 ⚠️ 需要更多信息才能生成精准文案的产品：药品、医疗器械、金融产品、保险 --- ## 定价参考（运营者可参考） | 套餐 | 价格 | 说明 | |------|------|------| | 单次使用 | ¥19.9/次 | 同一商品无限次优化 | | 月度会员 | ¥49/月 | 无限次使用 | | 年度会员 | ¥399/年 | 无限次 + 优先新功能 | --- *本工具由米糖Skill工坊出品，专为小电商卖家赋能*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chenpipi0807/weekly-newspaper-assistant (version 1.0.0)

--- name: weekly-card description: 周报助手 — 将用户的工作内容、周报、日报、总结一键生成一张 4:3 极简科技风汇报卡片（单张图，非多页PPT）。触发词：周报助手、周报卡片、日报卡片、一张图、汇报卡、生成卡片。 --- # 周报助手 将任意工作内容提炼为**一张** 4:3 极简科技风汇报卡片。 ## 核心理念 - **一张图解决问题** — 不分页，不翻页，截图即用 - **内容极度精炼** — 每条要点 ≤15 字，最多 6 条 - **视觉层次清晰** — 标题 > 要点 > 标注，三层结构 - **深色科技风** — 黑底 + 蓝/紫光斑 + 白字 ## 生成流程 ### Step 1: 提炼内容 - 读取原始内容 - 提取核心要点（≤6 条） - 生成一个冲击力标题（≤12字） - 生成一句副标题/日期标注 ### Step 2: 生成 HTML 卡片 - 比例严格 **4:3**（宽1200px × 高900px） - 单屏，无滚动，无翻页 - 包含动态光斑背景动画 - 使用 Tailwind CDN + Google Fonts（国内镜像） ### Step 3: 截图输出 生成 HTML 后，使用以下命令截图为 PNG： ```bash # 使用 puppeteer-screenshot 或 chromium headless /Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \ --headless --screenshot=output.png \ --window-size=1200,900 \ --disable-gpu \ file:///path/to/card.html ``` 若截图命令不可用，直接告知用户打开 HTML 文件并截图。 ## 图片处理规则（有图时必须严格遵守） 1. **必须放入** — 用户发了图片，100% 要排进卡片，不能忽略 2. **禁止裁切/遮挡** — 图片必须完整显示，使用 `object-fit: contain` + 等比缩放，绝对不用 `cover` 或 `clip` 3. **先算图再排版** — 计算图片数量和宽高比，确定图片区域大小，再安排文字布局（图片优先，文字自适应剩余空间） 4. **多图时** — 纵向排列，每张图保持完整，不重叠 ## 视觉规范 | 项目 | 规范 | |------|------| | 比例 | 4:3（1200×900px） | | 背景 | #000 + 模糊光斑（蓝/紫/青） | | 主标题 | 白色，font-black，**4~5rem**（宁大勿小） | | 要点文字 | rgba(255,255,255,0.85)，font-normal，**1.05~1.2rem** | | 标注文字 | rgba(255,255,255,0.35)，font-light | | 强调色 | #60a5fa（蓝）/ #a78bfa（紫）/ #34d399（绿）| | 字体 | Noto Sans SC + Inter | ## 卡片结构模板 无图版： ``` ┌─────────────────────────────────────────────┐ │ [日期标注] [标识] │ │ │ │ 主标题（大，白，4~5rem，bold） │ │ 副标题 │ │ ───────────────────────────────────────── │ │ • 要点一（1.1rem） │ │ • 要点二 │ │ • 要点三 │ │ [底部标注] │ └─────────────────────────────────────────────┘ ``` 有图版（左文右图 或 上文下图，根据图片数量决定）： ``` ┌──────────────────────┬──────────────────────┐ │ 主标题（3.5rem+） │ [图片1 完整显示] │ │ 副标题 │ │ │ ─────────────────── │ [图片2 完整显示] │ │ • 要点一 │ │ │ • 要点二 │ 图片说明 │ │ [底部标注] │ │ └──────────────────────┴──────────────────────┘ ``` ## 严禁行为 - 多页/翻页结构 - 超过 8 个要点 - 密集小字（要点字号 < 1rem） - 图片裁切、object-fit:cover、overflow:hidden 遮挡图片 - 横向表格 - 复杂图表 ## 输出 1. 保存为 `~/.openclaw/workspace/card-YYYYMMDD.html` 2. 尝试用 Chrome headless 截图为 PNG 3. 发送卡片图片给用户


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 534422530/laosi-github (version 2.0.0)

--- name: github-helper description: GitHub操作助手 - PR/Issue/代码搜索/仓库管理，基于gh CLI。v2新增：CI状态监控、Release管理、Code Review辅助 metadata: {"openclaw": {"requires": {"bins": ["gh"]}, "install": []}} tags: [github, git, pr, issue, repository, devops] version: 2.0.0 author: laosi source: adapted --- # GitHub Helper - GitHub操作助手 > 激活词: GitHub / PR操作 / Issue管理 ## 功能 - PR创建和管理 - Issue操作 - 仓库浏览 - 代码搜索 - 代码审查 ## 安装 ```bash # Windows winget install gh # 登录 gh auth login ``` ## 命令参考 ### PR操作 ```bash # 创建PR gh pr create --title "标题" --body "描述" # 查看PR列表 gh pr list --state open # PR详情 gh pr view 123 # 合并PR gh pr merge 123 --squash # 评论PR gh pr comment 123 --body "评论内容" ``` ### Issue操作 ```bash # 创建Issue gh issue create --title "标题" --body "描述" # 查看Issue列表 gh issue list --state open # 关闭Issue gh issue close 456 ``` ### 仓库操作 ```bash # 查看仓库 gh repo view # 克隆仓库 gh repo clone owner/repo # 查看代码 gh search code "keyword" ``` ## Python封装 ```python import subprocess class GitHubHelper: def __init__(self, repo: str = None): self.repo = repo def create_pr(self, title: str, body: str = "") -> str: result = subprocess.run( ["gh", "pr", "create", "--title", title, "--body", body], capture_output=True, text=True ) return result.stdout.strip() def list_prs(self, state: str = "open") -> list: result = subprocess.run( ["gh", "pr", "list", "--state", state, "--json", "number,title,state"], capture_output=True, text=True ) import json return json.loads(result.stdout) def create_issue(self, title: str, body: str = "") -> str: result = subprocess.run( ["gh", "issue", "create", "--title", title, "--body", body], capture_output=True, text=True ) return result.stdout.strip() def search_code(self, query: str, repo: str = None) -> list: cmd = ["gh", "search", "code", query, "--json", "path,repository"] if repo: cmd.extend(["--repo", repo]) result = subprocess.run(cmd, capture_output=True, text=True) import json return json.loads(result.stdout) ``` ## 使用场景 1. 自动化PR审查流程 2. 批量Issue管理 3. 代码搜索分析 4. 仓库状态监控


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: langyou/skill-discovery-seo (version 1.0.0)

--- name: skill-discovery-seo description: Improve a skill package so AI agents can find, select, and trust it across registries and ecosystems, including Codex, Claude-style agents, OpenClaw, and other skill-based tools. Use when creating, renaming, packaging, publishing, or refining a skill; writing metadata, names, descriptions, tags, listings, or example prompts; or trying to improve skill discoverability, skill SEO, trigger accuracy, semantic matching, and install or selection confidence. --- # Skill Discovery SEO ## Overview Improve a skill's discoverability for both humans and AI agents. Focus on triggerable names, dense metadata, realistic example prompts, clear scope, and packaging decisions that help registries and meta-skills choose the right skill reliably. Load [references/discoverability-guide.md](references/discoverability-guide.md) before rewriting metadata or publish copy. ## Workflow ### 1. Identify the discovery surface Check which fields the registry or agent ecosystem will likely index: - skill `name` - frontmatter `description` - `display_name` - short description - default prompt - tags or categories - example prompts - listing copy or repository summary If the platform is unknown, assume most systems use a mix of exact text match, semantic retrieval, and usage or trust signals. ### 2. Tighten the trigger language Rewrite the discovery fields so they answer four questions fast: - what domain is this for - what job does it do - what inputs does it accept - what output or decision does it produce Prefer literal wording over branding, slogans, or clever titles. ### 3. Expand query coverage without bloating Include the main phrases a user or agent would search for: - primary nouns such as platform, artifact, or file type - major verbs such as review, check, create, revise, deploy, or analyze - important synonyms such as policy and compliance, or caption and script - edge-case phrases only if the skill truly handles them Do not keyword-stuff. Add only terms that improve routing accuracy. ### 4. Check trust and scope Make sure the skill: - has a narrow, believable scope - states boundaries clearly - promises outputs it can actually deliver - avoids agent-specific assumptions unless truly required If the skill claims too much, narrow it until another agent could select it confidently. ### 5. Produce a publish-ready pack Return: - recommended skill name - frontmatter description - `display_name` - short description - default prompt - optional tags - 3 to 8 realistic example prompts - one short note on what not to claim in the listing ## Output Rules - Keep names literal, ASCII-safe, and easy to type. - Put the highest-signal trigger terms early in the description. - Match the wording users would naturally use, not internal jargon. - Keep agent-specific wording out of general-purpose skills unless the skill is actually tied to one ecosystem. - Prefer one strong promise over many weak ones.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wimi321/claude-code-memory-landscape-review (version 1.0.0)

--- name: "memory_landscape_review" description: "Use when the user wants to review auto-memory, promote durable instructions into CLAUDE.md or local memory, and clean up duplicates or conflicts." --- # Memory Landscape Review Use this skill to review project memory, local memory, and auto-memory together. ## Workflow 1. Gather repo memory files and current auto-memory context. 2. Classify each entry as repo-wide, personal, team-wide, or temporary. 3. Detect duplicates, conflicts, and outdated instructions. 4. Present a grouped report before making any changes. 5. Only apply promotions or cleanup after explicit approval. ## Guardrails - Propose first, edit second. - Do not guess when an instruction might be personal vs shared. - Keep transient notes out of durable memory files. ## Example Requests - Review my auto-memory and tell me what belongs in durable memory. - Find duplicate or conflicting instructions across memory layers. ## Inputs - Repo memory files - Auto-memory state - User preference for shared vs local memory ## Outputs - Promotion proposals - Conflict cleanup report - No-change recommendations ## Success Criteria - Durable memory candidates are identified clearly. - Duplicates and stale entries are surfaced. - No edits happen before approval. ## Non-Goals - Silently editing memory files - Guessing personal vs shared intent when ambiguous ## Source Provenance Derived from `src/skills/bundled/remember.ts`.

path: agents/openai.yaml
content: interface:
  display_name: "Memory Landscape Review"
  short_description: "Review and reorganize memory layers"
  default_prompt: "Audit my memory layers and propose what should move into durable repo or personal memory."


path: references/provenance.md
content: # Provenance

- Skill slug: `memory-landscape-review`
- Source module: `src/skills/bundled/remember.ts`
- Extracted from local Claude Code source analysis


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/amazon-seller-analytics (version 1.0.0)

--- name: amazon-seller-analytics description: "Analyze Amazon seller storefronts and product portfolios. Estimate revenue, identify top products, assess growth trajectory, and uncover strategy patterns. Useful for competitive intelligence and market research." metadata: nexscope: emoji: "📊" category: amazon --- # Amazon Seller Analytics 📊 Analyze Amazon seller storefronts and product portfolios. Estimate revenue, identify top products, assess growth trajectory, and uncover strategy patterns. Useful for competitive intelligence and market research. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill amazon-seller-analytics -g ``` ## Usage ``` Analyze this Amazon seller's strategy: they sell kitchen gadgets and have about 50 products. Help me understand their approach. ``` ## Capabilities - Seller storefront analysis (product count, categories, pricing strategy) - Revenue estimation from BSR and category benchmarks - Product portfolio assessment (hero products, long-tail, new launches) - Review velocity and quality scoring - Growth trajectory estimation - Competitive positioning analysis ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: theshimaw-svg/offer-generator (version 1.0.0)

name: $0 to $100 Offer Generator description: Creates a simple, sellable digital offer from any skill or idea. trigger: when: - user wants to make money online - user has a skill, idea, or niche but no product avoid: - job search advice - general motivation input_schema: skill_or_interest: string target_audience: optional string execution: - identify a painful problem in the niche - define a fast, tangible outcome - create a simple product: - ebook, checklist, mini-course, or template - generate: - product name - offer promise - 3 key benefits - pricing between $1–$29 - quick delivery format - include a simple “why buy now” angle output_format: PRODUCT NAME: OFFER: TARGET PROBLEM: BENEFITS: - - - PRICE: DELIVERY: URGENCY ANGLE: constraints: - keep it simple (no complex SaaS ideas) - must be launchable within 24 hours - avoid vague benefits


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sinoslug/token-optimization-sinoslug (version 1.0.0)

--- name: token-optimization description: Reduce unnecessary token usage with the current production-proven workflow. Use when users ask to optimize token consumption, reduce prompt/context bloat, standardize concise replies, or align token-saving behavior across assistants. Includes only currently implemented practices (no future/experimental tactics). --- # Token Optimization Use this skill when the task is about reducing token usage while keeping answer quality stable. ## Workflow 1. Read the current baseline spec first: - `references/current-spec.md` 2. Apply only the already-implemented rules from that file. 3. Do not introduce speculative or not-yet-implemented optimization tactics. 4. If asked to change behavior, update the spec and version it. ## Scope guard - In scope: concise reply behavior, log/tool-output trimming, current token-show usage flow, and currently active execution process. - Out of scope: future ideas not yet implemented (auto-threshold switching, automatic split replies, weekly auto-reviews), unless user explicitly requests a new version update. ## Output expectation When asked to explain or sync the optimization plan, provide the “current-state only” version and cite that it follows `references/current-spec.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qqq664590975-del/usage-stats (version 1.0.0)

--- name: usage-stats description: | OpenClaw 使用统计技能。自动分析会话记录，生成 token 消耗、费用、工具使用等完整报告。 触发场景：用户提到"使用统计"、"token 消耗"、"费用分析"、"使用报告"、"使用情况"、 "usage stats"、"token usage"、"消费记录"、"我用了多少"、"统计"等关键词时使用。 关键词：usage, token, stats, 统计, 费用, cost, 消耗, report --- # Usage Stats — OpenClaw 使用统计 ## 概述 解析 `~/.qclaw/agents/main/sessions/` 下的所有会话记录，生成完整的 OpenClaw 使用统计报告，包含 Token 消耗、费用估算、模型分布、工具调用排行、错误分析、时段分布、日趋势等维度。 **使用方式：** 直接告诉我"跑一下使用统计"或"统计一下我的使用情况"，我调用脚本生成报告。 ## 核心功能 - ✅ **Token 统计**：input / output / cacheRead / cacheWrite / 总计 - ✅ **费用估算**：基于 usage 数据计算总费用 - ✅ **模型分布**：各模型的调用次数和 token 消耗 - ✅ **缓存命中率**：cacheRead / (cacheRead + cacheWrite) - ✅ **工具调用排行**：所有工具的调用频次（exec / browser / message 等） - ✅ **错误分析**：归类常见错误 + 根因 + 解决方案 - ✅ **会话详情**：每个会话的开始时间、时长、token、轮次 - ✅ **日趋势**：每日 token/会话/错误量变化 - ✅ **时段分布**：24 小时活跃热力图 - ✅ **历史趋势对比**：与上次运行的对比 ## 工作流程 ### 步骤 1：运行脚本 执行 `scripts/analyze_usage.py`，报告输出到： `~/.qclaw/workspace/memory/usage_stats_latest.md` 历史快照保存在：`~/.qclaw/workspace/memory/usage_stats_history.json` ### 步骤 2：解读报告 | 章节 | 含义 | |------|------| | Overall Stats | 整体汇总（总费用、总 token、活跃时长） | | Model Distribution | 各模型的调用量和费用 | | Error Analysis | 错误类型排行 + 自动根因分析 | | Tool Usage | 工具调用频次排行 | | Exec Performance | 命令执行耗时（P50/P95） | | Conversation Activity | 消息角色分布 + 会话结束原因 | | Text Volume | 平均消息长度 | | Session Detail | 每个会话的详细数据 | | Daily Trend | 每日 token / 会话量趋势 | | Hourly Distribution | 24 小时活跃时段热力图 | | Trend (vs Last Run) | 与上次运行的环比变化 | | Troubleshooting Guide | 错误知识库（根因 + 修复方案） | ### 步骤 3：更新记忆 将重要发现写入 `memory/YYYY-MM-DD.md`（如：发现了某个高频错误、token 消耗异常等）。 ## 数据来源 - **会话记录**：`~/.qclaw/agents/main/sessions/*.jsonl` - **历史快照**：`~/.qclaw/workspace/memory/usage_stats_history.json` > 数据来自会话消息中的 `usage` 字段，由 OpenClaw 自动记录，无需手动维护。 ## 限制与注意事项 - **缓存命中**：仅统计 OpenClaw 明确记录的 `cacheRead`/`cacheWrite`，实际缓存效果可能更高 - **费用估算**：基于报告数据计算，可能与实际账单有差异，仅供参考 - **错误归类**：脚本按错误模式自动归类，部分罕见错误可能归到泛型类型（`exit_code` 等） - **数据范围**：仅统计 `main` agent 的会话，子 agent 会话不包含在内 - **历史长度**：最多保留 90 条历史快照，超出自动清理旧记录 ## 常见问题 | 问题 | 答案 | |------|------| | 报告在哪里？ | `~/.qclaw/workspace/memory/usage_stats_latest.md` | | 历史记录在哪？ | `~/.qclaw/workspace/memory/usage_stats_history.json` | | 如何查看趋势？ | 报告中 "Trend (vs Last Run)" 章节 | | 发现高频错误怎么办？ | 参见 "Troubleshooting Guide" 章节，按提示修复 | | 能统计指定时间段吗？ | 目前自动统计全部历史，可手动编辑脚本过滤日期 | | 缓存命中率 100% 正常吗？ | 正常，说明 AI 回复大量复用上下文缓存 | ## 输出格式规范 详见 `references/output-format.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)