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Trigger: /caveman-compress FILEPATH or \"compress memory file\"\n","caveman-compress",{"claudeCode":12},"SKILL.md frontmatter at skills/caveman-compress/SKILL.md (coalesced with duplicate skill at plugins/caveman/skills/caveman-compress)",[410,411,412,414,416,418,420,422,424,426],{"path":395,"priority":330},{"path":339,"priority":342},{"path":413,"priority":353},"SECURITY.md",{"path":415,"priority":374},"scripts/__init__.py",{"path":417,"priority":374},"scripts/__main__.py",{"path":419,"priority":374},"scripts/benchmark.py",{"path":421,"priority":374},"scripts/cli.py",{"path":423,"priority":374},"scripts/compress.py",{"path":425,"priority":374},"scripts/detect.py",{"path":427,"priority":374},"scripts/validate.py",{"basePath":429,"description":430,"displayName":431,"installMethods":432,"rationale":433,"selectedPaths":434,"source":331,"sourceLanguage":265,"type":254},"skills/caveman-stats","Show real token usage and estimated savings for the current session. Reads directly from the Claude Code session log — no AI estimation. Triggers on /caveman-stats. Output is injected by the mode-tracker hook; the model itself does not compute the numbers.\n","caveman-stats",{"claudeCode":12},"SKILL.md frontmatter at skills/caveman-stats/SKILL.md (coalesced with duplicate skill at plugins/caveman/skills/caveman-stats)",[435,436],{"path":395,"priority":330},{"path":339,"priority":342},{"basePath":438,"description":439,"displayName":440,"installMethods":441,"rationale":442,"selectedPaths":443,"source":331,"sourceLanguage":265,"type":254},"skills/caveman-commit","Ultra-compressed commit message generator. Cuts noise from commit messages while preserving intent and reasoning. Conventional Commits format. Subject ≤50 chars, body only when \"why\" isn't obvious. Use when user says \"write a commit\", \"commit message\", \"generate commit\", \"/commit\", or invokes /caveman-commit. Auto-triggers when staging changes.\n","caveman-commit",{"claudeCode":12},"SKILL.md frontmatter at skills/caveman-commit/SKILL.md",[444,445],{"path":395,"priority":330},{"path":339,"priority":342},{"basePath":447,"description":448,"displayName":449,"installMethods":450,"rationale":451,"selectedPaths":452,"source":331,"sourceLanguage":265,"type":254},"skills/caveman-help","Quick-reference card for all caveman modes, skills, and commands. One-shot display, not a persistent mode. Trigger: /caveman-help, \"caveman help\", \"what caveman commands\", \"how do I use caveman\".\n","caveman-help",{"claudeCode":12},"SKILL.md frontmatter at skills/caveman-help/SKILL.md",[453,454],{"path":395,"priority":330},{"path":339,"priority":342},{"basePath":456,"description":457,"displayName":458,"installMethods":459,"rationale":460,"selectedPaths":461,"source":331,"sourceLanguage":265,"type":254},"skills/caveman-review","Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says \"review this PR\", \"code review\", \"review the diff\", \"/review\", or invokes /caveman-review. Auto-triggers when reviewing pull requests.\n","caveman-review",{"claudeCode":12},"SKILL.md frontmatter at skills/caveman-review/SKILL.md",[462,463],{"path":395,"priority":330},{"path":339,"priority":342},{"basePath":264,"description":465,"displayName":466,"installMethods":467,"license":247,"rationale":468,"selectedPaths":469,"source":331,"sourceLanguage":265,"type":475},"Caveman installer — detects your AI coding agents and installs caveman for each one.","caveman-installer",{"npm":466},"cli ecosystem detected at /",[470,472,473,474],{"path":471,"priority":330},"package.json",{"path":339,"priority":330},{"path":341,"priority":342},{"path":373,"priority":353},"cli",{"basePath":477,"description":478,"displayName":479,"installMethods":480,"license":247,"rationale":481,"selectedPaths":482,"source":331,"sourceLanguage":265,"type":475},"src/mcp-servers/caveman-shrink","MCP proxy that compresses prose fields (tool descriptions, etc.) using caveman rules. Same accuracy, fewer context tokens.","caveman-shrink",{"npm":479},"cli ecosystem detected at src/mcp-servers/caveman-shrink",[483,484,485],{"path":471,"priority":330},{"path":339,"priority":330},{"path":486,"priority":353},"index.js",{"sources":488},[489],"manual",{"npmPackage":466},{"closedIssues90d":239,"description":492,"forks":240,"homepage":493,"license":247,"openIssues90d":241,"pushedAt":242,"readmeSize":237,"stars":243,"topics":494},"🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman","https://getcaveman.dev/",[495,496,13,497,498,215,499,218,254,500],"ai","anthropic","claude","claude-code","meme","tokens",{"downloads":8},{"classifiedAt":503,"discoverAt":504,"extractAt":505,"githubAt":505,"npmAt":506,"updatedAt":503},1778691765329,1778691760140,1778691762475,1778691764072,[217,214,215,218,216],{"evaluatedAt":509,"extractAt":296,"updatedAt":250},1778691864047,[],[512,543,571,598,631,658],{"_creationTime":513,"_id":514,"community":515,"display":516,"identity":522,"providers":527,"relations":536,"tags":539,"workflow":540},1778694269038.6682,"k1752cypc448mke749yjbkc65186mg6f",{"reviewCount":8},{"description":517,"installMethods":518,"name":520,"sourceUrl":521},"This skill should be used when the user asks to \"compress context\", \"summarize conversation history\", \"implement compaction\", \"reduce token usage\", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.",{"claudeCode":519},"muratcankoylan/Agent-Skills-for-Context-Engineering","Context Compression","https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering",{"basePath":523,"githubOwner":524,"githubRepo":525,"locale":265,"slug":526,"type":254},"skills/context-compression","muratcankoylan","Agent-Skills-for-Context-Engineering","context-compression",{"evaluate":528,"extract":535},{"promptVersionExtension":207,"promptVersionScoring":208,"score":529,"tags":530,"targetMarket":284,"tier":219},100,[531,215,532,533,214,534],"context-engineering","agent","summarization","evaluation",{"commitSha":286,"license":247},{"parentExtensionId":537,"repoId":538},"k1754dy3wbsv2a5gr1a983zzs586njca","kd7f12maf5nxmx5xttjx7scfnx86m1tv",[532,214,531,534,215,533],{"evaluatedAt":541,"extractAt":542,"updatedAt":541},1778694410149,1778694269038,{"_creationTime":544,"_id":545,"community":546,"display":547,"identity":553,"providers":558,"relations":565,"tags":567,"workflow":568},1778687399826.0247,"k173skx1fvkafn38prsv2d0qbh86nn4h",{"reviewCount":8},{"description":548,"installMethods":549,"name":551,"sourceUrl":552},"Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.",{"claudeCode":550},"github/awesome-copilot","Arize Prompt Optimization","https://github.com/github/awesome-copilot",{"basePath":554,"githubOwner":555,"githubRepo":556,"locale":265,"slug":557,"type":254},"skills/arize-prompt-optimization","github","awesome-copilot","arize-prompt-optimization",{"evaluate":559,"extract":564},{"promptVersionExtension":207,"promptVersionScoring":208,"score":529,"tags":560,"targetMarket":284,"tier":219},[215,218,561,562,475,563],"optimization","arize","data-analysis",{"commitSha":286,"license":247},{"repoId":566},"kd7dsmv976w8rtkqnjjfdtfgks86nnmw",[562,475,563,215,561,218],{"evaluatedAt":569,"extractAt":570,"updatedAt":569},1778689257343,1778687399826,{"_creationTime":572,"_id":573,"community":574,"display":575,"identity":581,"providers":585,"relations":590,"tags":593,"workflow":594},1778675791860.845,"k174f2y3jxg9senxnc66r7j6dx86md8y",{"reviewCount":8},{"description":576,"installMethods":577,"name":579,"sourceUrl":580},"应用提示重复以提高非推理 LLM 的准确性",{"claudeCode":578},"asklokesh/loki-mode","prompt-optimization","https://github.com/asklokesh/loki-mode",{"basePath":582,"githubOwner":583,"githubRepo":584,"locale":18,"slug":579,"type":254},"agent-skills/prompt-optimization","asklokesh","loki-mode",{"evaluate":586,"extract":589},{"promptVersionExtension":207,"promptVersionScoring":208,"score":529,"tags":587,"targetMarket":284,"tier":219},[215,218,588,561],"accuracy",{"commitSha":286},{"repoId":591,"translatedFrom":592},"kd7dy44r49793jt8bt02wdezk586nxsn","k17bjgfyvbnt05kpma9n0gn4j186n152",[588,215,561,218],{"evaluatedAt":595,"extractAt":596,"updatedAt":597},1778675713014,1778675659559,1778675791860,{"_creationTime":599,"_id":600,"community":601,"display":602,"identity":608,"providers":611,"relations":622,"tags":626,"workflow":627},1778694473202.7083,"k17fabzte0hynzp1fmsvfh1en986nzex",{"reviewCount":8},{"description":603,"installMethods":604,"name":606,"sourceUrl":607},"Claude Code 的高效持久化内存系统，在会话启动时可节省约 67% 的 token（经 tiktoken 验证）。分层架构，具有渐进式加载、紧凑编码、分支感知上下文、智能压缩、会话差异、冲突检测、会话续传协议和恢复模式。在会话开始时（如果存在 MEMORY.md）、在响应“记住这一点”、“继续我上次的进度”、“我们上次在做什么”、“总结”、“保存进度”、“别忘了”、“切换上下文”、“交接”、“内存健康”、“保存状态”、“继续我上次的进度”、“上下文预算”、“还剩多少上下文”或任何具有现有内存文件的项目会话开始时激活。此技能一次解决两个问题：Claude 在会话之间忘记一切，以及会话太快达到上下文限制。它用紧凑、结构化的内存加载取代了数千个浪费的重新解释 token，使 Claude 能够在 2000 个 token 内获得完整的项目上下文。\n",{"claudeCode":605},"Nagendhra-web/memory-bank","memory-bank","https://github.com/Nagendhra-web/memory-bank",{"basePath":609,"githubOwner":610,"githubRepo":606,"locale":18,"slug":606,"type":254},"skills/memory-bank","Nagendhra-web",{"evaluate":612,"extract":620},{"promptVersionExtension":207,"promptVersionScoring":208,"score":211,"tags":613,"targetMarket":284,"tier":219},[614,615,616,617,216,618,214,619],"memory","context","persistence","sessions","branch-aware","ai-assistant",{"commitSha":286,"license":621},"Apache-2.0",{"parentExtensionId":623,"repoId":624,"translatedFrom":625},"k17dbjbnpmrxhbszzw5sx6tsgs86n09d","kd740s537z66ds6qge39y9dk3186nbp9","k1723rcremvxvx59dy10cjc0f186n67k",[619,618,214,615,614,616,617,216],{"evaluatedAt":628,"extractAt":629,"updatedAt":630},1778694438899,1778694389360,1778694473202,{"_creationTime":632,"_id":633,"community":634,"display":635,"identity":641,"providers":645,"relations":652,"tags":654,"workflow":655},1778697652123.8982,"k175ckmrqc4x6sjm90k7ejbj3s86ntxs",{"reviewCount":8},{"description":636,"installMethods":637,"name":639,"sourceUrl":640},"Use the Slack tool to react, pin/unpin, send, edit, delete messages, or fetch Slack member info.",{"claudeCode":638},"steipete/clawdis","slack","https://github.com/steipete/clawdis",{"basePath":642,"githubOwner":643,"githubRepo":644,"locale":265,"slug":639,"type":254},"skills/slack","steipete","clawdis",{"evaluate":646,"extract":651},{"promptVersionExtension":207,"promptVersionScoring":208,"score":529,"tags":647,"targetMarket":284,"tier":219},[639,648,217,649,650],"messaging","automation","api",{"commitSha":286},{"repoId":653},"kd738npxg9yh3xf3vddzy9fyfh86nhng",[650,649,217,648,639],{"evaluatedAt":656,"extractAt":657,"updatedAt":656},1778698950505,1778697652123,{"_creationTime":659,"_id":660,"community":661,"display":662,"identity":668,"providers":672,"relations":677,"tags":680,"workflow":681},1778696833339.6218,"k176gr5qszggh36kjn8a1cwehs86n2jd",{"reviewCount":8},{"description":663,"installMethods":664,"name":666,"sourceUrl":667},"Interact with Gmail - search emails, read messages, send emails, create drafts, and manage labels.\nUse when user asks to: search email, read email, send email, create email draft, mark as read,\narchive email, star email, or manage Gmail labels. Lightweight alternative to full Google\nWorkspace MCP server with standalone OAuth authentication.\n",{"claudeCode":665},"sanjay3290/ai-skills","gmail","https://github.com/sanjay3290/ai-skills",{"basePath":669,"githubOwner":670,"githubRepo":671,"locale":265,"slug":666,"type":254},"skills/gmail","sanjay3290","ai-skills",{"evaluate":673,"extract":676},{"promptVersionExtension":207,"promptVersionScoring":208,"score":529,"tags":674,"targetMarket":284,"tier":219},[666,675,217,649,281],"email",{"commitSha":286},{"parentExtensionId":678,"repoId":679},"k17es37z10n1sw6t2m3f0vsydx86mnje","kd71np0fyqg23qg8w2hcfw0h0h86nkn0",[649,217,675,666,281],{"evaluatedAt":682,"extractAt":683,"updatedAt":682},1778696939862,1778696833339]