[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718VYGWEB4K4X8APP4VAMC8":3,"skill-same:01M718VYGWEB4K4X8APP4VAMC8":71},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M718VYGWEB4K4X8APP4VAMC8","hierarchical-agent-memory",null,"Scoped CLAUDE.md memory system that reduces context token spend. Creates directory-level context files, tracks savings via dashboard, and routes agents to the right sub-context.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhierarchical-agent-memory\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"memory","agent memory","hierarchical agent memory","context","savings","context files","token","safe","https","2026","directory","via","github","dashboard","ops","files","memory system","scoped","system","tracks","2026-11-10 10:12:30",false,"---\nname: hierarchical-agent-memory\ndescription: \"Scoped CLAUDE.md memory system that reduces context token spend. Creates directory-level context files, tracks savings via dashboard, and routes agents to the right sub-context.\"\nrisk: safe\nsource: \"https:\u002F\u002Fgithub.com\u002Fkromahlusenii-ops\u002Fham\"\ndate_added: \"2026-02-27\"\n---\n\n# Hierarchical Agent Memory (HAM)\n\nScoped memory system that gives AI coding agents a cheat sheet for each directory instead of re-reading your entire project every prompt. Root CLAUDE.md holds global context (~200 tokens), subdirectory CLAUDE.md files hold scoped context (~250 tokens each), and a `.memory\u002F` layer stores decisions, patterns, and an inbox for unconfirmed inferences.\n\n## When to Use This Skill\n\n- Use when you want to reduce input token costs across Claude Code sessions\n- Use when your project has 3+ directories and the agent keeps re-reading the same files\n- Use when you want directory-scoped context instead of one monolithic CLAUDE.md\n- Use when you want a dashboard to visualize token savings, session history, and context health\n- Use when setting up a new project and want structured agent memory from day one\n\n## How It Works\n\n### Step 1: Setup (\"go ham\")\n\nAuto-detects your project platform and maturity, then generates the memory structure:\n\n```\nproject\u002F\n├── CLAUDE.md              # Root context (~200 tokens)\n├── .memory\u002F\n│   ├── decisions.md       # Architecture Decision Records\n│   ├── patterns.md        # Reusable patterns\n│   ├── inbox.md           # Inferred items awaiting confirmation\n│   └── audit-log.md       # Audit history\n└── src\u002F\n    ├── api\u002FCLAUDE.md      # Scoped context for api\u002F\n    ├── components\u002FCLAUDE.md\n    └── lib\u002FCLAUDE.md\n```\n\n### Step 2: Context Routing\n\nThe root CLAUDE.md includes a routing section that tells the agent exactly which sub-context to load:\n\n```markdown\n## Context Routing\n\n→ api: src\u002Fapi\u002FCLAUDE.md\n→ components: src\u002Fcomponents\u002FCLAUDE.md\n→ lib: src\u002Flib\u002FCLAUDE.md\n```\n\nThe agent reads root, then immediately loads the relevant subdirectory context — no guessing.\n\n### Step 3: Dashboard (\"ham dashboard\")\n\nLaunches a web dashboard at localhost:7777 that visualizes:\n- Token savings (HAM-on vs HAM-off sessions)\n- Daily token and cost trends\n- Per-directory session breakdown\n- Context file health (missing\u002Fstale\u002Finherited CLAUDE.md coverage)\n- Routing compliance (how often the agent follows the routing map)\n- Carbon\u002Fenergy estimates\n\n## Commands\n\n| Trigger | What it does |\n|---|---|\n| `go ham` | Set up HAM — auto-detect platform, generate CLAUDE.md files |\n| `ham savings` | Show token and cost savings report |\n| `ham dashboard` | Launch the interactive web dashboard |\n| `ham audit` | Health check on memory files |\n| `ham insights` | Generate actionable insights from session data |\n| `ham route` | Add\u002Fupdate Context Routing section in root CLAUDE.md |\n| `ham carbon` | Show energy and carbon efficiency data |\n\n## Examples\n\n### Example 1: First-time setup\n\n```\nUser: go ham\n\nAgent: HAM setup complete. Created 8 files.\n  - CLAUDE.md (root)\n  - .memory\u002Fdecisions.md\n  - .memory\u002Fpatterns.md\n  - .memory\u002Finbox.md\n  - src\u002Fapi\u002FCLAUDE.md\n  - src\u002Fcomponents\u002FCLAUDE.md\n  - src\u002Flib\u002FCLAUDE.md\n  - src\u002Futils\u002FCLAUDE.md\n\nBaseline captured in .memory\u002Fbaseline.json\nRun \"HAM savings\" to see your token and cost savings.\n```\n\n### Example 2: Checking savings\n\n```\nUser: ham savings\n\nAgent:\n  Before HAM: ~7,500 tokens\u002Fprompt\n  After HAM:    ~450 tokens\u002Fprompt\n  Savings:    7,050 tokens (94%)\n\n  Monthly projection (1,500 prompts):\n    Sonnet: ~$31.73 saved\n    Opus:   ~$158.63 saved\n```\n\n## Best Practices\n\n- Keep root CLAUDE.md under 60 lines \u002F 250 tokens\n- Keep subdirectory CLAUDE.md files under 75 lines each\n- Run `ham audit` every 2 weeks to catch stale or missing context files\n- Use `ham route` after adding new directories to keep routing current\n- Review `.memory\u002Finbox.md` periodically — confirm or reject inferred items\n\n## Limitations\n\n- Token estimates use ~4 chars = 1 token approximation, not a real tokenizer\n- Baseline savings comparisons are estimates based on typical agent behavior\n- Dashboard requires Node.js 18+ and reads session data from `~\u002F.claude\u002Fprojects\u002F`\n- Context routing detection relies on CLAUDE.md read order in session JSONL files\n- Does not auto-update subdirectory CLAUDE.md content — you maintain those manually or via `ham audit`\n- Carbon estimates use regional grid averages, not real-time energy data\n\n## Related Skills\n\n- `agent-memory-systems` — general agent memory architecture patterns\n- `agent-memory-mcp` — MCP-based memory integration\n","# Hierarchical Agent Memory (HAM)\n\nScoped memory system that gives AI coding agents a cheat sheet for each directory instead of re-reading your entire project every prompt. Root CLAUDE.md holds global context (~200 tokens), subdirectory CLAUDE.md files hold scoped context (~250 tokens each), and a `.memory\u002F` layer stores decisions, patterns, and an inbox for unconfirmed inferences.\n\n## When to Use This Skill\n\n- Use when you want to reduce input token costs across Claude Code sessions\n- Use when your project has 3+ directories and the agent keeps re-reading the same files\n- Use when you want directory-scoped context instead of one monolithic CLAUDE.md\n- Use when you want a dashboard to visualize token savings, session history, and context health\n- Use when setting up a new project and want structured agent memory from day one\n\n## How It Works\n\n### Step 1: Setup (\"go ham\")\n\nAuto-detects your project platform and maturity, then generates the memory structure:\n\n```\nproject\u002F\n├── CLAUDE.md              # Root context (~200 tokens)\n├── .memory\u002F\n│   ├── decisions.md       # Architecture Decision Records\n│   ├── patterns.md        # Reusable patterns\n│   ├── inbox.md           # Inferred items awaiting confirmation\n│   └── audit-log.md       # Audit history\n└── src\u002F\n    ├── api\u002FCLAUDE.md      # Scoped context for api\u002F\n    ├── components\u002FCLAUDE.md\n    └── lib\u002FCLAUDE.md\n```\n\n### Step 2: Context Routing\n\nThe root CLAUDE.md includes a routing section that tells the agent exactly which sub-context to load:\n\n```markdown\n## Context Routing\n\n→ api: src\u002Fapi\u002FCLAUDE.md\n→ components: src\u002Fcomponents\u002FCLAUDE.md\n→ lib: src\u002Flib\u002FCLAUDE.md\n```\n\nThe agent reads root, then immediately loads the relevant subdirectory context — no guessing.\n\n### Step 3: Dashboard (\"ham dashboard\")\n\nLaunches a web dashboard at localhost:7777 that visualizes:\n- Token savings (HAM-on vs HAM-off sessions)\n- Daily token and cost trends\n- Per-directory session breakdown\n- Context file health (missing\u002Fstale\u002Finherited CLAUDE.md coverage)\n- Routing compliance (how often the agent follows the routing map)\n- Carbon\u002Fenergy estimates\n\n## Commands\n\n| Trigger | What it does |\n|---|---|\n| `go ham` | Set up HAM — auto-detect platform, generate CLAUDE.md files |\n| `ham savings` | Show token and cost savings report |\n| `ham dashboard` | Launch the interactive web dashboard |\n| `ham audit` | Health check on memory files |\n| `ham insights` | Generate actionable insights from session data |\n| `ham route` | Add\u002Fupdate Context Routing section in root CLAUDE.md |\n| `ham carbon` | Show energy and carbon efficiency data |\n\n## Examples\n\n### Example 1: First-time setup\n\n```\nUser: go ham\n\nAgent: HAM setup complete. Created 8 files.\n  - CLAUDE.md (root)\n  - .memory\u002Fdecisions.md\n  - .memory\u002Fpatterns.md\n  - .memory\u002Finbox.md\n  - src\u002Fapi\u002FCLAUDE.md\n  - src\u002Fcomponents\u002FCLAUDE.md\n  - src\u002Flib\u002FCLAUDE.md\n  - src\u002Futils\u002FCLAUDE.md\n\nBaseline captured in .memory\u002Fbaseline.json\nRun \"HAM savings\" to see your token and cost savings.\n```\n\n### Example 2: Checking savings\n\n```\nUser: ham savings\n\nAgent:\n  Before HAM: ~7,500 tokens\u002Fprompt\n  After HAM:    ~450 tokens\u002Fprompt\n  Savings:    7,050 tokens (94%)\n\n  Monthly projection (1,500 prompts):\n    Sonnet: ~$31.73 saved\n    Opus:   ~$158.63 saved\n```\n\n## Best Practices\n\n- Keep root CLAUDE.md under 60 lines \u002F 250 tokens\n- Keep subdirectory CLAUDE.md files under 75 lines each\n- Run `ham audit` every 2 weeks to catch stale or missing context files\n- Use `ham route` after adding new directories to keep routing current\n- Review `.memory\u002Finbox.md` periodically — confirm or reject inferred items\n\n## Limitations\n\n- Token estimates use ~4 chars = 1 token approximation, not a real tokenizer\n- Baseline savings comparisons are estimates based on typical agent behavior\n- Dashboard requires Node.js 18+ and reads session data from `~\u002F.claude\u002Fprojects\u002F`\n- Context routing detection relies on CLAUDE.md read order in session JSONL files\n- Does not auto-update subdirectory CLAUDE.md content — you maintain those manually or via `ham audit`\n- Carbon estimates use regional grid averages, not real-time energy data\n\n## Related Skills\n\n- `agent-memory-systems` — general agent memory architecture patterns\n- `agent-memory-mcp` — MCP-based memory integration\n",{"name":5,"risk":31,"source":49,"date_added":50,"description":7},"https:\u002F\u002Fgithub.com\u002Fkromahlusenii-ops\u002Fham","2026-02-27","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fglobal\u002Fhierarchical-agent-memory","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill hierarchical-agent-memory",{"tool":63,"command":64},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill hierarchical-agent-memory",{"tool":66,"command":67},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill hierarchical-agent-memory","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhierarchical-agent-memory\u002FSKILL.md",true,[],[72,96,122,132,145,160],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":83,"publishedAt":44},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[78],{"categoryNo":79,"categoryName":80,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[84,85,86,87,88,89,90,91,92,93,94,95],"wechat search","wechat","search","capabilities","openclaw","design","web search","compliance","web","official","fetch","with",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":44},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). 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