[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CTCNAEZH0EYWNMH1DXAXGR":3,"skill-same:01M6CTCNAEZH0EYWNMH1DXAXGR":76},{"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":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":48,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6CTCNAEZH0EYWNMH1DXAXGR","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.","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\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],"memory","agent memory","hierarchical agent memory","directory","safe","github","dashboard","savings","https","system","context","ops","files","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 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Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoho-crm-automation\u002FSKILL.md",[136],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[138,113,139,140,141,142,143,102,118,144,126,145,123,122,117,146,103,147,119,148],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":158,"publishedAt":37},"01M6GDRCKQBAVTGHX5XBH26KPZ","zod-validation-expert","Expert in Zod — TypeScript-first schema validation. 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