hierarchical-agent-memory

name	hierarchical-agent-memory
description	"Use when you want to reduce input token costs across Claude Code sessions"

Hierarchical Agent Memory (HAM)

Scoped 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/ layer stores decisions, patterns, and an inbox for unconfirmed inferences.

When to Use This Skill

• Use when you want to reduce input token costs across Claude Code sessions
• Use when your project has 3+ directories and the agent keeps re-reading the same files
• Use when you want directory-scoped context instead of one monolithic CLAUDE.md
• Use when you want a dashboard to visualize token savings, session history, and context health
• Use when setting up a new project and want structured agent memory from day one

How It Works

Step 1: Setup ("go ham")

Auto-detects your project platform and maturity, then generates the memory structure:

project/
├── CLAUDE.md              # Root context (~200 tokens)
├── .memory/
│   ├── decisions.md       # Architecture Decision Records
│   ├── patterns.md        # Reusable patterns
│   ├── inbox.md           # Inferred items awaiting confirmation
│   └── audit-log.md       # Audit history
└── src/
    ├── api/CLAUDE.md      # Scoped context for api/
    ├── components/CLAUDE.md
    └── lib/CLAUDE.md

Step 2: Context Routing

The root CLAUDE.md includes a routing section that tells the agent exactly which sub-context to load:

## Context Routing

→ api: src/api/CLAUDE.md
→ components: src/components/CLAUDE.md
→ lib: src/lib/CLAUDE.md

The agent reads root, then immediately loads the relevant subdirectory context — no guessing.

Step 3: Dashboard ("ham dashboard")

Launches a web dashboard at localhost:7777 that visualizes:

• Token savings (HAM-on vs HAM-off sessions)
• Daily token and cost trends
• Per-directory session breakdown
• Context file health (missing/stale/inherited CLAUDE.md coverage)
• Routing compliance (how often the agent follows the routing map)
• Carbon/energy estimates

Commands

Trigger	What it does
go ham	Set up HAM — auto-detect platform, generate CLAUDE.md files
ham savings	Show token and cost savings report
ham dashboard	Launch the interactive web dashboard
ham audit	Health check on memory files
ham insights	Generate actionable insights from session data
ham route	Add/update Context Routing section in root CLAUDE.md
ham carbon	Show energy and carbon efficiency data

Examples

Example 1: First-time setup

User: go ham

Agent: HAM setup complete. Created 8 files.
  - CLAUDE.md (root)
  - .memory/decisions.md
  - .memory/patterns.md
  - .memory/inbox.md
  - src/api/CLAUDE.md
  - src/components/CLAUDE.md
  - src/lib/CLAUDE.md
  - src/utils/CLAUDE.md

Baseline captured in .memory/baseline.json
Run "HAM savings" to see your token and cost savings.

Example 2: Checking savings

User: ham savings

Agent:
  Before HAM: ~7,500 tokens/prompt
  After HAM:    ~450 tokens/prompt
  Savings:    7,050 tokens (94%)

  Monthly projection (1,500 prompts):
    Sonnet: ~$31.73 saved
    Opus:   ~$158.63 saved

Best Practices

• Keep root CLAUDE.md under 60 lines / 250 tokens
• Keep subdirectory CLAUDE.md files under 75 lines each
• Run ham audit every 2 weeks to catch stale or missing context files
• Use ham route after adding new directories to keep routing current
• Review .memory/inbox.md periodically — confirm or reject inferred items

Limitations

• Token estimates use ~4 chars = 1 token approximation, not a real tokenizer
• Baseline savings comparisons are estimates based on typical agent behavior
• Dashboard requires Node.js 18+ and reads session data from ~/.claude/projects/
• Context routing detection relies on CLAUDE.md read order in session JSONL files
• Does not auto-update subdirectory CLAUDE.md content — you maintain those manually or via ham audit
• Carbon estimates use regional grid averages, not real-time energy data

Related Skills

• agent-memory-systems — general agent memory architecture patterns
• agent-memory-mcp — MCP-based memory integration

Diff History

• v00.33.0: Ingested from antigravity-awesome-skills community repo

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Why This Skill Exists

Apply —

What If Fails

• condition: Modelo de ML indisponível ou não carregado