hierarchical-agent-memory
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.
What this skill does
# 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:
```markdown
## 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: ~$158.63 saved
```
## Best Practices
- Keep root CLAUDE.md under 60 lines / 250 tokens
- Keep subdirectory CLAUDE.md files under 75 lines each
- Run `ham audit` every 2 weeks to catch stale or missing context files
- Use `ham route` after adding new directories to keep routing current
- Review `.memory/inbox.md` periodically — confirm or reject inferred items
## Limitations
- Token estimates use ~4 chars = 1 token approximation, not a real tokenizer
- Baseline savings comparisons are estimates based on typical agent behavior
- Dashboard requires Node.js 18+ and reads session data from `~/.claude/projects/`
- Context routing detection relies on CLAUDE.md read order in session JSONL files
- Does not auto-update subdirectory CLAUDE.md content — you maintain those manually or via `ham audit`
- Carbon estimates use regional grid averages, not real-time energy data
## Related Skills
- `agent-memory-systems` — general agent memory architecture patterns
- `agent-memory-mcp` — MCP-based memory integration
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.