system_overview
Show how Continuous Claude works - the opinionated setup with hooks, memory, and coordination.
Best use case
system_overview is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Show how Continuous Claude works - the opinionated setup with hooks, memory, and coordination.
Teams using system_overview should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/system_overview/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How system_overview Compares
| Feature / Agent | system_overview | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Show how Continuous Claude works - the opinionated setup with hooks, memory, and coordination.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# System Overview
Show users how Continuous Claude works - the opinionated setup with hooks, memory, and coordination.
## When to Use
- User asks "how does this work?"
- User asks "what can you remember?"
- User asks "what's different about this setup?"
- User runs `/system_overview`
## Response
```
CONTINUOUS CLAUDE SYSTEM OVERVIEW
=================================
MEMORY LAYER (PostgreSQL + pgvector)
------------------------------------
- 78,000+ temporal facts from past sessions
- Learnings extracted automatically at session end
- Semantic search with embeddings
RECALL: uv run python opc/scripts/recall_temporal_facts.py --query "your topic"
HOOKS (9 event types registered)
--------------------------------
SessionStart → Load continuity ledger, rebuild symbol index
UserPromptSubmit → Skill activation check, context injection
PreToolUse → Smart search routing (Grep → TLDR for code)
PostToolUse → File claims, compiler feedback
PreCompact → Save state before context compaction
Stop → Extract learnings, create handoffs
SubagentStart → Register spawned agents
SubagentStop → Coordination, handoff creation
SessionEnd → Cleanup
CONTINUITY SYSTEM
-----------------
Ledger: thoughts/ledgers/CONTINUITY_CLAUDE-{session}.md
Handoffs: thoughts/shared/handoffs/{session}/*.yaml
Commands:
/resume_handoff <path> - Continue from handoff
/create_handoff - Create snapshot for transfer
TLDR CODE INTELLIGENCE
----------------------
5-layer analysis: AST → Call Graph → CFG → DFG → PDG
95% token savings vs reading raw files
Auto-intercepts Grep for .py/.ts/.go/.rs files
Pre-built index: /tmp/claude-symbol-index/symbols.json
SETUP
-----
Run: uv run python opc/scripts/setup/wizard.py
Options:
[1] SQLite only (simple, offline)
[2] PostgreSQL + pgvector (semantic search)
```
## Key Files
| Component | Location |
|-----------|----------|
| Hook registration | `.claude/settings.json` |
| Hook implementations | `.claude/hooks/src/*.ts` |
| Rules (auto-injected) | `.claude/rules/*.md` |
| Skills | `.claude/skills/*/SKILL.md` |
| Setup wizard | `opc/scripts/setup/wizard.py` |
| Recall script | `opc/scripts/recall_temporal_facts.py` |
| Store learning | `opc/scripts/core/store_learning.py` |
| Symbol index builder | `opc/scripts/build_symbol_index.py` |
## Environment Variables
| Variable | Purpose |
|----------|---------|
| `CONTINUOUS_CLAUDE_DB_URL` | PostgreSQL connection |
| `VOYAGE_API_KEY` | Embeddings (optional) |
| `BRAINTRUST_API_KEY` | Tracing (optional) |
| `CLAUDE_PROJECT_DIR` | Auto-set by Claude Code |Related Skills
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