Babysitter is a workflow engine for AI coding agents that enforces predefined steps, quality checks, human approvals, and decision records. It is used to coordinate complex, repeatable agent workflows across supported coding tools. The catalogue contains skills, agents, instructions, settings, a plugin, and an MCP integration for its workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add a5c-ai/babysitter --skill vector-memorygit clone --depth 1 https://github.com/a5c-ai/babysitterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/a5c-ai/babysitter/vector-memory)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/vector-memory"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/vector-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/a5c-ai/babysitter/vector-memory"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/vector-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.00352 |
| Opus 5.5 | $0.00012 | $0.00141 |
| Sonnet 5.5 | $0.00006 | $0.00070 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
vector-memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
- Building and querying knowledge graphs for project context
- Managing cross-session memory across project/local/user scopes
- Fast similarity search for routing decisions
HNSW Performance
- Search latency: ~61 microseconds
- Query throughput: ~16,400 QPS
- Configurable embedding dimensions (default: 128)
Knowledge Graph
- PageRank: Importance scoring for knowledge nodes
- Community Detection: Cluster related patterns
- LRU Cache: Fast access to frequently used patterns
- SQLite Backing: Persistent cross-session storage
3-Tier Memory
| Scope | Persistence | Content |
|---|---|---|
| Project | Codebase-level | Patterns, architecture decisions, dependencies |
| Local | Session-level | Context, adaptations, temporary patterns |
| User | Cross-project | Preferences, learned behaviors, global patterns |
Agents Used
agents/optimizer/- Memory and cache optimization
Tool Use
Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 45 lines · 31 tokens per session scan A a547a1d3e764
vector-memory is a skill published in the GitHub repository a5c-ai/babysitter (1,863 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 352 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
documentation-search
Search the internal knowledge base for runbooks, architecture documentation, ADRs, best practices, and troubleshooting guides using RAG. Use when looking for internal documentation, deployment procedures, architecture decisions, or operational runbooks.
AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
ai-context-layer
Designs cross-session agent memory: point-in-time facts, consolidation, tenant isolation. Use when auditing user memory or Mem0/Zep. Not for repo context (dev-context-engineering).
memory-optimization
Tune moflo's memory stack for speed, RAM, and index quality. Covers HNSW parameters (M, efConstruction, ef), vector quantization, batch operations, and common bottlenecks. Use when scaling past 100k entries or when search latency regresses.