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 agentmods add skills/vpeetla-ai/react-agent-pattern/loop-engineeringnpx skills add vpeetla-ai/react-agent-pattern --skill loop-engineeringgit clone --depth 1 https://github.com/vpeetla-ai/react-agent-patternWhat 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 | $0.00048 | $0.00307 |
| Opus 5 | $0.00024 | $0.00153 |
| Sonnet 5 | $0.00010 | $0.00061 |
| Haiku 4.5 | $0.00005 | $0.00031 |
Grade A, and why
loop-engineering 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 2d 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.
This is a copy
100% identical to loop-engineering — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Loop Engineering
ODAEU outer loop
| Phase | Action |
|---|---|
| Observe | Load RAG config vN, memory hints, corpus context |
| Decide | Plan ReAct steps or orchestrator decomposition |
| Act | MCP tools (read_file, search_docs) or repo patches |
| Evaluate | Recall, faithfulness, pytest, review score |
| Update | Tune RAG (top_k, hybrid_alpha) + write lesson |
RAG evolve signals
| Failure | Tune |
|---|---|
| low_recall | ↑ top_k, ↑ hybrid_alpha |
| low_faithfulness | ↑ rerank_threshold, ↓ hybrid_alpha |
Memory
Lesson(failure_mode, lesson, rag_version)→ JSON store (v1)- Hints injected on next run via
hints_for_query
MCP bridge
- Local adapters in
mcp/bridge.py; extensible to stdio MCP servers - Never give agents raw shell without allowlist
Reference
loop-engine-agent-platform/src/loop_engine/harness/loops/support-intelligence.yaml- ADR-001 in LoopForge repo
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.
- 2d ago First seen · 43 lines · 48 tokens per session scan A 7f855b529f66
loop-engineering is a skill published in the GitHub repository vpeetla-ai/react-agent-pattern (2 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 307 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to loop-engineering, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
deploy-vercel-render
Deploy vpeetla-ai demos: Vercel static/Next.js frontends, Render FastAPI backends, env vars, free tier gotchas. Use when shipping demos, fixing deploy failures, or adding render.yaml / vercel.json.
langgraph-orchestration
Build or modify LangGraph StateGraph agents in vpeetla-ai repos: typed state, nodes, conditional edges, MemorySaver, interruptbefore HITL. Use when adding orchestrators, coding loops, or multi-agent graphs.
portfolio-adr
Write ADRs, case studies, and portfolio copy for ai-architecture-portfolio and venkat-ai-portfolio. Use when documenting decisions, updating ecosystem pages, or syncing GitHub profile README with live demos.
tdd-agent-loops
Test-driven development for agent systems: red-green-refactor on graphs, mocked LLM fixtures, pytest-asyncio, trace assertions. Use when adding agent nodes, fixing loop bugs, or building pattern repos.
aegis-gateway
Integrate AegisAI gateway before tool side effects (notify, publish, deploy). Use when adding Slack/Telegram/WhatsApp notify, content publish, or any irreversible external action in VAP, AegisLoop, or ai-content-factory.
hitl-side-effects
Add human-in-the-loop gates for irreversible actions: LangGraph interruptbefore, AegisAI approval queue, UI approve/resume endpoints. Use when shipping, publishing, notifying, or merging agent-generated changes.