Borrowing it
Nothing to install: this file belongs to timothywarner-org/context-engineering. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/timothywarner-org/context-engineering/main/.claude/skills/warnerco-schematica/SKILL.mdgit clone --depth 1 https://github.com/timothywarner-org/context-engineeringWrote 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/timothywarner-org/context-engineering/warnerco-schematica)<a href="https://agentmods.dev/skills/timothywarner-org/context-engineering/warnerco-schematica"><img src="https://agentmods.dev/badge/skills/timothywarner-org/context-engineering/warnerco-schematica/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/timothywarner-org/context-engineering/warnerco-schematica"><img src="https://agentmods.dev/badge/skills/timothywarner-org/context-engineering/warnerco-schematica.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00083 | $0.01406 |
| Opus 5.5 | $0.00033 | $0.00562 |
| Sonnet 5.5 | $0.00017 | $0.00281 |
| Haiku 4.5 | $0.00008 | $0.00141 |
Grade A, and why
warnerco-schematica 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WARNERCO Robotics Schematica
Agentic robot schematics system with semantic memory and retrieval-augmented generation.
Architecture
┌─────────────────────────────────────────────────────────────┐
│ FastAPI + FastMCP │
├─────────────────────────────────────────────────────────────┤
│ LangGraph Flow (9-node Hybrid RAG) │
│ parse_intent -> query_graph -> inject_scratchpad -> retrieve│
│ -> compress -> reason -> respond │
├─────────────────────────────────────────────────────────────┤
│ Hybrid Memory Layer │
│ +-------------------+ +-------------------+ +-----------+│
│ | Vector Store | | Graph Store | | Scratchpad|│
│ | JSON->Chroma-> | | SQLite + NetworkX | | In-memory |│
│ | Azure AI Search | | (Knowledge Graph) | | (Session) |│
│ +-------------------+ +-------------------+ +-----------+│
└─────────────────────────────────────────────────────────────┘
Project Structure
src/warnerco/backend/
├── app/
│ ├── main.py # FastAPI application
│ ├── config.py # Settings and environment
│ ├── models.py # Pydantic schemas
│ ├── routes.py # API endpoints
│ ├── mcp_tools.py # FastMCP tool definitions
│ ├── adapters/ # Memory backend implementations
│ │ ├── json_store.py
│ │ ├── chroma_store.py
│ │ ├── azure_search_store.py
│ │ ├── graph_store.py
│ │ └── scratchpad_store.py
│ └── langgraph/
│ └── flow.py # 9-node hybrid RAG orchestration
├── data/
│ ├── schematics/ # JSON source of truth
│ └── chroma/ # Vector embeddings
├── static/dash/ # SPA dashboards
└── .env # Configuration
Commands
cd src/warnerco/backend
# Local development
uv sync
uv run uvicorn app.main:app --reload --port 8000
# Index schematics into Chroma
uv run python -c "from app.adapters.chroma_store import ChromaMemoryStore; import asyncio; asyncio.run(ChromaMemoryStore().index_all())"
# MCP stdio server (for Claude Desktop)
uv run warnerco-mcp
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.
- 2mo ago First seen · 157 lines · 83 tokens per session scan A f2d57b9b20ad
warnerco-schematica is a skill published in the GitHub repository timothywarner-org/context-engineering (27 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 1,406 once invoked, about $0.0003 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 2026-08-30.
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