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 instructions/colossus-lab/openarg_backend/claude-mdgit clone --depth 1 https://github.com/colossus-lab/openarg_backendWrote 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/instructions/colossus-lab/openarg_backend/claude-md)<a href="https://agentmods.dev/instructions/colossus-lab/openarg_backend/claude-md"><img src="https://agentmods.dev/badge/instructions/colossus-lab/openarg_backend/claude-md.svg" alt="Measured on agentmods" 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 | $0.03637 | $0.03637 |
| Opus 5 | $0.01818 | $0.01818 |
| Sonnet 5 | $0.00727 | $0.00727 |
| Haiku 4.5 | $0.00364 | $0.00364 |
Grade A, and why
openarg_backend CLAUDE.md 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 4d 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenArg Backend
Backend service for OpenArg — AI-powered analysis of Argentine government open data. Implements a pipeline that scrapes public data portals, generates vector embeddings, caches datasets, and answers natural-language queries using LLMs.
Stack
- Framework: FastAPI 0.115 + Uvicorn (async, UVLoop)
- Database: PostgreSQL 16 + pgvector (HNSW indexing, 1024-dim embeddings)
- ORM: SQLAlchemy 2.0 (async) + Alembic migrations
- DI: Dishka 1.6 (IoC container)
- Workers: Celery 5.4 + Redis 7 (broker + cache + results)
- AI: AWS Bedrock Claude Haiku 4.5 (primary LLM) + Google Gemini 2.5 Flash (fallback)
- Embeddings: AWS Bedrock Cohere Embed Multilingual v3 (1024-dim)
- Pipeline: LangGraph (stateful graph with checkpointing)
- HTTP: HTTPX (async client)
- Auth: PyJWT + bcrypt
- Rate Limiting: SlowAPI
- Config: TOML files + Pydantic settings
- Logging: structlog
Architecture (Hexagonal / Ports & Adapters)
src/app/
├── domain/ # Domain layer
│ ├── entities/ # Dataclass entities
│ │ ├── base.py # BaseEntity (id, created_at, updated_at)
│ │ ├── dataset.py # Dataset, DatasetChunk
│ │ ├── user_query.py # UserQuery, AgentTask
│ │ └── query_dataset_link.py
│ ├── ports/ # Abstract interfaces (ABC)
│ │ ├── source/data_source.py # IDataSource: fetch_catalog, download_dataset
│ │ ├── dataset/dataset_repository.py # IDatasetRepository: save, get_by_id, upsert
│ │ ├── llm/llm_provider.py # ILLMProvider, IEmbeddingProvider
│ │ ├── search/vector_search.py # IVectorSearch: search_datasets, index_dataset
│ │ ├── sandbox/sql_sandbox.py # ISQLSandbox: execute_readonly
│ │ └── cache/cache_port.py # ICacheService: get, set, delete
│ └── exceptions/ # Domain exceptions
│
├── infrastructure/ # Infrastructure layer
│ ├── adapters/
│ │ ├── source/
│ │ │ ├── datos_gob_ar_adapter.py # IDataSource → datos.gob.ar CKAN
│ │ │ └── caba_adapter.py # IDataSource → CABA CKAN
│ │ ├── llm/
│ │ │ ├── bedrock_llm_adapter.py # ILLMProvider → Claude Haiku 4.5 (AWS Bedrock, primary)
│ │ │ ├── bedrock_embedding_adapter.py # IEmbeddingProvider → Cohere Embed Multilingual v3 (1024-dim)
│ │ │ ├── gemini_adapter.py # ILLMProvider → Gemini 2.5 Flash (Google, fallback)
│ │ │ ├── anthropic_adapter.py # ILLMProvider → Claude Sonnet 4 (Anthropic API)
│ │ ├── search/
│ │ │ └── pgvector_search_adapter.py # IVectorSearch → pgvector
│ │ ├── sandbox/
│ │ │ └── pg_sandbox_adapter.py # ISQLSandbox → read-only PG queries
│ │ ├── dataset/
│ │ │ └── dataset_repository_sqla.py # IDatasetRepository → SQLAlchemy
│ │ └── cache/
│ │ └── redis_cache_adapter.py # ICacheService → Redis
│ ├── resilience/ # Fault tolerance
│ │ ├── retry.py # @with_retry decorator (exponential backoff + jitter)
│ │ └── circuit_breaker.py # In-memory circuit breaker (CLOSED→OPEN→HALF_OPEN)
│ ├── monitoring/ # Observability
│ │ ├── health.py # HealthCheckService (postgres, redis, ddjj, sesiones)
│ │ ├── metrics.py # MetricsCollector singleton (requests, connectors, cache, tokens)
│ │ └── middleware.py # MetricsMiddleware (ASGI)
│ ├── persistence_sqla/
│ │ ├── mappings/ # SQLAlchemy table ↔ entity mappings
│ │ ├── alembic/versions/ # Migration files
│ │ └── provider.py # DB session provider
│ └── celery/
│ ├── app.py # Celery app config + task routing
│ └── tasks/
│ ├── scraper_tasks.py # scrape_catalog, index_dataset_embedding
│ ├── collector_tasks.py # collect_dataset (download + cache in PG)
│ ├── embedding_tasks.py # reindex_all_embeddings
│ └── analyst_tasks.py # analyze_query (plan → search → gather → analyze)
│
├── presentation/http/controllers/ # API layer
│ ├── root_router.py # Composes all routers under /api/v1
│ ├── health/health_router.py # GET /health, /health/ready
│ ├── datasets/datasets_router.py # CRUD + scrape trigger
│ ├── query/query_router.py # Query submission + WebSocket stream
│ ├── query/smart_query_v2_router.py # LangGraph pipeline (POST /smart + WS /ws/smart)
│ ├── public_api/ask_router.py # POST /ask (public API with Bearer token auth)
│ ├── developers/developers_router.py # API key CRUD (create/list/revoke keys)
│ ├── skills/skills_router.py # GET /skills (list auto-detected skills)
│ ├── sandbox/sandbox_router.py # SQL sandbox + NL2SQL
│ ├── taxonomy/taxonomy_router.py # Taxonomy management
│ ├── transparency/transparency_router.py # Transparency data
│ ├── admin/tasks_router.py # Admin task management
│ └── monitoring/metrics_router.py # GET /api/v1/metrics
│
└── setup/
├── ioc/provider_registry.py # Dishka providers (all DI wiring)
├── config/
│ ├── settings.py # Pydantic settings classes
│ └── loader.py # TOML config loader
└── run.py # App factory (make_app)
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.
- 4d ago First seen · 269 lines · 3,637 tokens per session scan A d679bebba94f
openarg_backend CLAUDE.md is an instructions file published in the GitHub repository colossus-lab/openarg_backend (144 stars, last pushed yesterday), licensed MIT. It adds 3,637 tokens to every session, about $0.0182 per session on Opus 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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