OpenFang is an open-source operating system for autonomous AI agents, built in Rust to run agents that perform scheduled work such as research, monitoring, lead generation, and reporting. It is for people who want agents to operate continuously rather than only respond to prompts. The catalogue add-ons extend workflows around the OpenFang agent system.
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 RightNow-AI/openfang --skill data-pipelinegit clone --depth 1 https://github.com/RightNow-AI/openfangWrote 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/rightnow-ai/openfang/data-pipeline)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/data-pipeline"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/data-pipeline/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/rightnow-ai/openfang/data-pipeline"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/data-pipeline.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 | $0.00023 | $0.00644 |
| Opus 5.5 | $0.00009 | $0.00258 |
| Sonnet 5.5 | $0.00005 | $0.00129 |
| Haiku 4.5 | $0.00002 | $0.00064 |
Grade A, and why
data-pipeline 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- data-pipeline — 100% identical, 0 lines differ
- data-pipeline — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Expert
A data engineering specialist with extensive experience designing and operating production ETL/ELT pipelines, orchestration frameworks, and data quality systems. This skill provides guidance for building reliable, observable, and scalable data pipelines using industry-standard tools like Apache Airflow, Spark, and dbt across batch and streaming architectures.
Key Principles
- Prefer ELT over ETL when your target warehouse can handle transformations; load raw data first, then transform in place for reproducibility and auditability
- Design every pipeline step to be idempotent; re-running a task with the same inputs must produce the same outputs without side effects or duplicates
- Partition data by time or logical keys at every stage; partitioning enables incremental processing, efficient pruning, and manageable backfill operations
- Instrument pipelines with data quality checks between stages; catching bad data early prevents cascading corruption through downstream tables
- Separate orchestration (when and what order) from computation (how); the scheduler should not perform heavy data processing itself
Techniques
- Build Airflow DAGs with task-level retries, timeouts, and SLAs; use sensors for external dependencies and XCom for lightweight inter-task communication
- Design Spark jobs with proper partitioning (repartition/coalesce), broadcast joins for small dimension tables, and caching for reused DataFrames
- Structure dbt projects with staging models (source cleaning), intermediate models (business logic), and mart models (final consumption tables)
- Write dbt tests at multiple levels: schema tests (not_null, unique, accepted_values), relationship tests, and custom data tests for business rules
- Implement data quality gates using frameworks like Great Expectations: define expectations on row counts, column distributions, and referential integrity
- Use Change Data Capture (CDC) patterns with tools like Debezium to stream database changes into event pipelines without polling
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 · 39 lines · 23 tokens per session scan A 6aee870ca165
data-pipeline is a skill published in the GitHub repository RightNow-AI/openfang (18,236 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 644 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 2026-08-30.
Other skills, from other repositories
github
Manage GitHub issues and pull requests, inspect CI runs, and query repository data.
channels-setup
Use when a developer wants to build their first CopilotKit Channels agent and get it answering in Slack or Microsoft Teams: "set up a channel", "connect my agent to Slack", "get my agent into Teams", or starting from nothing and wanting a working channel end to end. Covers the whole path: inspecting or scaffolding the…
intelligence-vocabulary
Locks customer-facing CopilotKit Intelligence docs to one approved name per concept. Use when writing, editing, or reviewing Intelligence docs, nav labels, signup labels, or image alt text in showcase/shell-docs. Don't use for renaming code identifiers, route slugs, env vars, API fields, or component names, or for…
horizon
Install, configure, run, customize, schedule, and extend Horizon, the profile-driven news radar. Use for first-time Horizon installation, source selection and onboarding, generating or reading daily digests, tuning processing profiles and digest limits, setting up delivery and scheduled runs, diagnosing pipeline…
migrating-vercel-ai-sdk-and-eve-to-pydantic-ai
Migrate TypeScript Vercel AI SDK or Eve applications to Python with Pydantic AI and, only when needed, Pydantic AI Harness. Use when source code imports ai, @ai-sdk/, or eve, or relies on generateText, streamText, ToolLoopAgent, useChat, Eve agents, tools, skills, subagents, approvals, sessions, channels, schedules…
migrating-openai-agents-sdk-to-pydantic-ai
Migrate Python OpenAI Agents SDK applications to Pydantic AI and, when warranted, Pydantic AI Harness. Use for agents.Agent, Runner, function tools, handoffs, guardrails, sessions, human approval, streaming, or SandboxAgent. Do not use for applications built directly on the OpenAI Responses API without the Agents SDK…