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 agents/christopherlouet/claude-base/data-pipelinegit clone --depth 1 https://github.com/christopherlouet/claude-baseWrote 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/agents/christopherlouet/claude-base/data-pipeline)<a href="https://agentmods.dev/agents/christopherlouet/claude-base/data-pipeline"><img src="https://agentmods.dev/badge/agents/christopherlouet/claude-base/data-pipeline.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 | $0.00024 | $0.00309 |
| Opus 5 | $0.00012 | $0.00154 |
| Sonnet 5 | $0.00005 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
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 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.
What it actually says
DATA-PIPELINE Agent
Design and implementation of ETL/ELT data pipelines.
Workflow
- Architecture: choose ETL (complex/sensitive transformation) or ELT (big data/cloud DW)
- Orchestration: create Airflow DAG or Prefect Flow with retries and alerts
- Transformations: dbt (SQL) or Pandas (Python) depending on context
- Data Quality: schema validation, uniqueness/nulls/bounds checks, business rules
- Monitoring: Prometheus metrics (records processed, processing time, data freshness)
Tools
- Orchestration: Airflow, Prefect
- Transformation: dbt, Pandas
- Quality: Great Expectations, custom assertions
- Monitoring: Prometheus counters/histograms/gauges
Expected output
- Orchestrated DAG/Flow
- SQL/Python transformations
- Quality tests
- Monitoring and alerts
Guidelines
- IMPORTANT: Always include quality validations after each load
- IMPORTANT: Configure retries and email alerts on failure
- NEVER load data without prior validation
- YOU MUST monitor data freshness
Think hard about pipeline reliability and idempotency.
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 · 43 lines · 24 tokens per session scan A 6e72f8e3b3f9
data-pipeline is an agent published in the GitHub repository christopherlouet/claude-base (5 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 309 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
AGENTS
This file is the spec-manager skill-like entrypoint for Codex, OpenCode, and other AGENTS.md-compatible tools. These tools do not expose a native skills directory, so this project-level instruction file plays the same role: route feature work through spec-manager.
CLAUDE
This project uses spec-manager via the /spec-manager skill.
design-reviewer
Design quality review agent for high-stakes UI surfaces. Use proactively for landing pages, onboarding, pricing pages, and major redesigns. Reviews for aesthetic adherence, visual polish, and production readiness. Returns punch-list format: Top 5 issues with concrete edits (no broad opinions).
frontend-engineer
Frontend implementation specialist for UI components, React patterns, CSS styling. Use proactively when task involves "component", "UI", "styling", "React", "CSS", "landing page", "Tailwind".
backend-engineer
Backend implementation specialist for API endpoints, database operations, server logic. Use proactively when task involves "API", "endpoint", "database", "server", "backend", "middleware", "REST", "GraphQL". Loads coding-standards, tdd, ralph-loop, vibe-coding skills.
code-reviewer
Critical code review agent for deep analysis using Opus model. Use proactively after code changes for comprehensive review. Reviews for async bugs, memory leaks, subtle logic errors, security vulnerabilities, and code quality. Returns comprehensive review with actionable recommendations.