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/telagod/code-abyss/engineering-data-pipelinesnpx skills add telagod/code-abyss --skill engineering-data-pipelinesgit clone --depth 1 https://github.com/telagod/code-abyssWhat 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 | $0.00371 |
| Opus 5 | $0.00024 | $0.00186 |
| Sonnet 5 | $0.00010 | $0.00074 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
engineering-data-pipelines 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.
What it actually says
数据工程域 · Data Engineering
判断先于执行：决定「是否做 / 选什么 / 如何取舍」（栈、方案、架构、权衡）前，先读领域判断内核
skills/_kernel/backend/SKILL.md——它管 judgment，本秘典管 execution；冲突时以内核判断为准。
编排：Airflow(调度) | Dagster(资产) | Prefect(现代流)
流处理：Kafka Streams(嵌入式) | Flink(集群) | Spark Streaming
质量：Great Expectations | dbt tests | Soda Core
编排检查项
幂等(UPSERT/分区覆盖) | 增量(WHERE updated_at > last_run) | 事件驱动触发 | 跨 DAG 依赖 | 数据血缘(ref()/Asset deps)
流处理检查项
时间语义选择 | Watermark 乱序容忍 | 状态 TTL 防膨胀 | Checkpoint 间隔 | 端到端 Exactly-Once | 背压监控
质量检查项
分层验证(源→转换→目标) | 完整性+准确性+一致性 | 及时性阈值 | 加权评分 | 告警(Slack/PagerDuty)
工具对比、API 用法、质量维度详见 references/details.md
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.
- 2d ago First seen · 30 lines · 48 tokens per session scan A 04ff02981499
engineering-data-pipelines is a skill published in the GitHub repository telagod/code-abyss (240 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 371 once invoked, about $0.0002 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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