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 vaquarkhan/data-engineering-agent-skills --skill orchestration-and-backfillsgit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote 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/vaquarkhan/data-engineering-agent-skills/orchestration-and-backfills)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/orchestration-and-backfills"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/orchestration-and-backfills/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/vaquarkhan/data-engineering-agent-skills/orchestration-and-backfills"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/orchestration-and-backfills.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 | $0.00041 | $0.00520 |
| Opus 5 | $0.00020 | $0.00260 |
| Sonnet 5 | $0.00008 | $0.00104 |
| Haiku 4.5 | $0.00004 | $0.00052 |
Grade A, and why
orchestration-and-backfills 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 9d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestration And Backfills
Overview
Reliable data systems are shaped as much by rerun behavior as by transformation logic. This skill ensures schedules, retries, and backfills are deliberate, safe, and reversible.
When to Use
- adding or changing a scheduled pipeline
- designing retries or failure handling
- introducing a new dependency chain
- reprocessing historical data
- modifying publish windows or cutover behavior
Workflow
-
Define execution semantics. Specify:
- schedule or trigger type
- watermark behavior
- late-arriving data policy
- idempotency guarantees
- retry rules
- failure notification path
-
Separate normal runs from backfills. Historical reprocessing should not silently behave like daily incremental runs unless that has been proven safe. For
/backfillor publish-bound replay, loadsafe-backfill-and-replay-orchestrationfirst and completetemplates/backfill-plan.yamlbefore execution. -
Design the recovery path before rollout. Include:
- restart behavior
- partial failure handling
- duplicate prevention
- publish gating
- rollback or pause steps
-
Estimate cost and blast radius. Backfills can overload warehouses, queues, clusters, or downstream consumers.
-
Prove the run strategy. Use a dry run, limited slice, or non-production environment when possible.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "The scheduler will handle retries for us." | Default retries may duplicate writes or hide real data issues. |
| "Backfill is just rerunning the job for older dates." | Historical loads often need different concurrency, checks, and cutover rules. |
| "We can figure out rollback during the incident." | Recovery plans created during outage pressure are usually incomplete. |
Red Flags
- no idempotency strategy is documented
- backfill and incremental logic are conflated
- downstream consumers are not considered during replay
- recovery steps are absent from the plan
What ships with it
3 files 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.
- 9d ago First seen · 70 lines · 41 tokens per session scan A 8e6639343d20
orchestration-and-backfills is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 520 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 2026-09-03.
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